WO2020014715A2 - Appareil et procédé pour distinguer de larges battements cardiaques complexes - Google Patents

Appareil et procédé pour distinguer de larges battements cardiaques complexes Download PDF

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WO2020014715A2
WO2020014715A2 PCT/US2019/046713 US2019046713W WO2020014715A2 WO 2020014715 A2 WO2020014715 A2 WO 2020014715A2 US 2019046713 W US2019046713 W US 2019046713W WO 2020014715 A2 WO2020014715 A2 WO 2020014715A2
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Prior art keywords
baseline
data
heart beat
time
wide complex
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PCT/US2019/046713
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WO2020014715A3 (fr
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Adam M. MAY
Christopher V. DESIMONE
Abhishek J. DESHMUKH
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Mayo Foundation For Medical Education And Research
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Priority claimed from US16/445,036 external-priority patent/US11154233B2/en
Application filed by Mayo Foundation For Medical Education And Research filed Critical Mayo Foundation For Medical Education And Research
Priority to US17/253,262 priority Critical patent/US20210275081A1/en
Publication of WO2020014715A2 publication Critical patent/WO2020014715A2/fr
Publication of WO2020014715A3 publication Critical patent/WO2020014715A3/fr

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Definitions

  • WCT wide complex tachycardia
  • WCTs wide complex tachycardias
  • VT ventricular tachycardia
  • SWCT supraventricular wide complex tachycardia
  • ECG 12-lead electrocardiogram
  • the present invention describes an ability to accurately distinguish VT and SWCT without the need for manual ECG, electrogram (EMG) and/or vectorcardiogram (VCG) interpretation or calculation.
  • ECG electrogram
  • VCG vectorcardiogram
  • the present invention provides three types of embodiments for wide complex beat differentiation that can be automatically implemented using data provided by ECG, EMG and/or VCG interpretation software.
  • the first type is based in whole or in part on a WCT Formula.
  • the second type is based in whole or in part on a VT prediction model.
  • the third type is based in whole or in part on an analysis of ventricular repolarization (e.g., T wave).
  • One embodiment of the present invention provides a computerized method of classifying a wide complex heart beat(s) comprising: providing a computing device having an input/output interface, one or more processors and a memory; receiving one or more wide complex heart beat waveform amplitudes and/or time-voltage areas, and one or more baseline heart beat waveform amplitudes and/or time-voltage areas via the input/output interface or the memory; determining a signal change between the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas using the one or more processors; and providing the signal change via the input/output interface, wherein the signal change provides an indication whether the wide complex heart beat(s) is from a ventricular source or a supraventricular aberrant condition.
  • the signal change further provides the indication whether the wide complex heart beat(s) is due to ventricular pacing.
  • the wide complex heart beat(s) comprise a wide complex tachycardia (WCT)
  • the ventricular source comprises a ventricular tachycardia (VT)
  • the supraventricular aberrant condition comprises a supraventricular wide complex tachycardia (SWCT).
  • providing the signal change via the input/output interface comprises: automatically determining a wide complex heart beat classification for the wide complex heart beat(s) by comparing the signal change to a predetermined value using the one or more processors, wherein the wide complex heart beat classification comprises a ventricular source or a supraventricular aberrant condition; and providing the wide complex heart beat classification via the input/output interface.
  • the signal change comprises a VT probability
  • the wide complex heart beat classification comprises a VT whenever the VT probability is greater than or equal to the predetermined value
  • the wide complex heart beat classification comprises a SWCT whenever the VT probability is less than the predetermined value.
  • the method further comprises selecting the predetermined value from a range of 0% to 100%.
  • the predetermined value comprises about 1%, 10%, 25%, 50%, 75%, 90% or 99%.
  • providing the signal change comprises providing a“shock” signal, a “no shock” signal, or no signal.
  • the method further comprises obtaining the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas from an electrocardiogram (ECG) QRS signal, a ventricular electrogram (EMG) signal, and/or a vectorcardiogram (VCG) signal.
  • ECG electrocardiogram
  • EMG ventricular electrogram
  • VCG vectorcardiogram
  • the wide complex heart beat waveform amplitudes and/or time- voltage areas comprise a plurality of measured amplitudes and/or time-voltage areas of a ECG QRS waveform, a EMG waveform and/or a VCG waveform above and below an isoelectric baseline; and the baseline heart beat waveform amplitudes and/or time-voltage areas comprise a plurality of measured amplitudes and/or time-voltage areas of a baseline ECG QRS waveform, a baseline EMG waveform and/or a baseline VCG waveform above and below the isoelectric baseline.
  • receiving the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas, and one or more baseline heart beat waveform amplitudes and/or time-voltage areas comprises: receiving a ECG QRS data, a EMG data, a VCG data and/or a mathematically synthesized VCG data via the input/output interface or the memory; receiving a baseline ECG QRS data, a baseline EMG data and/or a baseline VCG data via the input/output interface or the memory; determining the one or more waveform amplitudes and/or time-voltage areas from the ECG QRS data, the EMG data and/or the VCG data using the one or more processors; and determining the one or more baseline waveform amplitudes and/or time-voltage areas from the baseline ECG QRS data, the baseline EMG data and/or the baseline VCG data using the one or more processors.
  • the ECG QRS data, the EMG data and/or the VCG data is generated or recorded before or after the baseline ECG QRS data, the baseline EMG data and/or the baseline VCG data.
  • the ECG QRS data, the EMG data and/or the VCG data is generated or recorded after the baseline ECG QRS data, the baseline EMG data and/or the baseline VCG data and determining the signal change.
  • the method further comprises generating or recording the ECG QRS data, the EMG data and/or the VCG data and the baseline ECG QRS data, the baseline EMG data and/or the baseline VCG data using one or more sensors or devices.
  • the one or more sensors or devices comprise a 12-lead ECG device, a continuous ECG telemetry monitor, a stress testing system, an extended monitoring device, a smartphone-enabled ECG medical device, an external cardioverter-defibrillator therapy device, a subcutaneous implantable cardioverter defibrillators (ICD), a pacemaker, an automated external defibrillators (AED), or an automatic implantable cardioverter defibrillator (AICD).
  • the computing device is integrated into the one or more sensors or devices; or the one or more sensors or devices are integrated into the computing device.
  • determining the signal change between the wide complex heart beat waveform amplitudes and/or time- voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas comprises: receiving a wide complex heart beat waveform duration via the input/output interface or the memory; determining, using the one or more processors, a percent amplitude change (PAC) based on the wide complex heart beat waveform amplitudes and the baseline wide complex heart beat waveform amplitudes, and/or a percent time-voltage area change (PTVAC) based on the wide complex heart beat waveform time-voltage areas and the baseline wide complex heart beat waveform time-voltage areas; determining a classification probability based on the wide complex heart beat waveform duration, and the PAC and/or the PTVAC using the one or more processors; and wherein the signal change comprises the classification probability, and the classification probability comprises a VT probability, a SWCT probability, or a ventricular pacing probability.
  • PAC percent amplitude change
  • PTVAC percent time-
  • determining the classification probability is further determined based one or more additional classification predictors.
  • the PAC comprises a frontal PAC and a horizontal PAC
  • the PTVAC comprises a frontal PTVAC and a horizontal PTVAC.
  • determining the signal change between the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas comprises: receiving a WCT QRS duration via the input/output interface or the memory; the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas comprise one or more frontal plane WCT positive waveform amplitudes and/or time-voltage areas, one or more horizontal plane WCT positive waveform amplitudes and/or time-voltage areas, one or more frontal plane WCT negative waveform amplitudes and/or time-voltage areas, and one or more horizontal plane WCT negative waveform amplitudes and/or time-voltage areas; the one or more the baseline heart beat waveform amplitudes and/or time-voltage areas comprise one or more frontal plane baseline positive waveform amplitudes and/or time-voltage areas, one or more horizontal plane baseline positive waveform amplitudes and/or time-voltage areas
  • the statistical or machine learning process comprises a linear regression algorithm, a logistic regression model, a linear discriminate analysis algorithm, a Naive Bayes algorithm, a computational model using artificial neural networks, a computational model based on classification or regression trees, a k-nearest neighbors based model, a support vector machine based model, a boosting algorithm, or an ensemble machine learning algorithm.
  • the frontal PAC is determined by ⁇ % ⁇ ⁇
  • the input/output interface comprises a remote device, and the remote device is communicably coupled to the one or more processors via one or more networks.
  • the method further comprises providing a recommendation to select or exclude a therapy, medication, diagnostic testing or referral for a patient based on the signal change.
  • the computing device comprises a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, or a medical device.
  • the method can be implemented using a non- transitory computer readable medium that when executed causes the one or more processors to perform the method.
  • Another embodiment of the present invention provides an apparatus for classifying a wide complex heart beat(s) comprising an input/output interface, a memory, and one or more processors communicably coupled to the input/output interface and the memory.
  • the one or more processors receive one or more wide complex heart beat waveform amplitudes and/or time-voltage areas, and one or more baseline heart beat waveform amplitudes and/or time- voltage areas via the input/output interface or the memory, determine a signal change between the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas using the one or more processors, and provide the signal change via the input/output interface, wherein the signal change provides an indication whether the wide complex heart beat(s) is from a ventricular source or a supraventricular aberrant condition.
  • the signal change further provides the indication whether the wide complex heart beat(s) is due to ventricular pacing.
  • the wide complex heart beat(s) comprise a wide complex tachycardia (WCT)
  • the ventricular source comprises a ventricular tachycardia (VT)
  • the supraventricular aberrant condition comprises a supraventricular wide complex tachycardia (SWCT).
  • the one or more processors provide the signal change via the input/output interface by: automatically determining a wide complex heart beat classification for the wide complex heart beat(s) by comparing the signal change to a predetermined value, wherein the wide complex heart beat classification comprises the ventricular source or the supraventricular aberrant condition; and providing the wide complex heart beat classification via the input/output interface.
  • the signal change comprises a VT probability
  • the wide complex heart beat classification comprises a VT whenever the VT probability is greater than or equal to the predetermined value
  • the wide complex heart beat classification comprises a SWCT whenever the VT probability is less than the predetermined value.
  • the one or more processors select the predetermined value from a range of 0% to 100%.
  • the predetermined value comprises about 1%, 10%, 25%, 50%, 75%, 90% or 99%.
  • the one or more processors provide the signal change by providing a “shock” signal, a“no shock” signal, or no signal.
  • the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas are obtained from an electrocardiogram (ECG) QRS signal, a ventricular electrogram (EMG) signal, and/or a vectorcardiogram (VCG) signal.
  • ECG electrocardiogram
  • EMG ventricular electrogram
  • VCG vectorcardiogram
  • the wide complex heart beat waveform amplitudes and/or time-voltage areas comprise a plurality of measured amplitudes and/or time-voltage areas of a ECG QRS waveform, a EMG waveform and/or a VCG waveform above and below an isoelectric baseline; and the baseline heart beat waveform amplitudes and/or time-voltage areas comprise a plurality of measured amplitudes and/or time-voltage areas of a baseline ECG QRS waveform, a baseline EMG waveform and/or a baseline VCG waveform above and below the isoelectric baseline.
  • the one or more processors receive the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas, and one or more baseline heart beat waveform amplitudes and/or time-voltage areas by: receiving a ECG QRS data, a EMG data, a VCG data and/or a mathematically synthesized VCG data via the input/output interface or the memory; receiving a baseline ECG QRS data, a baseline EMG data and/or a baseline VCG data via the input/output interface or the memory; determining the one or more waveform amplitudes and/or time-voltage areas from the ECG QRS data, the EMG data and/or the VCG data; and determining the one or more baseline waveform amplitudes and/or time-voltage areas from the baseline ECG QRS data, the baseline EMG data and/or the baseline VCG data.
  • the ECG QRS data, the EMG data and/or the VCG data is generated or recorded before or after the baseline ECG QRS data, the baseline EMG data and/or the baseline VCG data.
  • the ECG QRS data, the EMG data and/or the VCG data is generated or recorded after the baseline ECG QRS data, the baseline EMG data and/or the baseline VCG data and determining the signal change.
  • the ECG QRS data, the EMG data and/or the VCG data and the baseline ECG QRS data, the baseline EMG data and/or the baseline VCG data are generated or recorded using one or more sensors or devices.
  • the one or more sensors or devices comprise a 12-lead ECG device, a continuous ECG telemetry monitor, a stress testing system, an extended monitoring device, a smartphone-enabled ECG medical device, a cardioverter-defibrillator therapy device, a subcutaneous implantable cardioverter defibrillators (ICD), a pacemaker, an automated external defibrillators (AED), or an automatic implantable cardioverter defibrillator (AICD).
  • the input/output interface, the memory and the one or more processors are integrated into the one or more sensors or devices; or the one or more sensors or devices are integrated into a computing device comprising the input/output interface, the memory and the one or more processors.
  • the one or more processors determine the signal change between the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas by: receiving a wide complex heart beat waveform duration via the input/output interface or the memory; determining a percent amplitude change (PAC) based on the wide complex heart beat waveform amplitudes and the baseline wide complex heart beat waveform amplitudes, and/or a percent time-voltage area change (PTVAC) based on the wide complex heart beat waveform time-voltage areas and the baseline wide complex heart beat waveform time-voltage areas; determining a classification probability based on the wide complex heart beat waveform duration, the PAC and/or the PTVAC; and wherein the signal change comprises the classification probability, and the classification probability comprises a VT probability, a SWCT probability, or a ventricular pacing probability.
  • PAC percent amplitude change
  • PTVAC percent time-voltage area change
  • determining the classification probability is further determined based one or more additional classification predictors.
  • the PAC comprises a frontal PAC and a horizontal PAC
  • the PTVAC comprises a frontal PTVAC and a horizontal PTVAC.
  • the one or more processors determine the signal change between the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas by: receiving a WCT QRS duration via the input/output interface or the memory; the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas comprise one or more frontal plane WCT positive waveform amplitudes and/or time-voltage areas, one or more horizontal plane WCT positive waveform amplitudes and/or time-voltage areas, one or more frontal plane WCT negative waveform amplitudes and/or time-voltage areas, and one or more horizontal plane WCT negative waveform amplitudes and/or time-voltage areas; the one or more the baseline heart beat waveform amplitudes and/or time-voltage areas comprise one or more frontal plane baseline positive waveform amplitudes and/or time-voltage areas, one or more horizontal plane baseline positive waveform amplitudes and/or time-voltage
  • the statistical or machine learning process comprises a linear regression algorithm, a logistic regression model, a linear discriminate analysis algorithm, a Naive Bayes algorithm, a computational model using artificial neural networks, a computational model based on classification or regression trees, a k-nearest neighbors based model, a support vector machine based model, a boosting algorithm, or an ensemble machine learning algorithm.
  • the frontal PAC is determined by
  • the input/output interface comprises a remote device, and the remote device is communicably coupled to the one or more processors via one or more networks.
  • the one or more processors provide a recommendation to select or exclude a therapy, medication, diagnostic testing or referral for a patient based on the signal change.
  • apparatus comprises a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, or a medical device.
  • One embodiment of the present invention provides a computerized method of classifying a wide complex heart beat(s) comprising: providing a computing device having an input/output interface, one or more processors and a memory; receiving a wide complex heart beat data comprising at least a wide complex heart beat QRS duration, a wide complex heart beat R wave axis and a wide complex heart beat T wave axis via the input/output interface or the memory; receiving a baseline heart beat data comprising at least a baseline heart beat QRS duration, a baseline heart beat R wave axis, and a baseline heart beat T wave axis via the input/output interface or the memory; determining a signal change between the wide complex heart beat data and the baseline heart beat data areas using the one or more processors; and providing the signal change via the input/output interface, wherein the signal change provides an indication whether the wide complex heart beat(s) is from a ventricular source or a supraventricular source.
  • the signal change further provides the indication whether the wide complex heart beat(s) is due to ventricular pacing or a premature ventricular contraction (PVC).
  • the wide complex heart beat(s) comprise a wide complex tachycardia (WCT); the ventricular source comprises a ventricular tachycardia (VT); and the supraventricular source comprises a supraventricular wide complex tachycardia (SWCT).
  • providing the signal change via the input/output interface comprises: automatically determining a wide complex heart beat classification for the wide complex heart beat(s) by comparing the signal change to a predetermined value using the one or more processors, wherein the wide complex heart beat classification comprises the ventricular source or the supraventricular source; and providing the wide complex heart beat classification via the input/output interface.
  • the signal change comprises a VT probability; the wide complex heart beat classification comprises a VT whenever the VT probability is greater than or equal to the predetermined value; and the wide complex heart beat classification comprises a SWCT whenever the VT probability is less than the predetermined value.
  • the method further comprises selecting the predetermined value from a range of 0% to 100%.
  • the predetermined value comprises about 1%, 10%, 25%, 50%, 75%, 90% or 99%.
  • providing the signal change comprises providing a“shock” recommendation signal, a“no shock” recommendation signal, or no signal.
  • the method further comprises obtaining the wide complex heart beat data and the baseline heart beat waveform data from an electrocardiogram (ECG) QRS signal, a ventricular electrogram (EMG) signal, vectorcardiogram (VCG) signal and/or a mathematically-synthesized VCG signal.
  • ECG electrocardiogram
  • EMG ventricular electrogram
  • VCG vectorcardiogram
  • receiving the wide complex heart beat data comprises receiving a ECG QRS data, a EMG data, a VCG data and/or a mathematically-synthesized VCG data via the input/output interface or the memory, and determining the wide complex heart beat data from the ECG QRS data, the EMG data, the VCG data and/or the mathematically- synthesized VCG data using the one or more processors; and receiving the baseline heart beat data comprises receiving a baseline ECG QRS data, a baseline EMG data, a baseline VCG data, and/or a baseline mathematically-synthesized VCG data via the input/output interface or the memory, and determining the baseline heart beat data from the ECG QRS data, the EMG data, the VCG data, and/or the mathematically-synthesized VCG data using the one or more processors.
  • the ECG QRS data, the EMG data, the VCG data and/or the mathematically-synthesized VCG data is generated or recorded before or after the baseline ECG QRS data, the baseline EMG data, the baseline VCG data and/or the mathematically-synthesized VCG data.
  • the ECG QRS data, the EMG data, the VCG data, and/or mathematically-synthesized VCG data is generated or recorded after the baseline ECG QRS data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically-synthesized VCG data and determining the signal change.
  • the method further comprises generating or recording the ECG QRS data, the EMG data, the VCG data, and the mathematically-synthesized VCG data as well as the baseline ECG QRS data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically-synthesized VCG data using one or more sensors or devices.
  • the one or more sensors or devices comprise two or more leads of a ECG device, a 12-lead ECG device, a continuous ECG telemetry monitor, a stress testing system, an extended monitoring device, a smartphone-enabled ECG medical device, a cardioverter- defibrillator therapy device, a subcutaneous implantable cardioverter defibrillators (ICD), an intracardiac or transvenous pacemaker device, an automated external defibrillators (AED), or an automatic implantable cardioverter defibrillator (AICD).
  • the computing device is integrated into the one or more sensors or devices; or the one or more sensors or devices are integrated into the computing device.
  • the signal change comprises a classification probability comprising a VT probability, a SWCT probability, a premature ventricular contraction probability, or a ventricular pacing probability. In another aspect determining the classification probability is further determined based one or more additional classification predictors. In another aspect, determining the signal change comprises determining a VT probability using a statistical or machine learning process. In another aspect, the statistical or machine learning process comprises a linear regression algorithm, a logistic regression model, a linear discriminate analysis algorithm, a Naive Bayes algorithm, a computational model using artificial neural networks, a computational model based on classification or regression trees, a k-nearest neighbors based model, a support vector machine based model, a boosting algorithm, or an ensemble machine learning algorithm. In another aspect, the signal change comprises determining a VT probability (P VT ) by:
  • the QRS axis change an absolute or non-absolute value of the wide complex heart beat R wave axis minus the baseline heart beat R wave axis
  • the T axis change an absolute or non-absolute value of the wide complex heart beat T wave axis minus the baseline heart beat T wave axis
  • the WCT QRS duration the wide complex heart beat QRS duration
  • the QRS duration change an absolute or non-absolute value of the wide complex heart beat QRS duration minus the baseline heart beat QRS duration.
  • the input/output interface comprises a remote device, and the remote device is communicably coupled to the one or more processors via one or more networks.
  • receiving the wide complex heart beat data comprises monitoring a person using one or more sensors or devices communicably coupled to the input/output interface.
  • the method further comprises providing a recommendation to select or exclude a therapy, medication, diagnostic testing or referral for a patient based on the signal change.
  • the method further comprises sending an alert to one or more devices in based on the signal change.
  • the method further comprises counting the frequency of various wide complex beats comprising ventricular tachycardia events, supraventricular tachycardia events, singular supraventricular wide complex beats, premature ventricular contractions, right ventricular pacing events and/or biventricular pacing events.
  • the method further comprises: receiving multiple sets of the wide complex heart beat data and the baseline heart beat data for a person or group of persons; determining the signal change for each set of the wide complex heart beat data and the baseline heart beat data for the person or the group of persons; and creating a VT prediction model for the person or the group of persons using the signal changes.
  • the computing device comprises a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, or a medical device.
  • a server computer a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, or a medical device.
  • Another embodiment of the present invention provides an apparatus for classifying a wide complex heart beat(s) comprising an input/output interface, a memory, and one or more processors communicably coupled to the input/output interface and the memory.
  • the one or more processors receive a wide complex heart beat data comprising at least a wide complex heart beat QRS duration, a wide complex heart beat R wave axis and a wide complex heart beat T wave axis via the input/output interface or the memory; receive a baseline heart beat data comprising at least a baseline heart beat QRS duration, a baseline heart beat R wave axis, and a baseline heart beat T wave axis via the input/output interface or the memory; determine a signal change between the wide complex heart beat data and the baseline heart beat data areas; and provide the signal change via the input/output interface, wherein the signal change provides an indication whether the wide complex heart beat(s) is from a ventricular source or a supraventricular source.
  • the signal change further provides the indication whether the wide complex heart beat(s) is due to ventricular pacing or a premature ventricular contraction (PVC).
  • the wide complex heart beat(s) comprise a wide complex tachycardia (WCT); the ventricular source comprises a ventricular tachycardia (VT); and the supraventricular source comprises a supraventricular wide complex tachycardia (SWCT).
  • the one or more processors provide the signal change via the input/output interface by: automatically determining a wide complex heart beat classification for the wide complex heart beat(s) by comparing the signal change to a predetermined value wherein the wide complex heart beat classification comprises the ventricular source or the supraventricular source; and providing the wide complex heart beat classification via the input/output interface.
  • the signal change comprises a VT probability; the wide complex heart beat classification comprises a VT whenever the VT probability is greater than or equal to the predetermined value; and the wide complex heart beat classification comprises a SWCT whenever the VT probability is less than the predetermined value.
  • the one or more processors select the predetermined value from a range of 0% to 100%.
  • the predetermined value comprises about 1%, 10%, 25%, 50%, 75%, 90% or 99%.
  • the one or more processors provide the signal change by providing a“shock” recommendation signal, a“no shock” recommendation signal, or no signal.
  • the one or more processors obtain the wide complex heart beat data and the baseline heart beat waveform data from an electrocardiogram (ECG) QRS signal, a ventricular electrogram (EMG) signal, vectorcardiogram (VCG) signal and/or mathematically-synthesized (VCG) signal.
  • ECG electrocardiogram
  • EMG ventricular electrogram
  • VCG vectorcardiogram
  • VCG mathematically-synthesized
  • the one or more processors receive the wide complex heart beat data by receiving a ECG QRS data, a EMG data, a VCG data and/or a mathematically-synthesized VCG data via the input/output interface or the memory, and determine the wide complex heart beat data from the ECG QRS data, the EMG data, the VCG data and/or the mathematically-synthesized VCG data; and receive the baseline heart beat data by receiving a baseline ECG QRS data, a baseline EMG data, a baseline VCG data, and/or a mathematically-synthesized VCG data via the input/output interface or the memory, and determine the baseline heart beat data from the ECG QRS data, the EMG data, the VCG data and/or the mathematically-synthesized VCG data.
  • the ECG QRS data, the EMG data, the VCG data and/or the mathematically- synthesized VCG data is generated or recorded before or after the baseline ECG QRS data, the baseline EMG data, the baseline VCG data and/or the mathematically-synthesized VCG data.
  • the ECG QRS data, the EMG data, the VCG data, and/or the mathematically-synthesized VCG data is generated or recorded after the baseline ECG QRS data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically- synthesized VCG data, and determining the signal change.
  • the one or more processors generate or record the ECG QRS data, the EMG data, the VCG data, and/or the mathematically-synthesized VCG data and the baseline ECG QRS data, the baseline EMG data and/or the baseline VCG data using one or more sensors or devices.
  • the one or more sensors or devices comprise two or more leads of a ECG device, a 12-lead ECG device, a continuous ECG telemetry monitor, a stress testing system, an extended monitoring device, a smartphone-enabled ECG medical device, a cardioverter-defibrillator therapy device, a subcutaneous implantable cardioverter defibrillators (ICD), an intracardiac or transvenous pacemaker device, an automated external defibrillators (AED), or an automatic implantable cardioverter defibrillator (AICD).
  • the computing device is integrated into the one or more sensors or devices; or the one or more sensors or devices are integrated into the computing device.
  • the signal change comprises a classification probability comprising a VT probability, a SWCT probability, a premature contraction probability, or a ventricular pacing probability.
  • the one or more processors determine the classification probability based one or more additional classification predictors.
  • the one or more processors determine the signal change by determining a VT probability using a statistical or machine learning process.
  • the statistical or machine learning process comprises a linear regression algorithm, a logistic regression model, a linear discriminate analysis algorithm, a Naive Bayes algorithm, a computational model using artificial neural networks, a computational model based on classification or regression trees, a k-nearest neighbors based model, a support vector machine based model, a boosting algorithm, or an ensemble machine learning algorithm.
  • the one or more processors determine the signal change by determining a VT probability (P VT ) by:
  • the QRS axis change an absolute or non-absolute value of the wide complex heart beat R wave axis minus the baseline heart beat R wave axis
  • the T axis change an absolute or non-absolute value of the wide complex heart beat T wave axis minus the baseline heart beat T wave axis
  • the WCT QRS duration the wide complex heart beat QRS duration
  • the QRS duration change an absolute or non-absolute value of the wide complex heart beat QRS duration minus the baseline heart beat QRS duration.
  • the input/output interface comprises a remote device, and the remote device is communicably coupled to the one or more processors via one or more networks.
  • the one or more processors receive the wide complex heart beat data by monitoring a person using one or more sensors or devices communicably coupled to the input/output interface.
  • the one or more processors provide a recommendation to select or exclude a therapy, medication, diagnostic testing or referral for a patient based on the signal change.
  • the one or more processors send an alert to one or more devices in based on the signal change.
  • the one or more processors count the frequency of various wide complex beats comprising ventricular tachycardia events, supraventricular tachycardia events, singular supraventricular wide complex beats, premature ventricular contraction, right ventricular pacing and/or biventricular pacing.
  • the one or more processors receive multiple sets of the wide complex heart beat data and the baseline heart beat data for a person or group of persons, determine the signal change for each set of the wide complex heart beat data and the baseline heart beat data for the person or the group of persons, and create a VT prediction model for the person or the group of persons using the signal changes.
  • the computing device comprises a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, or a medical device.
  • One embodiment of the present invention provides a computerized method of classifying a wide complex heart beat(s) comprising: providing a computing device having an input/output interface, one or more processors and a memory; receiving one or more wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization, and one or more baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization via the input/output interface or the memory; determining a signal change in ventricular repolarization between the wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization and the baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization using the one or more processors; and providing the signal change in ventricular repolarization via the input/output interface, wherein the signal change in ventricular repolarization provides an indication whether the wide complex heart beat(s) is from a
  • the signal changes in the ventricular repolarization further provides the indication whether the wide complex heart beat(s) is due to ventricular pacing or a premature ventricular contraction (PVC).
  • the wide complex heart beat(s) comprise a wide complex tachycardia (WCT)
  • the ventricular source comprises a ventricular tachycardia (VT)
  • the supraventricular source comprises a supraventricular wide complex tachycardia (SWCT).
  • providing the signal changes in the ventricular repolarization via the input/output interface comprises: automatically determining a wide complex heart beat classification for the wide complex heart beat(s) by comparing the signal change to a predetermined value using the one or more processors, wherein the wide complex heart beat classification comprises a ventricular source or a supraventricular source; and providing the wide complex heart beat classification via the input/output interface.
  • the signal change comprises a VT probability
  • the wide complex heart beat classification comprises a VT whenever the VT probability is greater than or equal to the predetermined value
  • the wide complex heart beat classification comprises a SWCT whenever the VT probability is less than the predetermined values.
  • the method further comprises selecting the predetermined value from a range of 0% to 100%.
  • the predetermined value comprises about 1%, 10%, 25%, 50%, 75%, 90% or 99%.
  • providing the signal changes in ventricular repolarization comprises providing a“shock” recommendation signal, a“no shock” recommendation signal, or no signal.
  • the method further comprises obtaining wide complex beat T- wave amplitudes and/or time-voltage areas and the baseline T-wave amplitudes and/or time- voltage areas from an electrocardiogram (ECG) signal, a ventricular electrogram (EMG) signal, a vectorcardiogram (VCG) signal and/or mathematically-synthesized vectorcardiogram (VCG) signal.
  • ECG electrocardiogram
  • EMG ventricular electrogram
  • VCG vectorcardiogram
  • VCG mathematically-synthesized vectorcardiogram
  • the T-wave amplitudes and/or time- voltage areas comprise a plurality of measured T-wave amplitudes and/or time-voltage areas of a ECG waveform, a EMG waveform, a VCG waveform, and/or a mathematically- synthesized vectorcardiogram (VCG) waveform above and below the isometric baseline.
  • VCG vectorcardiogram
  • receiving the one or more wide complex beat T-wave amplitudes and/or time-voltage areas, and one or more baseline T-wave amplitudes and/or time-voltage areas comprises: receiving a ECG data, a EMG data, a VCG data, or a mathematically- synthesized VCG data via the input/output interface or the memory; receiving a baseline ECG data, a baseline EMG data, a baseline VCG data, or a baseline mathematically- synthesized VCG data via the input/output interface or the memory; determining the one or more T-wave amplitudes and/or time-voltage areas from the ECG data, the EMG data, the VCG data, and/or the mathematically-synthesized VCG data using one or more processors; and determining the one or more baseline waveform amplitudes and/or time-voltage areas from the baseline ECG data, the baseline EMG data, the baseline VCG data, and/or the mathematically-synthesized V
  • the ECG data, the EMG data, the VCG data, or the mathematically-synthesized VCG data is generated or recorded before or after the baseline ECG data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically-synthesized VCG data.
  • the ECG data, the EMG data, the VCG data, and/or the mathematically-synthesized VCG data is generated or recorded after the baseline ECG data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically-synthesized VCG data and determining the changes in ventricular repolarization (i.e., T-wave changes).
  • the method further comprises generating or recording the ECG data, the EMG data, the VCG data, and/or the mathematically-synthesized VCG data and the baseline ECG data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically- synthesized VCG data using one or more sensors or devices.
  • the one or more sensors or devices comprise a 12-lead ECG device, a continuous ECG telemetry monitor, a stress testing system, an extended monitoring device, a smartphone-enabled ECG medical device, an external cardioverter-defibrillator therapy device, a subcutaneous implantable cardioverter defibrillators (S-ICD), an intracardiac or transvenous pacemaker device, an automated external defibrillators (AED), or an automatic implantable cardioverter defibrillator (AICD).
  • the computing device is integrated into the one or more sensors or devices; or the one or more sensors or devices are integrated into the computing device.
  • determining the changes in ventricular repolarization between the wide complex heart beat T-wave amplitudes and/or time-voltage areas and the baseline heart beat T-wave amplitudes and/or time-voltage areas comprises: receiving a wide complex heart beat waveform duration via the input/output interface or the memory; determining, using the one or more processors, a percent amplitude change (PAC) based on the wide complex heart beat T-wave amplitudes and the baseline T-wave amplitudes, and/or a percent time-voltage area change (PTVAC) based on the wide complex heart beat T-wave time-voltage areas and the baseline T-wave time-voltage areas; determining a classification probability based on the wide complex heart beat waveform duration, and the T-wave PAC and/or the T-wave PTVAC using the one or more processors; and wherein the signal change comprises the classification probability, and the classification probability comprises a VT probability, a SWCT probability, or a ventricular pacing probability.
  • PAC percent
  • determining the classification probability is further determined based one or more additional classification predictors.
  • the one or more classification predictors comprise changes in ventricular repolarization.
  • the T-wave PAC comprises a frontal T-wave PAC and a horizontal T-wave PAC
  • the T-wave PTVAC comprises a frontal T-wave PTVAC and a horizontal T-wave PTVAC.
  • determining the changes in ventricular repolarization between the wide complex heart beat T-wave amplitudes and/or time-voltage areas and the baseline heart beat T-wave amplitudes and/or time-voltage areas comprises: receiving a WCT QRS duration via the input/output interface or the memory; the one or more wide complex heart beat T-wave amplitudes and/or time-voltage areas comprise one or more frontal plane WCT positive T-wave amplitudes and/or time-voltage areas, one or more horizontal plane WCT positive T-wave amplitudes and/or time-voltage areas, one or more frontal plane WCT negative T-wave amplitudes and/or time-voltage areas, and one or more horizontal plane WCT negative T-wave amplitudes and/or time-voltage areas; the one or more the baseline heart beat T-wave amplitudes and/or time-voltage areas comprise one or more frontal plane baseline positive T-wave amplitudes and/or time-voltage areas, one or more
  • the statistical or machine learning process comprises a linear regression algorithm, a logistic regression model, a linear discriminate analysis algorithm, a Naive Bayes algorithm, a computational model using artificial neural networks, a computational model based on classification or regression trees, a k-nearest neighbors based model, a support vector machine based model, a boosting algorithm, or an ensemble machine learning algorithm.
  • the input/output interface comprises a remote device, and the remote device is communicably coupled to the one or more processors via one or more networks.
  • the method further comprises providing a recommendation to select or exclude a therapy, medication, diagnostic testing or referral for a patient based on the signal change.
  • the computing device comprises a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, or a medical device.
  • the method can be implemented using a non- transitory computer readable medium that when executed causes the one or more processors to perform the method.
  • Another embodiment of the present invention provides an apparatus for classifying a wide complex heart beat(s) comprising an input/output interface, a memory, and one or more processors communicably coupled to the input/output interface and the memory.
  • the one or more processors receive one or more wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization, and one or more baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization via the input/output interface or the memory, determine a signal change in ventricular repolarization between the wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization and the baseline heart beat waveform amplitudes and/or time- voltage areas of ventricular repolarization using the one or more processors, and provide the signal change in ventricular repolarization via the input/output interface, wherein the signal change in ventricular repolarization provides an indication whether the wide complex heart beat(s) is from a
  • the signal change in ventricular repolarization further provides the indication whether the wide complex heart beat(s) is due to ventricular pacing or a premature ventricular contraction (PVC).
  • the wide complex heart beat(s) comprise a wide complex tachycardia (WCT)
  • the ventricular source comprises a ventricular tachycardia (VT)
  • the supraventricular source comprises a supraventricular wide complex tachycardia (SWCT).
  • the signal change in ventricular repolarization comprises a VT probability
  • the wide complex heart beat classification comprises a VT whenever the VT probability is greater than or equal to the predetermined value
  • the wide complex heart beat classification comprises a SWCT whenever the VT probability is less than the predetermined value.
  • the one or more processors select the predetermined value from a range of 0% to 100%. In another aspect, the predetermined value comprises about 1%, 10%, 25%, 50%, 75%, 90% or 99%. In another aspect, the one or more processors provide the signal change in ventricular repolarization by providing a “shock” recommendation signal, a“no shock” recommendation signal, or no signal.
  • the wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization comprise T wave amplitudes and/or time-voltage areas
  • the baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization comprise baseline T wave amplitudes and/or time-voltage ares
  • the T wave amplitudes and/or time-voltage areas and baseline T wave amplitudes and/or time- voltage areas are obtained from an electrocardiogram (ECG) signal, a ventricular electrogram (EMG) signal, a vectorcardiogram (VCG) and/or a mathematically-synthesized VCG signal.
  • ECG electrocardiogram
  • EMG ventricular electrogram
  • VCG vectorcardiogram
  • the wide complex heart beat T wave amplitudes and/or time- voltage areas comprise a plurality of measured T wave amplitudes and/or time-voltage areas of a ECG waveform, a EMG waveform, a VCG waveform, and/or a mathematically- synthesized VCG waveform above and below an isoelectric baseline; and the baseline heart beat T wave amplitudes and/or time-voltage areas comprise a plurality of measured T wave amplitudes and/or time-voltage areas of a baseline ECG waveform, a baseline EMG waveform, a baseline VCG waveform, and/or a baseline mathematically-synthesized VCG waveform above and below the isoelectric baseline.
  • the one or more processors receive the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization, and one or more baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization by: receiving a ECG data, a EMG data, a VCG data, and/or a mathematically-synthesized VCG data via the input/output interface or the memory; receiving a baseline ECG data, a baseline EMG data, baseline VCG data, and/or baseline mathematically-synthesized VCG data via the input/output interface or the memory; determining the one or more waveform amplitudes and/or time-voltage areas of ventricular repolarization from the ECG data, the EMG data, the VCG data, and/or the mathematically- synthesized VCG data; and determining the one or more baseline waveform amplitudes and/or time-voltage areas of ventricular repolarization from the
  • the ECG data, the EMG data, theVCG data, and/or the mathematically-synthesized VCG data is generated or recorded before or after the baseline ECG data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically-synthesized VCG data.
  • the ECG data, the EMG data, the VCG data and/or the mathematically-synthesized VCG data is generated or recorded after the baseline ECG data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically-synthesized VCG data and determining the signal change in ventricular repolarization.
  • the ECG data, the EMG data, the VCG data, and/or the mathematically-synthesized VCG data and the baseline ECG data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically-synthesized VCG data are generated or recorded using one or more sensors or devices.
  • the one or more sensors or devices comprise a 12-lead ECG device, a continuous ECG telemetry monitor, a stress testing system, an extended monitoring device, a smartphone-enabled ECG medical device, a cardioverter-defibrillator therapy device, a subcutaneous implantable cardioverter defibrillator (S-ICD), an intracardiac or transvenous pacemaker device, an automated external defibrillator (AED), or an automatic implantable cardioverter defibrillator (AICD).
  • a 12-lead ECG device a continuous ECG telemetry monitor
  • a stress testing system e.g., a smartphone-enabled ECG medical device
  • a cardioverter-defibrillator therapy device e.g., a subcutaneous implantable cardioverter defibrillator (S-ICD), an intracardiac or transvenous pacemaker device, an automated external defibrillator (AED), or an automatic implantable cardioverter def
  • the input/output interface, the memory and the one or more processors are integrated into the one or more sensors or devices; or the one or more sensors or devices are integrated into a computing device comprising the input/output interface, the memory and the one or more processors.
  • the one or more processors determine the signal change in ventricular repolarization between the wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization and the baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization by: receiving a wide complex heart beat QRS waveform duration via the input/output interface or the memory; determining a percent T-wave amplitude change (PAC) based on the wide complex heart beat waveform amplitudes of ventricular repolarization and the baseline heart beat waveform amplitudes of ventricular repolarization, and/or a percent T-wave time-voltage area change (PTVAC) based on the wide complex heart beat waveform time-voltage areas of ventricular repolarization and the baseline heart beat waveform time-voltage areas of ventricular repolarization; determining a classification probability based on the wide complex heart beat waveform QRS duration, and the T-wave PAC and/or the T-wave PTVAC; and
  • determining the classification probability is further determined based one or more additional classification predictors.
  • the PAC comprises a frontal T-wave PAC and a horizontal T-wave PAC
  • the T- wave PTVAC comprises a frontal T-wave PTVAC and a horizontal T-wave PTVAC.
  • the one or more processors determine the signal change in ventricular repolarization between the wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization and the baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization by: receiving a WCT QRS duration via the input/output interface or the memory; the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization comprise one or more frontal plane WCT positive T wave amplitudes and/or time-voltage areas, one or more horizontal plane WCT positive T wave amplitudes and/or time-voltage areas, one or more frontal plane WCT negative T wave amplitudes and/or time-voltage areas, and one or more horizontal plane WCT negative T wave amplitudes and/or time- voltage areas; the one or more the baseline heart beat waveform amplitudes and/or time- voltage areas of ventricular repolarization comprise one or more
  • the statistical or machine learning process comprises a linear regression algorithm, a logistic regression model, a linear discriminate analysis algorithm, a Naive Bayes algorithm, a computational model using artificial neural networks, a computational model based on classification or regression trees, a k-nearest neighbors based model, a support vector machine based model, a boosting algorithm, or an ensemble machine learning algorithm.
  • the input/output interface comprises a remote device, and the remote device is communicably coupled to the one or more processors via one or more networks.
  • the one or more processors provide a recommendation to select or exclude a therapy, medication, diagnostic testing or referral for a patient based on the signal change in ventricular repolarization.
  • apparatus comprises a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, or a medical device.
  • FIGURE 1A depicts a schematic representation of a normal ECG
  • FIGURE 1B depicts an example of ECG data collected and recorded from a patient’s 12-lead ECG
  • FIGURE 2 depicts a schematic representation of the resultant QRS amplitude changes that manifest between a patient’s baseline and WCT ECG;
  • FIGURES 3A-3E depicts panels that summarize the expected range of mean electrical vector of ventricular depolarization changes in the frontal ECG plane after WCT event onset;
  • FIGURES 4A-4E depicts panels that summarize the expected range of mean electrical vector changes of ventricular depolarization changes in the horizontal ECG plane after WCT event onset;
  • FIGURES 5A-5B are graphic depictions of select ECG lead combinations utilized by the frontal (aVR, aVL, aVF) and horizontal (V1, inverse V4, V6) PAC formulas in accordance with one embodiment of the present invention
  • FIGURES 6A-6C depict the structure of the frontal PAC formula, horizontal PAC formula and amplitude based WCT formula in accordance with one embodiment of the present invention
  • FIGURE 7 depicts a flow diagram representing the inputs and output of the amplitude based WCT Formula in accordance with one embodiment of the present invention
  • FIGURE 8 illustrates the inclusion criteria and reasons for exclusion during validation cohort selection for the WCT Formula validation
  • FIGURE 9 is Table 1 showing the ECG characteristics of the derivation cohort
  • FIGURE 10 is Table 2 showing the clinical characteristics of the derivation cohort
  • FIGURE 11 is Table 3 showing the mean and standard deviation (SD) of measured and calculated ECG variables among VT or SWCT groups within the derivation cohort;
  • FIGURES 12A-12C are box-plots demonstrating the median and proportional distribution of WCT QRS duration (ms) (FIGURE 12A), frontal PAC (%) (FIGURE 12B) and Horizontal PAC (%) (FIGURE 12C) for VT and SWCT groups in accordance with one embodiment of the present invention;
  • FIGURE 12D is a table showing electrocardiographic variables among baseline ECG sub-groups in accordance with on embodiment of the present invention.
  • FIGURE 13 is a graph of a receiver operating characteristic (ROC) curve depicting amplitude based WCT Formula diagnostic performance in accordance with one embodiment of the present invention
  • FIGURE 14 is Table 4 showing the ECG characteristics of the validation cohort
  • FIGURE 15 is Table 5 showing the clinical characteristics of the validation cohort
  • FIGURES 16A and 16B are histograms demonstrating the distribution of clinically diagnosed VT and SWCT according to the amplitude based WCT Formula diagnostic performance at on probability estimates (0.000%-99.999%) for the validation cohort;
  • FIGURE 17 is Table 6 showing the diagnostic performance of various VT probability partitions for the validation cohort in accordance with one embodiment of the present invention.
  • FIGURES 18A and 18B are Venn diagrams summarizing the distribution of shared and non-shared VT (FIGURE 18A) and SWCT (FIGURE 18B) diagnoses established by three diagnostic standards for the validation cohort: (1) clinical diagnosis, (2) ECG laboratory interpretation and (3) amplitude based WCT Formula’s 50% VT probability partition;
  • FIGURES 19A and 19B are tables showing the electrocardiographic characteristics of clinical SWCT classified as VT and clinical VT classified as SWCT by the amplitude based WCT Formula’s 50% VT probability partition for the validation cohort;
  • FIGURE 20 depicts a schematic representation of a normal ECG with time-voltage areas
  • FIGURES 21A-21B are graphic depictions of select ECG lead combinations utilized by the frontal (aVR, aVL, aVF) and horizontal (V1, inverse V4, V6) PAC formulas with respect to time-voltage areas in accordance with one embodiment of the present invention
  • FIGURE 22 depicts a schematic representation of the resultant QRS time-voltage area changes that manifest between a patient’s baseline and WCT ECG;
  • FIGURES 23A-23C depict derivations of the frontal PTVAC formula, horizontal PTVAC formula and time-voltage are based WCT formula in accordance with one embodiment of the present invention;
  • FIGURES 24A-24B are box-plots demonstrating the median and proportional distribution of frontal PTVAC (%) (FIGURE 24A) and horizontal PTVAC (%) (FIGURE 24B) for VT and SWCT groups in accordance with one embodiment of the present invention
  • FIGURES 25A-25B are ROC graphs depicting the diagnostic performance of frontal PTVAC (%) (FIGURE 25A) and horizontal PTVAC (%) (FIGURE 25B) in accordance with one embodiment of the present invention
  • FIGURE 26 is a graph depicting the time-voltage area based WCT Formula’s diagnostic performance for the derivation cohort (AUC of 0.95) in accordance with one embodiment of the present invention
  • FIGURES 27A and 27B are histograms demonstrating the distribution of clinical VT and SWCT according to the time-voltage area based WCT Formula diagnostic performance at VT probability estimates (0.000%-99.999%) in accordance with one embodiment of the present invention
  • FIGURE 28 is a graph depicting the VCG-VT Model’s diagnostic performance for the testing cohort (AUC of 0.97) in accordance with one embodiment of the present invention
  • FIGURE 29 is a block diagram of an apparatus in accordance with one embodiment of the present invention.
  • FIGURE 30 is a flow chart of a method in accordance with one embodiment of the present invention.
  • FIGURE 31 is a flow diagram of validation cohort selection for the VT Prediction Model validation
  • FIGURES 32A-32B are examples of paired VT (FIGURE 32A) and baseline (FIGURE 32B) ECGs assigned high VT probability (99.0006%) by the VT Prediction Model in accordance with one embodiment of the present invention
  • FIGURES 33A-33B are examples of paired SWCT (FIGURE 33A) and baseline (FIGURE 33B) ECGs assigned low VT probability (4.3609%) by the VT Prediction Model in accordance with one embodiment of the present invention
  • FIGURES 34A-34B are examples of paired SWCT (FIGURE 34A) and baseline (FIGURE 34B) ECGs assigned low VT probability (6.3613%) by the VT Prediction Model in accordance with one embodiment of the present invention
  • FIGURE 35 is a table showing the clinical and ECG laboratory diagnosis
  • FIGURE 36 is a table showing the patient characteristics
  • FIGURE 37 is a table showing the electrocardiographic variables
  • FIGURE 38 is a table showing the electrocardiographic variables among baseline ECG sub-groups
  • FIGURE 39A-39D are box-plots demonstrating the median and proportional distribution of WCT QRS duration (ms) (FIGURE 39A), WCT QRS duration change (ms) (FIGURE 39B), QRS axis change ( ⁇ ) (FIGURE 39C) and T axis change ( ⁇ ) (FIGURE 39D) in accordance with one embodiment of the present invention;
  • FIGURE 40 is a graph depicting a receiver operating characteristic curve for the VT Prediction Model (AUC of 0.942) in accordance with one embodiment of the present invention.
  • FIGURE 41 is a table showing the percent VT probability partitions in accordance with one embodiment of the present invention.
  • FIGURE 42 is a table showing the correct and erroneous WCT diagnoses in accordance with one embodiment of the present invention.
  • FIGURE 43 is a table summarizing the clinical diagnosis and ECG laboratory interpretation data of the validation cohort in accordance with one embodiment of the present invention.
  • FIGURE 44 is a table summarizing the patient characteristics of VT and SWCT groups for the validation cohort in accordance with one embodiment of the present invention.
  • FIGURE 45 is a graph depicting a receiver operating characteristic curve for the VT Prediction Model (AUC of 0.900; CI 0.862 to 0.939) in accordance with one embodiment of the present invention.
  • FIGURES 46A and 46B are histograms demonstrating the distribution of VT and SWCT according to the VT Prediction Model’s VT probability estimates in accordance with one embodiment of the present invention
  • FIGURES 47A-47E depicts panels that summarize the expected changes to the mean electrical vector of ventricular depolarization following WCT initiation;
  • FIGURES 48A-48E depicts panels that summarize the expected changes to the mean electrical vector of ventricular repolarization upon WCT initiation;
  • FIGURES 49-49B are examples of paired VT (FIGURE 49A) and baseline (FIGURE 49B) ECGs assigned low VT probability (9.8704%) by the VT Prediction Model in accordance with one embodiment of the present invention
  • FIGURES 50A-50B are examples of paired SWCT (FIGURE 50A) and baseline (FIGURE 50B) ECGs assigned high VT probability (54.0039%) by the VT Prediction Model in accordance with one embodiment of the present invention
  • FIGURE 51 is a flow chart of a method in accordance with another embodiment of the present invention.
  • FIGURE 52 is a flow chart of a method in accordance with another embodiment of the present invention.
  • WCT wide complex tachycardia
  • VT ventricular tachycardia
  • T wave an analysis of ventricular repolarization
  • One type of embodiments of the present invention provides a new electrophysiological principle (degree of QRS or ventricular electrogram signal change in amplitude and/or time-voltage area between the WCT and baseline heart rhythm helps distinguish ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT)) that can be exploited by ECG, EMG, VCG, and mathematically-synthesized VCG interpretation software to render precise and accurate predictions of VT verses SWCT.
  • the WCT differentiation method described herein can be automatically implemented by contemporary ECG interpretation software.
  • ECG electrogram
  • VCG vectorcardiogram
  • other medical devices that analyze ECG signals, electrogram (EMG) signals, vectorcardiogram (VCG) signals and/or mathematically-synthesized VCG signals from the heart
  • ECG electrogram
  • VCG vectorcardiogram
  • other formulas or algorithms based on the foregoing principle and other information described herein can be used to predict the source of a wide complex beat (supraventricular or ventricular) by diagnostic interpretation software analysing ECG, EMG, VCG, and mathematically-synthesized VCG signals.
  • the present invention is not limited to the WCT Formulas described herein.
  • the WCT Formulas were designed to effectively, accurately and automatically differentiate WCT into VT, which is usually a dangerous heart rhythm, and SWCT, which is ordinarily a less hazardous heart rhythm.
  • VT and SWCT are most often non-invasively diagnosed using a 12-lead ECG.
  • the present invention is applicable to any current or future technology that provides the relevant data using known or unknown detection devices or sensors (i.e., any device that generates and analyzes ECG signals, ventricular EMG signals, VCG signals and/or mathematically synthesized VCG signals).
  • the WCT Formulas are logistic regression models that deliver an automatic prediction VT likelihood (i.e., % VT probability) using ECG measurements (e.g. WCT duration) and calculations (e.g., frontal and horizontal Percent Amplitude Change (PAC), or frontal and horizontal Percent Time-Voltage Area Change (PTVAC)) derived from paired WCT and baseline ECGs.
  • the frontal and horizontal PAC and PTVAC formulas are highly predictive determinants of VT and SWCT, wherein a low PAC (%) or PTVAC (%) indicates SWCT and a high PAC (%) or PTVAC (%) indicates VT.
  • the frontal and horizontal PAC or PTVAC calculations are independent predictors of VT. Each calculation is able to provide a reliable means to effectively distinguish VT and SWCT. They can also be used to differentiate discrete ventricular depolarizations due to premature ventricular contractions, ventricular pacing, and wide complex beats from a supraventricular source.
  • FIGURES 1A and 1B the 12-lead ECG and resulting data used in the WCT Formula will be described.
  • the ECG currently is the most commonly used test to determine whether a patient’s underlying heart rhythm is normal or abnormal.
  • the 12-lead ECG records the electrical activity of the heart using 12 separate leads. Each lead records unique QRS complexes representative of the heart’s ventricular depolarization.
  • FIGURE 1A is a schematic representation of a stereotypical ECG pattern for a single heart beat 100.
  • the QRS complex waveform 102 is the combination of three graphical deflections: (1) the Q wave 104 having a downward deflection immediately following the P wave 106; (2) the R wave 108 having an upward deflection immediately following the Q wave 104; and (3) the S wave 110 having a downward deflection following the R wave 108.
  • the Q wave 104, R wave 108 and S wave 110 occur in rapid succession and are encompassed within the QRS complex waveform 102 and accompanying time interval, QRS duration 112.
  • the T-wave 114 follows the S wave 110.
  • Each wave has amplitude denoted as PA, QA, RA, SA and TA.
  • the QT interval 116 is the time interval extending from the onset of the QRS complex waveform 102 to the end of the T wave 114.
  • the QRS complex 102 is divided into positive (+) amplitudes 118 and negative (-) amplitudes 120.
  • the positive (+) amplitudes 118 are the vertical QRS complex deflections above the isoelectric baseline 122, namely the amplitude of r/R wave and r’/R’ wave.
  • the negative (-) amplitudes 120 are the vertical QRS complex deflections below the isoelectric baseline 122, namely the amplitude of q or QS wave, s/S wave and s’/S’ wave.
  • QRS complex waveform 102 attributes namely q or QS, r/R, s/S, r’/R’, s’/S’ durations (ms), amplitudes (mV), and time-voltage areas (mV ⁇ ms)
  • QRS complex waveform 102 attributes namely q or QS, r/R, s/S, r’/R’, s’/S’ durations (ms), amplitudes (mV), and time-voltage areas (mV ⁇ ms)
  • FIGURE 1B depicts a measurement matrix showing an example of 12-lead ECG data recorded and calculated by computerized ECG interpretation software.
  • the 12 leads are denoted as V1, V2, V3, V4, V5, V6, I, aVL, II, aVF, III, and aVR.
  • QRS waveform deflection (q or QS, r/R, s/S, r’/R’, s’/S’) measurements including duration (ms) and amplitude (mV) are provided by GE Healthcare’s MUSE ECG interpretation software and databank.
  • the amplitude (_A) and duration (_D) data for the various waves are denoted as PA, PPA, QA, QD, RA, RD, SA, SD, RPA, RPD,SPA, and SPD.
  • PA amplitude
  • PPA QA, QD
  • RA RA
  • RD RD
  • SA SD
  • RPA RPD
  • SPA SPA
  • the negative (-) amplitudes 120 are the vertical QRS complex deflections below the isoelectric baseline 122, namely the q or QS wave amplitude (mV) 154, s/S wave amplitude (mV) 156, and s’/S’ wave amplitude (mV) 158.
  • the voltage amplitude measurements from specific leads frontal ECG plane: V1, V4, V6; and horizontal ECG plane: aVL, aVF, aVR
  • frontal and horizontal PAC formulas to generate the frontal and horizontal PACs (%).
  • time-voltage area measurements of separate QRS waveform deflections can be automatically provided by computerized ECG interpretation software and electronic databanks (e.g., MUSE from GE Healthcare, etc.).
  • SWCTs As a result, many SWCTs, especially those with pre-existing aberrancy or ventricular pacing, demonstrate substantial electrocardiographic similarity with the baseline ECG. On the contrary, SWCTs with“functional” aberration exhibit recognizably different QRS complex configurations. However, since most functional SWCTs demonstrate antegrade impulse propagation and ventricular depolarization confined in the His-Purkinje network, they are destined to express a relatively constrained variety of electrocardiographically distinct QRS complexes.
  • the amplitude and time-voltage area based WCT Formulas can be similarly applied to these types of defibrillator devices because they either use ECG signals using surface ECG electrodes (or a modification thereof with the subcutaneous ICD) or EMG signals derived from intracardiac and extracardiac electrodes (in the case of AICDs and pacemakers) to help distinguish different heart rhythms. Because these devices acquire ventricular depolarization signals from surface ECG electrodes or EMG electrodes, the invention, and its principles of QRS (or ventricular EMG signal) amplitude (or time-voltage area) change, can be applied to help them more accurately discriminate SWCT and VT.
  • QRS ventricular EMG signal
  • VTs have an expansive means to which their ventricular electrograms (EMGs) may be morphologically distinct from the ventricular EMGs of the baseline heart rhythm.
  • EMGs ventricular electrograms
  • SWCTs depolarize the ventricular myocardium is ordinarily confined to the same His-Purkinje network or implantable device system utilized by the baseline heart rhythm; in rarer instances SWCTs may be to ventricular pre-excitation using separate atrioventricular accessory pathways.
  • SWCTs As a result, many ventricular EMGs from SWCTs, especially those with pre-existing aberrancy or ventricular pacing, demonstrate marked similarity with the ventricular EMGs for the patient’s baseline heart rhythm. On the contrary, SWCTs with“functional” aberration exhibit recognizably different ventricular EMG configurations. However, since functional SWCTs still demonstrate antegrade impulse propagation and ventricular depolarization confined in the His-Purkinje network, they tend to express a relatively constrained variety of ventricular EMG complexes.
  • various embodiments of the present invention can be used to further help guide therapy decisions (e.g.,“shock patient” for VT OR“do not shock the patient” for SWCT).
  • the likelihood of appropriate device defibrillations i.e., appropriate shocks
  • the likelihood of inappropriate device defibrillations may be increased while decreasing the likelihood of inappropriate device defibrillations (i.e. inappropriate shocks).
  • various embodiments of the present invention can be used by conventional transvenous lead based devices like AICDs or pacemakers or new intracardiac devices (e.g. Micra Transcatheter Pacing System).
  • EMG devices analyze multiple separate bipolar EMG signals derived from various intracardiac and extracardiac electrodes combinations (e.g., right ventricular coil to AICD generator housing OR extracardiac SVC coils to AICD generator housing OR RV right ventricular tip to right ventricular coil OR any other combination).
  • implanted devices usually store 2– 4 EMG channels which are analyzed by embedded interpretation algorithms. These EMG channels (separately or in combination) can be examined to establish the degree (or percentage) of ventricular EMG amplitude or time-voltage area change between the WCT and baseline EMG. This procedure/method can help distinguish VT and SWCT.
  • FIGURE 2 a schematic representation 200 of the resultant QRS amplitude changes that manifest between a patient’s baseline ECG 202 and WCT ECG 204 is shown.
  • the transition between a patient’s baseline and WCT ECG (or vice versa) is inherently associated with changes (large or small) in QRS amplitude.
  • FIGURES 3A-3E and 4A-4E panels that summarize mean electrical vector changes in the frontal (FIGURE 3A-3E) and horizontal (FIGURES 4A-4E) ECG planes after WCT event onset are shown.
  • the mean electrical vector (of the frontal or horizontal ECG plane) represents the summative electrical vector of ventricular depolarization. This value is determined from the QRS amplitudes derived from the 12-lead ECG.
  • Heavy arrows represent the mean electrical vector for an ECG with demonstrating normal sinus rhythm (Panels 3A-3D, 4A-4D) or pre-existing BBB (Panels 3E, 4E).
  • Shaded regions depict the range of potential axes and voltage intensities for mean electrical vectors that occur after WCT onset.
  • Select ECG leads utilized by the frontal (aVR, aVL, aVF) and horizontal (V1, V4, V6) PAC formulas are highlighted.
  • Inverse V4 is the inverted equivalent of its planar opposite: lead V4.
  • Panels 3A, 4A demonstrates the mean electrical vector for a typical normal sinus baseline ECG.
  • Panels 3B-3E, 4B-4E demonstrate the expected range of mean electrical vectors following the onset of various WCTs.
  • Panels 3B, 4B demonstrates VT’s incredibly expansive range of potential mean electrical vectors.
  • Panels 3C-3D, 4C-4D demonstrate the relatively constrained mean electrical vector changes for SWCTs due to functional RBBB (Panels 3C, 4C) and LBBB (Panels 3D, 4D).
  • SWCTs have“restricted” changes to the mean electrical vector that translates into smaller frontal and horizontal PACs
  • VTs tend to demonstrate “expansive” changes in the mean electrical vector that translates into larger frontal and horizontal PACs. Therefore, VT demonstrates much greater frontal and horizontal PACs than SWCT.
  • the larger frontal and horizontal PACs strongly predict VT, whereas smaller frontal and horizontal PACs predicted SWCT.
  • FIGURES 5A-5B graphic depictions of select ECG lead combinations utilized by the frontal (aVR, aVL, aVF)(FIGURE 5A) and horizontal (V1, inverse V4, V6)(FIGURE 5B) PAC formulas in accordance with one embodiment of the present invention are shown.
  • the QRS amplitude change ( ⁇ ) that manifests between the baseline and WCT ECGs at these selected leads is the foundation for each PAC calculation.
  • the absolute QRS amplitude changes ( ⁇ ’s) that manifest in lead V4 are mathematically equivalent to its planar opposite: inverse V4.
  • VTs often demonstrate QRS durations less than 140 ms (3, 4, 18, 19). This tends to occur among VTs that rapidly utilize the His-Purkinje network or develop in patients without structural heart disease.
  • the findings described herein support that VTs demonstrate longer QRS durations than SWCTs (see e.g., FIGURE 11).
  • a logistic regression formula (i.e. WCT Formula) capable of establishing accurate VT probability predictions using measurements and calculations provided by contemporary ECG interpretation software was created.
  • WCT Formula a logistic regression model
  • Other“machine learning” or artificial intelligence prediction methods e.g., artificial neural networks, support vector machines, Random Forests, etc.
  • the amplitude based WCT Formula incorporates the strong independent WCT predictors including (1) WCT QRS duration (ms), (2) frontal PAC (%) and (3) horizontal PAC (%).
  • the predictive contribution of each WCT predictor is concomitantly“weighed” according to their influence on the binary outcome (VT vs.
  • SWCT SWCT to render an exact VT probability estimation.
  • the amplitude based WCT Formula estimates higher VT probabilities for ECG pairs demonstrating greater WCT QRS durations, frontal PAC and/or horizontal PAC.
  • the amplitude based WCT Formula estimates lower VT probability for ECG pairs with smaller WCT QRS durations, frontal PAC and/or horizontal PAC.
  • the WCT Formula’ s logistic regression model structure uses select independent WCT predictors (WCT QRS duration (ms), frontal PAC (%) and horizontal PAC (%)) to render a precise prediction of VT probability (%).
  • WCT QRS duration ms
  • frontal PAC %
  • horizontal PAC %
  • Each WCT predictor (X x ) was assigned beta coefficients (b x ) according to their influence on the binary outcome (VT vs. non-VT).
  • The“constant” term (B 0 ) represents the y-intercept of the least squares regression line.
  • Discrete measured or calculated WCT predictor values derived from paired baseline and WCT ECGs are integrated into the amplitude based WCT Formula to calculate VT probability (P VT ).
  • a calculation series is used to quantify the degree of QRS amplitude change that manifests between the baseline ECG and WCT event by converting raw ECG measurements into the frontal and horizontal PAC.
  • the measured amplitudes (mV) of QRS waveforms above (+) (r/R and r’/R’) and below (-) (q/QS, s/S, and s’/S’) the isoelectric baseline from select frontal (aVR, aVL, aVF) and horizontal (V1, V4, V6) ECG leads were used to derive each calculation. Calculations were computed using JMP Pro 10 statistical software. Baseline Amplitude (BA), Absolute Amplitude Change (AAC) and Percent Amplitude Change (PAC) were calculated for both the frontal and horizontal ECG planes.
  • BA Baseline Amplitude
  • AAC Absolute Amplitude Change
  • PAC Percent Amplitude Change
  • LeadX denotes V1, V4, V6 (horizontal plane) or aVL, aVR, aVF (frontal plane).
  • ANC and APC equations have the“absolute” mathematical annotation (e.g. ⁇ equation’s contents ⁇ ).
  • Absolute Amplitude Change represents the absolute summative difference in QRS amplitude between the WCT and baseline ECG.
  • Baseline Amplitude (BA) represents the total sum amplitude of (+) and (-) QRS waveforms in the baseline ECG.
  • Percent Amplitude Change represents the percent change in QRS amplitude between the WCT and baseline EC G.
  • FIGURES 6A and 6B A diagram showing the derivation of the frontal PAC formula and horizontal PAC formula are shown in FIGURES 6A and 6B, respectively.
  • the amplitude based WCT Formula is a binary outcome logistic regression model that uses select independent WCT predictors: (1) WCT duration (ms), (2) frontal PAC (%) and (3) horizontal PAC (%).
  • Each WCT predictor (X x ) was assigned beta coefficients (b x ) according to their influence on the binary outcome (VT vs. non-VT).
  • The“constant” term (B 0 ) represents the y-intercept of the least squares regression line.
  • Discrete measured or calculated WCT predictor values derived from paired baseline and WCT ECGs are incorporated into the WCT Formula to calculate VT probability
  • ⁇ is the weighted sum of the WCT predictors
  • ⁇ ⁇ is the probablity of VT; independent WCT predictors n are
  • FIGURE 6C A diagram showing the amplitude based WCT Formula’s logistic regression structure is shown in FIGURE 6C.
  • FIGURE 7 a flow diagram 700 representing the inputs and output of the amplitude based WCT Formula in accordance with one embodiment of the present invention is shown.
  • the baseline ECG QRS waveform measurements may be obtained from GE Healthcare’s MUSE or other computerized ECG interpretation software in block 702, and the WCT ECG QRS waveform measurements may be obtained from GE Healthcare’s MUSE or other computerized ECG interpretation software in block 704.
  • the baseline (+) and (-) waveform amplitudes of V1, V4, V6 in block 706 and WCT (+) and (-) waveform amplitudes of V1, V4, V6 in block 708 are used in the horizontal PAC formula in block 710 to provide the horizontal PAC (%) in block 712.
  • the baseline (+) and (-) waveform amplitudes of aVL, aVR, aVF in block 714 and WCT (+) and (-) waveform amplitudes of aVL, aVR, aVF in block 716 are used in the frontal PAC formula in block 718 to provide the horizontal PAC (%) in block 720.
  • the WCT QRS duration is provided in block 722, which is used along with the horizontal PAC (%) in block 712 and frontal PAC (%) in block 720 by the amplitude based WCT Formula in block 724 to provide the VT Probability (%) in block 726.
  • a two-part study was designed to build and validate the amplitude based WCT Formula capable of automatic VT probability estimation.
  • Part 1 a derivation cohort of paired WCT and subsequent baseline ECGs was used to construct a logistic regression model using the strongest independent predictors of VT and SWCT. Independent predictors including WCT QRS duration (ms), frontal ECG plane percent amplitude change (PAC) (%) and horizontal ECG plane percent amplitude change (PAC) (%) were incorporated into the amplitude based WCT Formula.
  • the amplitude based WCT Formula’s performance was prospectively tested using a separate validation cohort of paired WCT and subsequent baseline ECGs.
  • Electrocardiogram pairs were identified using a MUSE ECG databank system (GE Healthcare). Electrocardiograms fulfilling WCT criteria (QRS duration 3 120 ms, heart rate 3 100 bpm) plus an ECG laboratory interpretation diagnosis of (1)“ventricular tachycardia,” (2)“supraventricular tachycardia,” or (3)“wide complex tachycardia” were defined as WCT events. Baseline ECGs were defined as the most proximate non-WCT ECG obtained after the WCT event.
  • Electrocardiogram pairs were excluded if the WCT did not have a subsequent baseline ECG or definite clinical diagnosis recorded within the patient’s electronic medical record. Polymorphic VTs and irregular SWCTs with varying atrioventricular (AV) conduction were excluded. Abbreviated WCTs that were not the dominant rhythm featured on the 12–lead ECG were excluded. Electrocardiogram pairs found to have irreconcilable faulty measurements (eg. QRS amplitude measurement of a pacing spike) or alternative lead placements (eg. right-sided chest leads) were excluded.
  • QRS amplitude measurement of a pacing spike e. QRS amplitude measurement of a pacing spike
  • alternative lead placements eg. right-sided chest leads
  • This version of the WCT Formula was developed and tested using two cohorts.
  • the derivation cohort consisted of 328 paired WCT (160 VT, 168 SWCT) and baseline ECGs from 229 patients presenting to the Mayo Clinic Rochester (September 2011 - March 2015).
  • the validation cohort was comprised of 313 paired WCT (123 VT, 190 SWCT) and baseline ECGs from 228 patients presenting to the Mayo Clinic Rochester and/or Mayo Clinic Health System of South Eastern Minnesota– including 40 additional patient care locations: community hospitals, emergency departments, and outpatient clinics (April 2015 - November 2016).
  • ECGs were formally interpreted at the Mayo Clinic ECG laboratory. ECG interpretation was under the supervision of a rotating consortium of attending cardiologists and electrophysiologists. Each supervising interpreter possessed extensive ECG interpretation experience along with complete access to the patient’s electronic medical record (including archived 12-lead ECGs). The interpretation strategy(s) utilized for each WCT was up to the supervising interpreter’s discretion. The degree of diagnostic certainty reported by the ECG laboratory for each WCT was semi-qualitatively re-categorized: (1) definite VT, (2) probable VT, (3) definite SWCT, (4) probable SWCT and (5) undifferentiated. The time separation between the WCT and subsequent baseline ECG was recorded.
  • the patient’s clinical diagnosis was identified from the electronic medical record.
  • the medical providers responsible for WCT diagnoses were categorized according to their level of expertise: (1) heart rhythm cardiologist, (2) non-heart rhythm cardiologist and (3) non-cardiologist.
  • the final WCT rhythm diagnosis was determined by the patient’s“most experienced” overseeing medical provider (heart rhythm cardiologist > non-heart rhythm cardiologist > non-cardiologist).
  • Each diagnosing provider had access to the ECG laboratory’s formal WCT interpretation. The completion of an electrophysiology procedure supporting the clinical diagnosis was recorded.
  • VT probability partitions were evaluated according to their agreement with clinical diagnosis.
  • Kappa statistics were applied to describe the diagnostic agreement between (1) clinical diagnosis, (2) ECG laboratory interpretation and (3) the amplitude based WCT Formula’s 50% VT probability partition.
  • McNemar’s test was used to test for differences among diagnostic standards.
  • Statistical analyses were completed using SAS version 9.4.
  • Table 1 shows the ECG characteristics of the derivation cohort, which consisted of 160 VTs and 168 SWCTs from 229 patients.
  • the majority of clinical diagnoses were established by heart rhythm cardiologists or non-heart rhythm cardiologists (86.6%).
  • the VT group had comparatively more clinical diagnoses established by heart rhythm cardiologists than the SWCT group (VT 93.8% vs. SWCT 44.6%, p ⁇ 0.001).
  • the SWCT group had a substantially higher percentage of clinical diagnoses established by non-cardiologists (SWCT 23.2% vs. VT 3.1%, p ⁇ 0.001).
  • the majority of WCTs were given definitive or probable interpretive diagnoses by the ECG laboratory (91.2%).
  • Table 2 shows the clinical characteristics of the derivation cohort. The majority of WCTs were derived from males (72.0%). The SWCT group included more events derived from females than the VT group (SWCT 36.9% vs. VT 17.8%, p ⁇ 0.001). The average age of the VT group was 5.4 years younger than the SWCT group.
  • the SWCT group included more ECG pairs with baseline bundle branch block (BBB) (SWCT 65.5% vs. VT 18.1%, p ⁇ 0.001).
  • the VT group included more ECG pairs with baseline ventricular pacing (VT 43.1% vs. SWCT 6.0%, p ⁇ 0.001).
  • the amplitude based WCT Formula diagnostic performance including (1) WCT QRS duration (ms), (2) frontal PAC (%) and (3) horizontal PAC (%) demonstrated favorable VT and SWCT differentiation (AUC of 0.96) using the derivation cohort (collection of paired WCT and baseline ECGs).
  • Table 4 shows the WCT event characteristics of the validation cohort, which consisted of 123 VTs and 190 SWCTs from 228 patients.
  • the majority of clinical diagnoses were established by heart rhythm cardiologists or non- heart rhythm cardiologists (85.3%).
  • the VT group had comparatively more clinical diagnoses established by heart rhythm cardiologists than the SWCT group (VT 87.8% vs. SWCT 43.7%, p ⁇ 0.001).
  • the SWCT group had a substantially higher percentage of clinical diagnoses established by non-cardiologists (SWCT 22.6% vs. VT 2.4%, p ⁇ 0.001).
  • the validation cohort included comparatively more WCTs with definitive or probable interpretive diagnoses coded by the ECG laboratory than the derivation cohort (98.1% vs. 91.2%, p ⁇ 0.001). Median time separation between the WCT event and subsequent baseline ECG was 4.7 hours. Most baseline ECGs were acquired within 24 hours of the WCT event (70.9%). Most clinical WCT diagnoses were not supplemented by the findings of an electrophysiology procedure (69.3%).
  • Table 5 shows the clinical characteristics of the validation cohort.
  • the majority of WCTs were derived from males (74.8%).
  • the SWCT group included more events derived from females than the VT group (SWCT 32.1% vs. VT 14.6%, p ⁇ 0.001).
  • the average age of the VT group was 4.4 years younger than the SWCT group.
  • the SWCT group included more ECG pairs with baseline BBB (SWCT 68.4% vs. VT 12.2%, p ⁇ 0.001).
  • the VT group included more ECG pairs with baseline ventricular pacing (VT 34.2% vs. SWCT 5.3%, p ⁇ 0.001).
  • FIGURES 16A and 16B histograms demonstrating the distribution of clinical SWCT and VT for the validation cohort according to the amplitude based WCT Formula diagnostic performance at VT probability estimates (0.000% - 99.999%) are shown in accordance with one embodiment of the present invention.
  • VT probability bins on the x-axis are arranged by 5.0% increments.
  • Most SWCTs (72.1%) were categorized as having low VT probability ( ⁇ 10.0%) with a compatible negative predictive value (97.2%).
  • Table 6 shows the VT probability partitions in accordance with one embodiment of the present invention.
  • This version of the WCT Formula demonstrated favorable diagnostic characteristics across a wide variety of VT probability partitions.
  • the amplitude based WCT Formula’s diagnoses agreed strongly with those provided by our institution’s clinical diagnosis and ECG laboratory interpretation practices. Remarkably, despite the ECG laboratory’s presumably strong influence on patients’ final clinical diagnosis, the amplitude based WCT Formula was able to“match” the ECG laboratory’s agreement with clinical diagnosis.
  • the amplitude based WCT Formula’s 50% VT probability partition did not match the exceptional performance originally ascribed to the Brugada algorithm (accuracy 98.0%; sensitivity 98.7%; specificity 96.5%) or Lead II R-wave to peak time (RWPT) criterion (sensitivity 93.2%; specificity 99.3%) (6, 14).
  • RWPT Lead II R-wave to peak time
  • the amplitude based WCT Formula’s 50% VT probability cut-point appears to be less sensitive (lead aVR 96.5% vs. WCT Formula 89.4%) but more specific (lead aVR 75.0% vs. WCT Formula 93.7%).
  • the principal difference between the amplitude based WCT Formula and other established ECG interpretation methods is that it does not require manual ECG interpretation.
  • the WCT Formula was designed to be automatically implemented by modern-day ECG interpretation software. Consequently, these methods escape the conventional challenges concerning provider recall (e.g.,“What is the first step of the Brugada algorithm again?”), subjective interpretation (e.g.,“Are those dissociated p waves?”), interobserver disagreement (21, 23, 26, 27, 30), and precise manual measurement (e.g., Vi/Vt of Vereckei’s aVR algorithm) characteristically present among manual interpretation strategies (1-15).
  • the WCT Formula provides an automatic and reliable means to differentiate WCTs irrespective of the user’s ECG interpretation abilities.
  • the WCT Formulas can help protect against or supersede faulty diagnoses reached by providers who incorrectly apply or fail to utilize manual interpretation methods.
  • Every proposed WCT differentiation criteria or algorithm is to help providers accurately differentiate WCTs.
  • the preferred strategy utilized by most methods is an“absolute” rhythm classification (VT vs. SWCT) according to the presence or absence of select differentiation criteria (6, 8, 9, 11-14). While this approach is meant to lead clinicians to the correct WCT diagnosis, it often leaves providers unaware of the likelihood that their diagnosis is actually correct. This is because the published diagnostic sensitivity and specificity of the various ECG interpretation methods are usually not immediately available or remembered.
  • Another drawback conventional WCT differentiation strategies is that they tend to overlook the predictive contributions of other relevant criteria found (or not found) on a patient’s ECG.
  • the amplitude based WCT Formula was designed to simultaneously evaluate and precisely“weigh” multiple coexistent WCT predictors to provide an automatic estimation of VT probability.
  • the amplitude based WCT Formula is able to deliver to its users an accurate and timely VT probability estimation to help them commit to or reconsider VT or SWCT diagnoses.
  • the WCT Formula’s logistic regression structure can allow the incorporation of other ECG measurements or calculations that may help to differentiate WCTs.
  • “actual” VTs may be erroneously classified as SWCT if they demonstrate narrow QRS durations (e.g. fascicular VT) and/or very similar mean electrical vectors compared to the baseline ECG (e.g. bundle branch re-entry).
  • narrow QRS durations e.g. fascicular VT
  • very similar mean electrical vectors compared to the baseline ECG (e.g. bundle branch re-entry).
  • examples were observed where the amplitude based WCT Formula“missed VTs” with narrower QRS durations and/or similar QRS configurations compared to the baseline ECG (FIGURE 19A).
  • the amplitude based WCT Formula may erroneously classify“actual” SWCTs as VT if they express wider QRS durations (e.g.
  • Each wave has amplitude denoted as PA, q (QS not shown), r/R (r’/R’ not shown), or s/S (s’/S’ not shown) and TA.
  • the QT interval 116 is the time interval extending from the onset of the QRS complex waveform 102 to the end of the T wave 114.
  • the QRS complex 102 is divided into positive (+) time-voltage areas (TVA) 118 and negative (-) TVAs 120.
  • the positive (+) TVAs 118 are the TVAs of the vertical QRS complex deflections above the isoelectric baseline 122, namely the TVAs of r/R wave (and r’/R’ not shown), wave 2002.
  • the negative (-) TVAs 120 are the TVAs of the vertical QRS complex deflections below the isoelectric baseline 122, namely the TVAs of q (or QS wave not shown) 2004, and s/S (and s’/S’ not shown) wave 2006.
  • Computerized ECG interpretation software such as the MUSE software provided by GE Healthcare, automatically measures QRS complex waveform 102 attributes, namely q or QS, r/R, s/S, r’/R’, s’/S’ durations (ms), amplitudes (mV), and time-voltage areas (mV ⁇ ms). Note that standard annotation of QRS complex waveforms of small QRS waveforms are in lower case and large QRS waveforms are in upper case.
  • FIGURES 21A-21B graphic depictions of select ECG lead combinations utilized by the frontal (aVR, aVL, aVF)(FIGURE 5A) and horizontal (V1, inverse V4, V6)(FIGURE 5B) percent time-voltage area change (PTVAC) formulas in accordance with one embodiment of the present invention are shown.
  • the QRS time- voltage area change ( ⁇ ) that manifests between the baseline and WCT ECGs at these selected leads is the foundation for each PTVAC calculation.
  • the absolute QRS time-voltage area changes ( ⁇ ’s) that manifest in lead V4 are mathematically equivalent to its planar opposite: inverse V4.
  • FIGURE 22 depicts a schematic representation of the resultant QRS time-voltage area changes that manifest between a patient’s baseline and WCT ECG.
  • the time-voltage area based WCT Formula incorporates the strong independent WCT predictors including (1) WCT QRS duration (ms), (2) frontal PTVAC (%) and (3) horizontal PTVAC (%).
  • the predictive contribution of each WCT predictor is concomitantly“weighed” according to their influence on the binary outcome (VT vs. SWCT) to render a exact VT probability estimation.
  • the time-voltage area based WCT Formula estimates higher VT probabilities for ECG pairs demonstrating greater WCT QRS durations, frontal PTVAC and/or horizontal PTVAC.
  • the time-voltage area based WCT Formula estimates lower VT probability for ECG pairs with smaller WCT QRS durations, frontal PTVAC and/or horizontal PTVAC.
  • the time-voltage area based WCT Formula is a multivariate logistic regression model that allows (1) delivery of precise VT probability predictions and (2) later inclusion of other well-established, enhanced and/or newly formulated WCT predictors.
  • Other“machine learning” or artificial intelligence prediction methods e.g., artificial neural networks, support vector machines, Random Forests, etc.
  • a step-wise decision-tree approach to diagnosis was avoided because of its tendency to prematurely commit to WCT diagnoses without considering the predictive strengths of other relevant predictors.
  • the time-voltage area based WCT Formula is a logistic regression formula that uses select independent WCT predictors (WCT QRS duration (ms), frontal PTVAC (%) and horizontal PTVAC (%)) to render a precise prediction of VT probability (%).
  • WCT QRS duration ms
  • frontal PTVAC %
  • horizontal PTVAC %
  • VT probability %
  • Each WCT predictor (X x ) was assigned beta coefficients (b x ) according to their influence on the binary outcome (VT vs. non-VT).
  • The“constant” term (B 0 ) represents the y-intercept of the least squares regression line.
  • Discrete measured or calculated WCT predictor values derived from paired baseline and WCT ECGs are incorporated into the time-voltage area based WCT Formula to calculate VT probability (P VT ).
  • a calculation series is used to quantify the degree of QRS time-voltage area change that manifests between the baseline ECG and WCT event by converting raw ECG measurements into the frontal and horizontal PTVAC.
  • the measured time-voltage areas (mV ⁇ ms) of QRS waveforms above (+) (r/R and r’/R’) and below (-) (q/QS, s/S, and s’/S’) the isoelectric baseline from select frontal (aVR, aVL, aVF) and horizontal (V1, V4, V6) ECG leads were used to derive each calculation.
  • Baseline Time-Voltage Area (BTVA), Absolute Time-Voltage Area Change (ATVAC) and Percent Time-Voltage Area Change (PTVAC) were calculated for both the frontal and horizontal ECG planes.
  • LeadX denotes V1, V4, V6 (horizontal plane) or aVL, aVR, aVF (frontal plane).
  • TVANC and TVAPC equations exhibit an“absolute” mathematical annotation (e.g. ⁇ equation’s contents ⁇ ).
  • Absolute Time-Voltage Area Change represents the absolute summative difference in QRS time-voltage area between the WCT and baseline ECG.
  • Baseline Time-Voltage Area represents the total sum time-voltage area of (+) and (-) QRS waveforms in the baseline ECG.
  • PTVAC Percent Time-Voltage Area Change
  • the time-voltage area based WCT Formula is a binary outcome logistic regression formula that uses select independent WCT predictors: (1) WCT duration (ms), (2) frontal PTVAC (%) and (3) horizontal Each WCT predictor (X x ) was assigned beta coefficients (b x ) according to their influence on the binary outcome (VT vs. non-VT).
  • The“constant” term (B 0 ) represents the y-intercept of the least squares regression line.
  • Discrete measured or calculated WCT predictor values derived from paired baseline and WCT ECGs are incorporated into the time-voltage area based WCT Formula to calculate VT probability (P).
  • ⁇ ⁇ is the independent WCT predictor n
  • FIGURE 23C A diagram showing the amplitude based WCT Formula’s logistic regression structure is shown in FIGURE 23C.
  • FIGURES 24A-24B are box-plots demonstrating the median and proportional distribution of frontal PTVAC (%) (FIGURE 24A) and horizontal PTVAC (%) (FIGURE 24B) for VT and SWCT groups in accordance with one embodiment of the present invention.
  • FIGURES 25A-25B are graphs depicting the frontal PTVAC (%) (FIGURE 25A) and horizontal PTVAC (%) (FIGURE 25B) in accordance with one embodiment of the present invention.
  • FIGURE 26 is a graph depicting the time-voltage area based WCT Formula diagnostic performance (AUC of 0.95) in accordance with one embodiment of the present invention.
  • FIGURES 27A and 27B are histograms demonstrating the distribution of clinical VT probabilities according to the time-voltage area based WCT Formula diagnostic performance at VT probability estimates (0.000%-99.999%).
  • the 641 paired WCT and baseline ECGs include both the validation and derivation cohorts.
  • the following discussion refers to both the amplitude and time-voltage area versions of the WCT Formula.
  • the WCT Formulas rely upon the presumed accuracy of ECG software measurements. Moreover, the WCT Formulas require the simultaneous evaluation of the WCT and baseline ECG. Before the technological advances of ECG interpretation software and electronic databank storage systems, the automatic application of sophisticated computer algorithms using data from multiple ECGs was not feasible. Fortunately, contemporary ECG interpretation software is now able to simultaneously record, store and integrate data from multiple ECGs occurring before and after WCT events. Although the WCT Formulas’ derivation and evaluation used only subsequent baseline ECGs, its performance is expected to be similar if applied baseline ECGs preceding the WCT event. For clinical situations where WCT patients present without previously recorded ECGs, providers will need to rely upon conventional ECG interpretation methods until they obtain the patient’s baseline ECG.
  • the WCT Formulas were derived from paired WCT and baseline ECGs acquired from clinical practice. Included WCTs did not require electrophysiology testing for further diagnostic confirmation. Although this selection strategy helps to avoid selection biases, it does not“guarantee” the accuracy of WCT diagnoses established by the ECG laboratory and clinicians. Nor does it allow a more comprehensive understanding of the strengths and weaknesses of both WCT Formulas that would be accomplished with electrophysiology testing.
  • the WCT Formulas were derived and evaluated using paired WCT and baseline ECGs separated by varying, sometimes lengthy, time intervals. As a consequence, deviations in ECG electrode placement and/or major changes to the patient’s baseline ECG (e.g. new ventricular pacing following AICD implantation) may have influenced study results.
  • baseline ECG e.g. new ventricular pacing following AICD implantation
  • the WCT formulas can be used on not only for 12-lead ECGs, but for any extended heart rhythm monitoring devices such as continuous ECG telemetry monitors, stress testing systems, extended monitoring devices (e.g., Holter monitors, etc.), smartphone-enabled ECG medical devices, cardioverter-defibrillator therapy devices, such as wearable cardioverter defibrillators (e.g., Zoll Life Vest), subcutaneous implantable cardioverter defibrillators (S-ICD) (e.g., Emblem S-ICD by Boston Scientific), intracardiac or transvenous pacemaker devices, automated external defibrillators (AED) (e.g., HeartStart OnSite AED by Phillips), and conventional automatic implantable cardioverter defibrillators (AICD).
  • wearable cardioverter defibrillators e.g., Zoll Life Vest
  • S-ICD subcutaneous implantable cardioverter defibrillators
  • AED automated external defibri
  • Every WCT differentiation criteria or algorithm is to help providers successfully differentiate WCTs.
  • This invention provides examples of how modern-day ECG interpretation software could be used to help providers successfully differentiate VT and SWCT.
  • This alternative approach to diagnosis has the natural advantage of automatically delivering precise estimations of VT probability to clinicians irrespective of their ECG interpretation abilities.
  • automated methods like the amplitude based and time-voltage area based WCT Formulas, are particularly well-suited to help providers with less ECG interpretation experience and/or unrelated clinical expertise provide accurate and timely WCT diagnoses.
  • the incorporation of the present invention into computerized ECG interpretation software systems will supplement current diagnostic strategies so to improve the quality of care provided to patients with WCT.
  • the WCT Formulas’ principles could be applied by diagnostic ECG interpretation software to predict VT.
  • the present invention is not limited to the WCT or PAC or PTVAC formulas.
  • This new electrophysiological principle degree of QRS complex change in amplitude or time-voltage area between the WCT and baseline ECG helps distinguish VT and SWCT
  • ECG interpretation software can be utilized by ECG interpretation software to render precise and accurate predictions of VT or SWCT.
  • VCG-VT Model a Random Forests model
  • VCG-VT Model a Random Forests model
  • VCG-VT Model a Random Forests model
  • a derivation cohort comprised of 450 WCT (199 VT, 251 SWCT) and baseline ECG pairs was used to train a VCG-VT Model comprised of WCT QRS duration (ms), X-lead percent QRS amplitude change (%), Y-lead percent QRS amplitude change (%), and Z-lead percent QRS amplitude change (%).
  • VCG-VT Model implementation on the testing cohort of 150 WCT (73 VT, 77 SWCT) and baseline ECG pairs resulted in an overall AUC, accuracy, sensitivity, and specificity of 0.97 (CI 0.94 - 0.99), 91.3% (CI 85.6% - 95.3%), 93.2% (CI 84.7% - 97.7%), and 89.6% (CI 80.6% - 95.4%), respectively as shown in FIGURE 28.
  • the WCT Formula’s electrophysiological principles may be applied to a wide variety of ECG, EMG, VCG and/or mathematically-synthesized VCG analysis platforms beyond the diagnostic 12-lead ECG, including continuous ECG telemetry monitors, stress testing systems, extended monitoring devices (e.g., Holter monitors, etc.), smartphone-enabled ECG medical devices, cardioverter-defibrillator therapy devices, such as wearable cardioverter defibrillators (e.g., Zoll Life Vest), subcutaneous implantable cardioverter defibrillators (ICD) (e.g., Emblem S-ICD by Boston Scientific), intracardiac or transvenouspacemaker devices, automated external defibrillators (AED) (e.g., HeartStart OnSite AED by Phillips), and conventional automatic implantable cardioverter defibrillators (AICD).
  • wearable cardioverter defibrillators e.g., Zoll Life Vest
  • ICD subcutaneous implantable
  • Measurements and calculations of EMG signals recorded from intracardiac (e.g. right ventricular AICD coil) and/or extracardiac electrodes (e.g. AICD generator housing) may also be used to established the degree (or percentage) of change in amplitude or time- voltage area between the WCT and baseline ventricular EMGs to help distinguish VT and SWCT.
  • a similar process may be used to determine the source of individual wide complex beats (premature ventricular contraction or supraventricular wide complex beat or ventricular pacing). This discrimination process could be used to determine the need to deliver of device-related therapies, including anti-tachycardia pacing and defibrillator shocks.
  • the apparatus 2900 can be a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, a medical device or any other device capable of performing the functions described herein.
  • the apparatus 2900 includes an input/output interface 2902, a memory 2904, and one or more processors 2906 communicably coupled to the input/output interface 2902 and the memory 2904. Note that the apparatus 2900 may include other components not specifically described herein.
  • the memory 2904 can be local, remote or distributed.
  • the one or more processors 2906 can be local, remote or distributed.
  • the input/output interface 2902 can be any mechanism for facilitating the input and/or output of information (e.g., web-based interface, touchscreen, keyboard, mouse, display, printer, etc.) Moreover, the input/output interface 2902 can be a remote device communicably coupled to the one or more processors 2906 via one or more communication links 2908 (e.g., network(s), cable(s), wireless, satellite, etc.). The one or more communication links 2908 can communicably couple the apparatus 2900 to other devices 2910 (e.g., databases, remote devices, hospitals, doctors, researchers, patients, etc.).
  • devices 2910 e.g., databases, remote devices, hospitals, doctors, researchers, patients, etc.
  • the one or more processors 2906 receive one or more wide complex heart beat waveform amplitudes and/or time-voltage areas, and one or more baseline heart beat waveform amplitudes and/or time-voltage areas via the input/output interface 2902 or the memory 2904, determine a signal change between the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas, and provide the signal change via the input/output interface 2902, wherein the signal change provides an indication whether the wide complex heart beat(s) is from a ventricular source or a supraventricular source.
  • the delivery of a signal change, such as % VT probability, to clinicians provides an invaluable diagnostic tool that allows them to use their clinical judgement as how to manage the patient.
  • the one or more processors 2906 provide the signal change via the input/output interface 2902 by automatically determining a wide complex heart beat classification for the wide complex heart beat(s) by comparing the signal change to a predetermined value using the one or more processors, and providing the wide complex heart beat classification via the input/output interface 2902.
  • FIGURE 30 a flow chart of a computerized method 3000 of automatically classifying a wide complex heart beat(s) is shown.
  • a computing device having an input/output interface, one or more processors and a memory is provided in block 3002.
  • One or more wide complex heart beat waveform amplitudes and/or time-voltage areas, and one or more baseline heart beat waveform amplitudes and/or time-voltage areas are received via the input/output interface or the memory in block 3004.
  • a signal change between the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas is determined using the one or more processors in block 3006.
  • the signal change is provided via the input/output interface in block 3008, wherein the signal change provides an indication whether the wide complex heart beat(s) is from a ventricular source or a supraventricular source.
  • a wide complex heart beat classification for the wide complex heart beat(s) is automatically determined by comparing the signal change to a predetermined value in block 3010, wherein the wide complex heart beat classification comprises the ventricular source or the supraventricular sourec.
  • the wide complex heart beat classification is provided via the input/output interface in block 3012.
  • the signal change can be concomitantly“weighted” with other predictors of VT, SWCT, ventricular pacing, supraventricular premature contraction or ventricular premature contraction.
  • the method can be implemented using a non-transitory computer readable medium that when executed causes the one or more processors to perform the method.
  • the signal change further provides the indication whether the wide complex heart beat(s) is due to ventricular pacing or premature ventricular contractions.
  • the wide complex heart beat(s) comprise a wide complex tachycardia (WCT)
  • the ventricular source comprises a ventricular tachycardia (VT)
  • the supraventricular source comprises a supraventricular wide complex tachycardia (SWCT).
  • the signal change comprises a VT probability
  • the wide complex heart beat classification comprises a VT whenever the VT probability is greater than or equal to the predetermined value
  • the wide complex heart beat classification comprises a SWCT whenever the VT probability is less than the predetermined value.
  • the one or more processors select the predetermined value from a range of 0% to 100%.
  • the predetermined value comprises about 1%, 10%, 25%, 50%, 75%, 90% or 99%.
  • the one or more processors provide the signal change by providing a“shock” recommendation signal, a“no shock” recommendation signal, or no signal.
  • the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas are obtained from an electrocardiogram (ECG) QRS signal, a ventricular electrogram (EMG) signal, a vectorcardiogram (VCG) and/or a mathematically-synthesized VCG signal.
  • ECG electrocardiogram
  • EMG ventricular electrogram
  • VCG vectorcardiogram
  • the wide complex heart beat waveform amplitudes and/or time-voltage areas comprise a plurality of measured amplitudes and/or time-voltage areas of a ECG QRS waveform, a EMG waveform, a VCG waveform and/or a mathematically-synthesized VCG waveformabove and below an isoelectric baseline; and the baseline heart beat waveform amplitudes and/or time-voltage areas comprise a plurality of measured amplitudes and/or time-voltage areas of a baseline ECG QRS waveform, a baseline EMG waveform, a baseline VCG waveform and/or a baseline mathematically-synthesized VCG waveform above and below the isoelectric baseline.
  • the one or more processors receive the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas, and one or more baseline heart beat waveform amplitudes and/or time-voltage areas by: receiving a ECG QRS data, a EMG data, a VCG data and/or a mathematically-synthesized VCG data via the input/output interface or the memory; receiving a baseline ECG QRS data, a baseline EMG data, a baseline VCG data, and/or a baseline mathematically-synthesized VCG data via the input/output interface or the memory; determining the one or more waveform amplitudes and/or time-voltage areas from the ECG QRS data, the EMG data, the VCG data, and the mathematically-synthesized VCG data; and determining the one or more baseline waveform amplitudes and/or time-voltage areas from the baseline ECG QRS data, the baseline EMG data, the baseline VCG data,
  • the ECG QRS data, the EMG data, the VCG data and/or the mathematically- synthesized VCG data is generated or recorded before or after the baseline ECG QRS data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically- synthesized VCG data.
  • the ECG QRS data, the EMG data, the VCG data, and/or the mathematically-synthesized VCG data is generated or recorded after the baseline ECG QRS data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically-synthesized VCG data and determining the signal change.
  • the ECG QRS data, the EMG data, the VCG data, and/or the mathematically-synthesized VCG data and the baseline ECG QRS data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically-synthesized VCG data are generated or recorded using one or more sensors or devices.
  • the one or more sensors or devices comprise a 12-lead ECG device, a continuous ECG telemetry monitor, a stress testing system, an extended monitoring device, a smartphone-enabled ECG medical device, a cardioverter-defibrillator therapy device, a subcutaneous implantable cardioverter defibrillators (S-ICD), a pacemaker, an automated external defibrillators (AED), or an automatic implantable cardioverter defibrillator (AICD).
  • the input/output interface, the memory and the one or more processors are integrated into the one or more sensors or devices; or the one or more sensors or devices are integrated into a computing device comprising the input/output interface, the memory and the one or more processors.
  • the one or more processors determine the signal change between the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas by: receiving a wide complex heart beat waveform duration via the input/output interface or the memory; determining a percent amplitude change (PAC) based on the wide complex heart beat waveform amplitudes and the baseline heart beat waveform amplitudes, and/or a percent time-voltage area change (PTVAC) based on the wide complex heart beat waveform time-voltage areas and the baseline heart beat waveform time-voltage areas; determining a classification probability based on the wide complex heart beat waveform duration, and the PAC and/or the PTVAC; and wherein the signal change comprises the classification probability, and the classification probability comprises a VT probability, a SWCT probability, or a ventricular pacing probability.
  • PAC percent amplitude change
  • PTVAC percent time-voltage area change
  • determining the classification probability is further determined based one or more additional classification predictors.
  • the PAC comprises a frontal PAC and a horizontal PAC
  • the PTVAC comprises a frontal PTVAC and a horizontal PTVAC.
  • the one or more processors determine the signal change between the wide complex heart beat waveform amplitudes and/or time-voltage areas and the baseline heart beat waveform amplitudes and/or time-voltage areas by: receiving a WCT QRS duration via the input/output interface or the memory; the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas comprise one or more frontal plane WCT positive waveform amplitudes and/or time-voltage areas, one or more horizontal plane WCT positive waveform amplitudes and/or time-voltage areas, one or more frontal plane WCT negative waveform amplitudes and/or time-voltage areas, and one or more horizontal plane WCT negative waveform amplitudes and/or time-voltage areas; the one or more the baseline heart beat waveform amplitudes and/or time-voltage areas comprise one or more frontal plane baseline positive waveform amplitudes and/or time-voltage areas, one or more horizontal plane baseline positive waveform amplitudes and/or time-voltage
  • the statistical or machine learning process comprises a linear regression algorithm, a logistic regression model, a linear discriminate analysis algorithm, a Naive Bayes algorithm, a computational model using artificial neural networks, a computational model based on classification or regression trees, a k-nearest neighbors based model, a support vector machine based model, a boosting algorithm, or an ensemble machine learning algorithm.
  • the frontal PAC is determined by
  • the input/output interface comprises a remote device, and the remote device is communicably coupled to the one or more processors via one or more networks.
  • the one or more processors provide a recommendation to select or exclude a therapy, medication, diagnostic testing or referral for a patient based on the signal change.
  • apparatus comprises a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, or a medical device.
  • the following types of embodiments of the present invention provide a WCT differentiation method that is based in whole or in part on a VT prediction model.
  • This simplified means of WCT differentiation operates solely on computerized measurements routinely displayed on 12-lead ECG paper recordings.
  • a logistic regression prediction model i.e., VT Prediction Model
  • VT Prediction Model a logistic regression model composed of computerized measurements and calculations derived from paired WCT and baseline ECGs was developed and validated.
  • a logistic regression model i.e. VT Prediction Model
  • VT Prediction Model was derived from 601 paired WCT and subsequent baseline ECGs.
  • the VT Prediction Model was tested using a separate validation cohort of 241 paired WCT and baseline ECGs. Patient data acquisition and analysis was approved by the Mayo Clinic Institutional Review Board.
  • Electrocardiograms were standard, 12-lead recordings (paper speed: 25 mm/s, voltage calibration: 10 mm/mV) identified within our institution’s centralized MUSE ECG data archives (GE Healthcare; Milwaukee, WI).
  • Wide complex tachycardias were defined as ECGs fulfilling WCT criteria (QRS duration 3 120 ms; heart rate 3 100 bpm) plus a formal ECG laboratory interpretation of (1)“ventricular tachycardia,” (2)“supraventricular tachycardia,” or (3)“wide complex tachycardia.”
  • Baseline ECGs were defined as being either the first subsequent ECG (i.e., for the derivation cohort) or most proximate ECG (i.e., for the validation cohort) not meeting WCT criteria.
  • WCTs with a paired baseline ECG and definite clinical diagnosis established by the patient’s overseeing physician were analyzed.
  • Polymorphic VTs and SWCTs with varying atrioventricular conduction were not evaluated.
  • Abbreviated WCTs e.g. non-sustained VT
  • a dominant baseline ECG rhythm e.g. normal sinus rhythm
  • Paired ECGs demonstrating erroneous computerized ECG measurements due to ECG artifact were excluded.
  • the derivation cohort consisted of 601 paired WCT (273 VT, 328 SWCT) and baseline ECGs from 421 patients presenting to Mayo Clinic Rochester and/or Mayo Clinic Health System of South Eastern Minnesota (September 2011 to November 2016). Clinical and electrocardiographic data of the derivation cohort was previously examined in a separate analysis (13,14).
  • the validation cohort comprised 241 WCT (97 VT, 144 SWCT) and baseline ECG pairs from 177 patients presenting to the entire Mayo Clinic enterprise (January 2018 to December 2018) – including three tertiary medical centers (Rochester, Minnesota; Jacksonville, Florida; and Phoenix/Scottsdale, Arizona) and other affliated patient care locations (e.g., surrounding community hospitals and outpatient clinics).
  • FIGURE 31 shows a flow diagram of validation cohort selection. Sixty-five out of 306 consecutive WCTs were excluded. Forty-two of those excluded were abbreviated WCTs occurring within a dominant baseline heart rhythm. Thirteen WCTs did not have an established clinical diagnosis. Seven ECG pairs were disqualified due to due excessive ventricular assist device artifact. Two WCTs did not have a corresponding baseline ECG. One WCT was recorded using erroneous ECG lead placements (i.e. limb lead reversal). [00202] Clinical diagnoses (VT or SWCT) were established by the patient’s overseeing physician.
  • Physicians responsible for clinical diagnoses were classified according to their level of expertise: (1) heart rhythm cardiologist, (2) non-heart rhythm cardiologist and (3) non-cardiologist.
  • The“most expert” overseeing physician (heart rhythm cardiologist > non-heart rhythm cardiologist > non-cardiologist) determined the patient’s final clinical diagnosis. All overseeing physicians had access to interpretive diagnoses provided by the ECG laboratory.
  • Formal ECG laboratory interpration was provided by supervising physician interpreters according to the established site-specific practices of various patient care locations across the Mayo Clinic enterprise. Six heart rhythm cardiologists and 12 non-heart rhythm cardiologists were responsible for ECG interpretations of WCTs comprising the derivation cohort. Eleven heart rhythm cardiologists and 18 non-heart rhythm cardiologists were responsible for ECG interpretations of WCTs comprising the validation cohort.
  • the ECG interpretation strategy(s) utilized to differentiate WCTs was determined by the supervising interpreter.
  • Supervising interpreters had access to patients’ electronic medical record and archived ECGs at the time of ECG interpretation.
  • Interpretive diagnoses were semi-qualitatively re-categorized according to diagnostic certainty: (1) definite VT, (2) probable VT, (3) definite SWCT, (4) probable SWCT and (5) undifferentiated.
  • the time separation between paired WCT and baseline ECGs was recorded.
  • Clinical data including patient age, gender, prior myocardial infarction, structural heart disease, baseline bundle branch block (BBB), implanted automatic implantable cardioverter-defibrillator (AICD), and ongoing Vaughan-Williams Class I and III antiarrhythmic drug use were obtained from the electronic medical record. The completion of an electrophysiology procedure supplementing the patient’s clinical diagnosis was recorded.
  • BBB baseline bundle branch block
  • AICD implanted automatic implantable cardioverter-defibrillator
  • Vaughan-Williams Class I and III antiarrhythmic drug use were obtained from the electronic medical record. The completion of an electrophysiology procedure supplementing the patient’s clinical diagnosis was recorded.
  • the VT Prediction Model constituents include data that is obtained from standard computerized ECG measurements, namely WCT QRS duration (ms), QRS duration change (ms), QRS axis change (°), and T axis change (°).
  • QRS duration change is the absolute difference in QRS duration (ms) measurements between paired WCT and baseline ECGs was calculated.
  • QRS axis change is the absolute difference in frontal plane QRS axis (°) between paired WCT and baseline ECGs was calculated.
  • the magnitude of QRS axis change ranged from 0° (i.e. no axis shift) to 180° (i.e. complete axis shift to the straight angle opposite).
  • T axis change is the absolute difference in frontal plane T axis (°) between paired WCT and baseline ECGs was calculated.
  • the magnitude of T axis change ranged from 0° (i.e. no axis shift) to 180° (i.e. complete axis shift to the straight angle opposite).
  • frontal plane axes were converted into a different mapping rubric for directionality which utilizes 1° - 360° instead of ECG paper recordings description of axis (i.e. negative axis numbers (i.e. -1° through– 90°) were changed to positive axis numbers (i.e.359° through 270°) , respectively).
  • absolute values were used in the analysis, non-absolute values can be used for various prediction models (e.g. artificial neural networks).
  • the VT Prediction Model integrates computerized ECG measurements and basic mathematical computations derived from paired WCT and baseline ECGs to generate an estimation of VT probability (0.000% - 99.999%).
  • the logistic regression structure of the VT Prediction Model is outlined below:
  • Independent explanatory variables (X x ) include: WCT QRS duration (ms), QRS duration change (ms), QRS axis change (°), and T axis change (°).
  • Beta coefficients (b x ) were assigned to each VT predictor (X x ) according to their influence on the binary outcome (VT or SWCT).
  • The“constant” term (B 0 ) is the y-intercept of the least squares regression line.
  • Estimated VT probability (P) and the weighted sum predictor (X b ) are derived from integrated VT predictor (X x ) values.
  • the VT probability (P VT ) is determined by:
  • the changes in the R wave axis, T wave axis, and QRS duration can be absolute values or non-absolute values as shown below. These computationally engineering inputs can be fed into the VT Prediction Modes.
  • the VT Prediction Model operates on readily available measurements provided by contemporary ECG interpretation software including QRS duration (ms), corrected QT interval duration (ms), QRS axis (°) and T axis (°).
  • QRS duration e.g., T axis change
  • WCT QRS duration e.g., WCT QRS duration
  • VT predictors may be incorporated into automated prediction models (e.g., logistic regression) that do not depend on clinicians’ manual application of conventional ECG criteria.
  • automated prediction models e.g., logistic regression
  • this approach enables the use of sophisticated“machine learning” methods (e.g., artificial neural networks or support vector machines) more apt to decipher non-linear and non-parametric data relationships.
  • the same basic ECG measurements used by the VT Prediction Model can be used by successive model iterations better able to distinguish VT and SWCT.
  • the VT Prediction Model assigns unambiguous VT probabilities (0.000% - 99.999%) using independent VT predictors simultaneously“weighed” according to their influence on the binary outcome (VT vs. SWCT). Given each VT predictor’s direct relationship with VT probability, the VT Prediction Model estimates higher VT probability for ECG pairs demonstrating greater WCT QRS duration, QRS duration change, QRS axis change and/or T axis change.
  • WCT QRS duration 182 ms
  • QRS duration change 48 ms
  • QRS axis change 134°
  • FIGURE 35 Clinical diagnosis and ECG laboratory interpretation data is summarized in FIGURE 35.
  • the numbers in parentheses are percent (%) of n or standard deviation.
  • the majority (85.7%) of clinical diagnoses were established by heart rhythm or non-heart rhythm cardiologists.
  • Most (94.8%) WCTs were assigned definitive or probable interpretive diagnoses by the ECG laboratory.
  • Just over half (50.6%) of WCTs were derived from patients who underwent an electrophysiology procedure and/or possessed an implanted intracardiac device.
  • FIGURE 36 Patient characteristics of VT and SWCT groups are summarized in FIGURE 36.
  • the numbers in parentheses are percent (%) of n or standard deviation (SD).
  • AICD is automatice implantable cardioverter-defibrillator.
  • LVEF is left ventricular ejection fraction.
  • the VT group included more ECG pairs from patients with coronary artery disease, prior MI, ongoing antiarrhythmic drug use, ischemic cardiomyopathy, non-ischemic cardiomyopathy and implanted AICD.
  • Baseline ECGs with ventricular pacing were more common in the VT group, while baseline BBB was more common in the SWCT group. No SWCTs demonstrated atrioventricular pre-excitation.
  • Paired ECGs in the VT group expressed greater baseline ECG QTc duration, WCT QRS duration, QRS duration change, QRS axis change and T axis change (FIGURE 37).
  • the numbers in parentheses are percent (%) of n or standard deviation (SD).
  • the numbers in parentheses are standard deviation.
  • the VT group demonstrated greater WCT QRS duration and QRS axis change.
  • FIGURES 39A-39D A summary of the median and proportional distribution of the VT Prediction Model’s constituents is shown in FIGURES 39A-39D.
  • the box-plots demonstrate the median and proportional distribution of WCT QRS duration (ms) (FIGURE 39A), WCT QRS duration change (ms) (FIGURE 39B), QRS axis change ( ⁇ ) (FIGURE 39C) and T axis change ( ⁇ ) (FIGURE 39D).
  • the VT Prediction Model composed of WCT QRS duration (ms) (p ⁇ 0.0001), QRS duration change (ms) (p ⁇ 0.0001), QRS axis change ( ⁇ ) (p ⁇ 0.0001) and T axis change ( ⁇ ) (p ⁇ 0.0001) demonstrated favorable WCT differentiation (AUC 0.924; CI 0.903-0.944) for the derivation cohort.
  • FIGURE 40 is a plot showing the receiver operating characteristic curve for the VT Prediction Model (AUC of 0.924; CI 0.903-0.944). Overall, the VT Prediction Model yielded an accuracy, sensitivity and specificity of 84.9%, 80.6% and 88.4%, respectively.
  • the electrocardiographic characteristics of correct and incorrect diagnoses established by the VT Prediction Model for the derivation cohort are summarized in FIGURE 42. Displayed numbers represent mean values. Numbers in parentheses are standard deviation.
  • the erroneous SWCT prediction group comprises clinical VTs assigned a low VT probability ( ⁇ 50%). Fifty-three out of 278 (19.1%) clinical VTs were erroneously categorized as SWCT.
  • erroneous classifications of clinical VT as SWCT exhibited shorter WCT QRS duration and limited changes in QRS duration, QRS axis and T axis between paired baseline and WCT ECGs. Thirty-eight out of 323 (11.8%) clinical SWCTs were erroneously categorized as VT.
  • erroneous classifications of clinical SWCT as VT demonstrated more prolonged WCT QRS duration and greater changes in QRS duration, QRS axis and T axis between paired baseline and WCT ECGs.
  • FIGURE 43 Clinical diagnosis and ECG laboratory interpretation data of the validation cohort is summarized in FIGURE 43.
  • FIGURE 44 Patient characteristics of VT and SWCT groups for the validation cohort are summarized in FIGURE 44.
  • the VT group included more ECG pairs from patients with coronary artery disease, prior MI, ongoing antiarrhythmic drug use, ischemic cardiomyopathy, and an implanted AICD.
  • Baseline ECGs with ventricular pacing were more prevalent in the VT group, whereas baseline BBB was more common in the SWCT group.
  • Four SWCTs demonstrated atrioventricular pre-excitation.
  • the VT Prediction Model yielded effective VT and SWCT differentiation (AUC 0.900; CI 0.862– 0.939) when implemented on the validation cohort (FIGURES 45 and 46).
  • AUC 0.900; CI 0.862– 0.939 effective VT and SWCT differentiation
  • the VT Prediction Model accurately differentiates the large majority of WCTs expected to be encountered in clinical practice. Moreover, despite the presumably persuasive influence that an ECG laboratory diagnosis may have on patients’ final clinical diagnosis the VT Prediction Model agreed with patients’ clinical diagnosis just as well as ECG interpretations provided by supervising cardiologies interpreters.
  • VT Owing to less efficient ventricular depolarization, VT ordinarily expresses longer QRS durations than SWCT. This dissimilarity was originally verified by Wellens and co-workers in 1978 (3), and has since inspired proposed QRS duration cut-offs to determine VT diagnoses: QRS > 140 ms for WCTs with right bundle branch block (BBB) configuration and QRS > 160 ms for WCTs with left BBB configuration (14). Unfortunately, due to considerable QRS duration range overlap, VT and SWCT discrimination cannot be confidently accomplished using only WCT QRS duration cut-offs.
  • BBB bundle branch block
  • SWCTs will demonstrate QRS durations greater than 160 ms, especially when they develop among patients with ongoing anti-arrhythmic drug use, preexisting BBB and/or advanced cardiomyopathy.
  • VTs may periodically demonstrate QRS durations less than 140 ms if they arise from patients without structural heart disease and/or originate within or near the His-Purkinje network.
  • WCT QRS duration affects a graded increase or decrease in VT likelihood– WCT QRS prolongation increases VT probability, whereas shortened WCT QRS durations decrease VT probability.
  • Wide complex tachycardia onset or offset may produce large or small changes in the mean electrical vector of ventricular depolarization.
  • VT may express an expansive variety of mean electrical vectors with differing direction (i.e. mean electrical axis) and/or voltage intensity dissimilar to the respective baseline heart rhythm.
  • SWCTs depolarize the ventricular myocardium is ordinarily confined to the same His-Purkinje network or intracardiac pacing delivery system utilized by the baseline heart rhythm. Only in rare circumstances are SWCTs due to ventricular pre-excitation arising from separate atrioventricular accessory pathways.
  • SWCTs especially those with preexisting aberrancy or ventricular pacing, express similar mean electrical vectors compared their respective baseline ECG.
  • SWCTs with functional aberration typically express more substantial changes in the direction and/or magnitude of the mean electrical vector.
  • functional SWCTs arise from antegrade impulse propagation and ventricular depolarization confined within the His-Purkinje network, they are destined to express a more constrained variety of mean electrical vectors.
  • SWCTs including those due to functional aberrancy, are expected to demonstrate smaller changes to the mean electrical axis than VT. In a similar manner, VT is anticipated to exhibit larger changes in the mean electrical axis.
  • FIGURES 47A-47E depict panels summarizing the expected changes to the mean electrical vector of ventricular depolarization following WCT initiation.
  • Displayed arrows represent mean electrical vectors for ventricular depolarization within the frontal ECG plane.
  • the directional orientation of individual arrows represents the mean electrical axis of ventricular depolarization (i.e., QRS axis).
  • Heavy yellow arrows represent the baseline heart rhythm’s mean electrical vector for ventricular depolarization (the inscribed “R” signifies“R axis” [i.e. QRS axis] displayed on ECG paper recordings).
  • Color-shaded regions represent the expected range of mean electrical vectors after WCT onset.
  • Panel 47A depicts the mean electrical vector of ventricular depolarization for a normal baseline heart rhythm.
  • Panels 47B-47E depict various examples of expected changes in the mean electrical vector of depolarization that occur upon WCT initiation.
  • VT Panel 47B
  • SWCTs arising from BBB (Panel 47E) demonstrate minimal mean electrical vector changes.
  • FIGURES 48A-48E depict panels summarizing expected changes to the mean electrical vector of ventricular repolarization upon WCT initiation. Displayed arrows represent mean electrical vectors for ventricular repolarization in the frontal ECG plane. The directional orientation of individual arrows represents the mean electrical axis of ventricular repolarization (i.e., T axis).
  • Heavy orange arrows represent the baseline heart rhythm’s mean electrical vector for ventricular repolarization (the inscribed“T” signifies“T axis” displayed on ECG paper recordings). Color-shaded regions represent the expected range of mean electrical vectors after WCT onset.
  • Panel 48A depicts the mean electrical vector of ventricular depolarization and repolarization for a normal baseline heart rhythm.
  • Panels 46B– 46E depict various examples of expected changes in the mean electrical vector of repolarization that occur upon WCT initiation.
  • VT Panel 48B
  • SWCTs arising from BBB demonstrate minimal mean electrical vector changes. Therefore, in much the same way as changes in the mean electrical axis of depolarization (i.e. QRS axis change), quantifying the degree of change in the mean electrical axis of ventricular repolarization (i.e. T axis change) provides a means to correctly differentiate WCTs.
  • “actual” VTs may be erroneously classified as SWCT if they demonstrate narrow QRS durations (e.g. fascicular VT), minimal QRS duration changes (e.g., VT arising from a baseline ventricular paced rhythm) and/or similar ventricular depolarization and/or repolarization patterns compared to the baseline ECG (e.g. bundle branch re-entry).
  • narrow QRS durations e.g. fascicular VT
  • minimal QRS duration changes e.g., VT arising from a baseline ventricular paced rhythm
  • similar ventricular depolarization and/or repolarization patterns compared to the baseline ECG (e.g. bundle branch re-entry).
  • erroneous SWCT predictions for clinical VTs that demonstrate narrower QRS durations and/or minimal changes in QRS duration, QRS axis, and/or T axis compared to the baseline ECG.
  • the VT Prediction Model may erroneously classify “actual” SWCTs as VT if they express wider QRS durations (e.g. QRS prolongation due to antiarrhythmic drugs), large QRS duration changes (e.g. functional right or left BBB) and/or pronounced changes in ventricular depolarization and/or repolarization (e.g.
  • the VT Prediction Model is a prototypical example of how to successfully differentiate WCTs without relying upon the manual application of traditional ECG criteria or algorithms.
  • the VT Prediction Model’s implementation merely requires the input of computerized ECG measurements routinely displayed on 12-lead ECG paper recordings: QRS duration, QRS axis (i.e.“R” axis) and T axis.
  • QRS duration i.e.“R” axis
  • T axis i.e.“R” axis
  • three universal ECG measurements displayed on WCT and baseline ECGs– recorded before or after the WCT event– may be readily used to deliver an estimation of VT probability.
  • a similar approach may be performed by successive iterations that use more sophisticated modelling techniques (e.g., artificial neural networks).
  • VT Prediction Model embodiments would be well-suited to provide diagnostic assistance to ECG interpreters– especially those who struggle to accurately differentiate WCTs with manual ECG interpretation methods.
  • online calculators and/or mobile device applications implementing the VT Prediction Model to deliver an unambiguous estimation of VT probability, may help clinicians commit to (or reconsider) VT or SWCT diagnoses reached by other WCT differentiation methods.
  • the VT Prediction Model (and its successive interations) would have a tangible means to deliver clinicians a cognitively meaningful estimation of VT probability.
  • the VT Prediction Model is a prototypical example of how universal computerized ECG measurements may be utilized to create a simplified, user-friendly means to differentiate WCTs.
  • This approach to WCT diagnosis has the natural advantage of providing definite estimations of VT probability irrespective of clinicians’ competency using manually-applied WCT criteria or algorithms.
  • the VT Prediction Model can be applied to any standard 12 lead ECG performed. Thus, those performed in the outpatient office, inpatient setting, on remotely worn devices may utilize the VT Prediciton Model This can also include application to 12 lead ECGs, Holter monitoring, or other wearable devices.
  • the VT Prediction Model can be directly incorporated into ECG interpretation software (e.g., MUSE by GE Healthcare, etc.).
  • QRS duration (ms), R wave axis (°) and T wave (°) can be derived from as few as two leads (e.g., Lead I and III). This feature increases the diagnostic utility and bandwidth of utility of this method in more general settings, such as rural hospitals or in intensive care units where 2-3 leads are commonly used for continuous heart rhythm monitoring and provide an automated means of detection for any care setting.
  • a similar operation may be employed using QRS duration, QRS axis and T wave axis derived from EMG data, a VCG data, and/or a mathematically-synthesized VCG data.
  • comparable models may ultilize an interface that can be downloaded to a patient’s smartphone such as an mobile device application, via a website for clinicians with access to a computer, or on wireless ECG based interpretation systems— i.e., used by ambulance, ICU, cath labs, or ECG hospital monitoring based platforms so that an automated alert is sent to a member of a care team in order to expedite workup and evaluation for a given patient.
  • smartphone such as an mobile device application
  • wireless ECG based interpretation systems i.e., used by ambulance, ICU, cath labs, or ECG hospital monitoring based platforms so that an automated alert is sent to a member of a care team in order to expedite workup and evaluation for a given patient.
  • VT prediction model Although a logistic regression model was used above to demonstrate the accuracy of the VT prediction model, other machine learning models can be used, such as neural networks, Random Forests, K-nearest neighbors, support vector machines, ect. Moreover, additional inputs or parameters can be used to adapt or modify the VT prediction model. For example, there are several permutations of higher order and magnitude that can be more complex and thus provide even more robust data and enhanced accuracy. These can include training data sets which become“smarter” for each patient or collection of patients to offer a refining model– i.e. improvement through learning steps associated from augmented intelligence from a fed-in learning set. This can include but is not limited to several features for VT probability prediction or VT/SWCT classification models.
  • augmented intelligence techniques can provide personalized models via several ECGs with“WCT” versus“normal” ECGs that could correctly inform/notify providers of relapsed VT or SWCT and to allow an even more precise/accurate read out of an ECG in an automated fashion.
  • the VT Prediction model (or its successive iterations or other predictive modelling variants) can be applied by computerized ECG interpretation software systems in multiple permutations, tailored for specific patient populations, and applied in multiple interfaces.
  • the apparatus 2900 can be a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, a medical device or any other device capable of performing the functions described herein.
  • the apparatus 2900 includes an input/output interface 2902, a memory 2904, and one or more processors 2906 communicably coupled to the input/output interface 2902 and the memory 2904. Note that the apparatus 2900 may include other components not specifically described herein.
  • the memory 2904 can be local, remote or distributed.
  • the one or more processors 2906 can be local, remote or distributed.
  • the input/output interface 2902 can be any mechanism for facilitating the input and/or output of information (e.g., web-based interface, touchscreen, keyboard, mouse, display, printer, etc.) Moreover, the input/output interface 2902 can be a remote device communicably coupled to the one or more processors 2906 via one or more communication links 2908 (e.g., network(s), cable(s), wireless, satellite, etc.). The one or more communication links 2908 can communicably couple the apparatus 2900 to other devices 2910 (e.g., databases, remote devices, hospitals, doctors, researchers, patients, etc.).
  • devices 2910 e.g., databases, remote devices, hospitals, doctors, researchers, patients, etc.
  • the one or more processors 2906 receive a wide complex heart beat data comprising at least a wide complex heart beat QRS duration, a wide complex heart beat R wave axis and a wide complex heart beat T wave axis via the input/output interface 2902 or the memory 2904; receive a baseline heart beat data comprising at least a baseline heart beat QRS duration, a baseline heart beat R wave axis, and a baseline heart beat T wave axis via the input/output interface 2902 or the memory 2904; determine a signal change between the wide complex heart beat data and the baseline heart beat data areas; and provide the signal change via the input/output interface 2902, wherein the signal change provides an indication whether the wide complex heart beat(s) is from a ventricular source or a supraventricular source.
  • FIGURE 51 a flow chart of a computerized method 5100 of automatically classifying a wide complex heart beat(s) is shown.
  • a computing device having an input/output interface, one or more processors and a memory is provided in block 5102.
  • a wide complex heart beat data comprising at least a wide complex heart beat QRS duration, a wide complex heart beat R wave axis and a wide complex heart beat T wave axis is received via the input/output interface or the memory in block 5104.
  • a baseline heart beat data comprising at least a baseline heart beat QRS duration, a baseline heart beat R wave axis, and a baseline heart beat T wave axis is received via the input/output interface or the memory in block 5106.
  • a signal change between the wide complex heart beat data and the baseline heart beat data areas is determined using the one or more processors in block 5108.
  • the signal change is provided via the input/output interface in block 5110, wherein the signal change provides an indication whether the wide complex heart beat(s) is from a ventricular source or a supraventricular source.
  • a wide complex heart beat classification for the wide complex heart beat(s) is automatically determined by comparing the signal change to a predetermined value using the one or more processors in block 5112, wherein the wide complex heart beat classification comprises the ventricular source or the supraventricular source.
  • the wide complex heart beat classification is provided via the input/output interface in block 5114.
  • the signal change in ventricular repolarization can be concomitantly“weighted” with other predictors of VT, SWCT or ventricular pacing.
  • the method can be implemented using a non-transitory computer readable medium that when executed causes the one or more processors to perform the method.
  • the signal change in ventricular repolarization further provides the indication whether the wide complex heart beat(s) is due to ventricular pacing or a premature ventricular contraction (PVC).
  • the wide complex heart beat(s) comprise a wide complex tachycardia (WCT)
  • the ventricular source comprises a ventricular tachycardia (VT)
  • the supraventricular source comprises a supraventricular wide complex tachycardia (SWCT).
  • the signal change in ventricular repolarization comprises a VT probability
  • the wide complex heart beat classification comprises a VT whenever the VT probability is greater than or equal to the predetermined value
  • the wide complex heart beat classification comprises a SWCT whenever the VT probability is less than the predetermined value.
  • the one or more processors select the predetermined value from a range of 0% to 100%.
  • the predetermined value comprises about 1%, 10%, 25%, 50%, 75%, 90% or 99%.
  • the one or more processors provide the signal change in ventricular repolarization by providing a “shock” recommendation signal, a“no shock” recommendation signal, or no signal.
  • the wide complex heart beat data and the baseline heart beat waveform data are obtained from an electrocardiogram (ECG) signal, a ventricular electrogram (EMG) signal, a vectorcardiogram (VCG), and/or a mathematically-synthesized VCG signal.
  • ECG electrocardiogram
  • EMG ventricular electrogram
  • VCG vectorcardiogram
  • the one or more processors receive the wide complex heart beat data by receiving a ECG QRS data, a EMG data, a VCG data, and/or a mathematically- synthesized VCG data via the input/output interface or the memory, and determine the wide complex heart beat data from the ECG QRS data, the EMG data, the VCG data and/or the mathematicallly-synthesized VCG data; and receive the baseline heart beat data by receiving a baseline ECG QRS data, a baseline EMG data, a baseline VCG data, and/or a baseline mathematically-synthesized VCG data via the input/output interface or the memory, and determine the baseline heart beat data from the baseline ECG QRS data, the baseline EMG data, the baseline VCG data and/or the baseline mathematically-synthesized VCG data.
  • the ECG data, the EMG data, the VCG data and/or the mathematically- synthesized VCG data is generated or recorded before or after the baseline ECG data, the baseline EMG data, the baseline VCG data and/or the baseline mathematically-synthesized VCG data.
  • the ECG data, the EMG data, the VCG data and/or the mathematically-synthesized VCG data is generated or recorded after the baseline ECG data, the baseline EMG data, the baseline VCG data and/or the baseline mathematically- synthesized VCG data and determining the signal change.
  • the ECG data, the EMG data, the VCG data and/or the mathematically-synthesized VCG data and the baseline ECG data, the baseline EMG data, the baseline VCG data and/or the baseline mathematically-synthesized VCG data are generated or recorded using one or more sensors or devices.
  • the one or more sensors or devices comprise two or more leads of a ECG device, a 12-lead ECG device, a continuous ECG telemetry monitor, a stress testing system, an extended monitoring device, a smartphone-enabled ECG medical device, a cardioverter-defibrillator therapy device, a subcutaneous implantable cardioverter defibrillators (ICD), an intracardiac or transvenous pacemaker device, an automated external defibrillators (AED), or an automatic implantable cardioverter defibrillator (AICD).
  • ICD subcutaneous implantable cardioverter defibrillators
  • AED automated external defibrillators
  • AICD automatic implantable cardioverter defibrillator
  • the input/output interface, the memory and the one or more processors are integrated into the one or more sensors or devices; or the one or more sensors or devices are integrated into a computing device comprising the input/output interface, the memory and the one or more processors.
  • the signal change comprises a classification probability comprising a VT probability, a SWCT probability, ventricular pacing probability or a ventricular pacing probability.
  • the one or more processors determine the classification probability based one or more additional classification predictors.
  • the one or more processors determine the signal change by determining a VT probability using a statistical or machine learning process.
  • the statistical or machine learning process comprises a linear regression algorithm, a logistic regression model, a linear discriminate analysis algorithm, a Naive Bayes algorithm, a computational model using artificial neural networks, a computational model based on classification or regression trees, a k-nearest neighbors based model, a support vector machine based model, a boosting algorithm, or an ensemble machine learning algorithm.
  • the one or more processors determine the signal change by determining a VT probability (P VT ) by:
  • the QRS axis change an absolute or non-absolute value of the wide complex heart beat R wave axis minus the baseline heart beat R wave axis,
  • the T axis change an absolute or non-absolute value of the wide complex heart beat T wave axis minus the baseline heart beat T wave axis,
  • the WCT QRS duration the wide complex heart beat QRS duration
  • the QRS duration change an absolute or non-absolute value of the wide complex heart beat QRS duration minus the baseline heart beat QRS duration.
  • the input/output interface comprises a remote device, and the remote device is communicably coupled to the one or more processors via one or more networks.
  • the one or more processors receive the wide complex heart beat data by monitoring a person using one or more sensors or devices communicably coupled to the input/output interface.
  • the one or more processors provide a recommendation to select or exclude a therapy, medication, diagnostic testing or referral for a patient based on the signal change in ventricular repolarization.
  • the one or more processors send an alert to one or more devices in based on the signal change.
  • the one or more processors count the frequency of various wide complex beats comprising ventricular tachycardia events, supraventricular tachycardia events, singular supraventricular wide complex beats, premature ventricular contraction, right ventricular pacing and/or biventricular pacing.
  • the one or more processors receive multiple sets of the wide complex heart beat data and the baseline heart beat data for a person or group of persons, determine the signal change for each set of the wide complex heart beat data and the baseline heart beat data for the person or the group of persons, and create a VT prediction model for the person or the group of persons using the signal changes.
  • apparatus comprises a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, or a medical device.
  • the following types of embodiments of the present invention provide a WCT differentiation method that is based in whole or in part on an analysis of ventricular repolarization.
  • This alternative approach to diagnosis has the natural advantage of automatically delivering precise estimations of VT probability to clinicians irrespective of their ECG interpretation abilities.
  • automated methods incorporating T wave amplitude based and T wave time-voltage area based parameters are particularly well-suited to help providers with less experience and/or differing clinical expertise provide accurate and timely WCT diagnoses.
  • the incorporation of the present invention into computerized ECG interpretation software systems will not only supplement current diagnostic strategies but may also improve the quality of care provided to patients with WCT.
  • T wave amplitude based and T wave time-voltage area based parameters when added to other known discriminators of VT and SWCT (e.g., percent change in QRS amplitude), the incorporation of T wave amplitude based and T wave time-voltage area based parameters will augment the differentiating capacity of predictive models.
  • electrophysiological principles described herein may be applied to a wide variety of ECG, EMG, VCG, and/or mathematically-synthesized VCG analysis platforms beyond the diagnostic 12-lead ECG, including continuous ECG telemetry monitors, stress testing systems, extended monitoring devices (e.g., Holter monitors, etc.), smartphone-enabled ECG medical devices, cardioverter-defibrillator therapy devices, such as wearable cardioverter defibrillators (e.g., Zoll Life Vest), subcutaneous implantable cardioverter defibrillators (ICD) (e.g., Emblem S-ICD by Boston Scientific), intracardiac or transvenous pacemaker devices, automated external defibrillators (AED) (e.g., HeartStart OnSite AED by Phillips), and conventional automatic implantable cardioverter defibrillators (AICD).
  • wearable cardioverter defibrillators e.g., Zoll Life Vest
  • ICD subcutaneous implantable cardiovert
  • Measurements and calculations of EMG signals recorded from intracardiac (e.g. right ventricular AICD coil) and/or extracardiac electrodes (e.g. AICD generator housing) may also be used to established the degree (or percentage) change in ventricular repolarization. For example, amplitude or time-voltage area changes in ventricular repolarization between the WCT and baseline ventricular EMGs to help distinguish VT and SWCT. This discrimination process could be used to determine the need to deliver of device-related therapies, including anti-tachycardia pacing and defibrillator shocks.
  • the apparatus 2900 can be a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, a medical device or any other device capable of performing the functions described herein.
  • the apparatus 2900 includes an input/output interface 2902, a memory 2904, and one or more processors 2906 communicably coupled to the input/output interface 2902 and the memory 2904. Note that the apparatus 2900 may include other components not specifically described herein.
  • the memory 2904 can be local, remote or distributed.
  • the one or more processors 2906 can be local, remote or distributed.
  • the input/output interface 2902 can be any mechanism for facilitating the input and/or output of information (e.g., web-based interface, touchscreen, keyboard, mouse, display, printer, etc.) Moreover, the input/output interface 2902 can be a remote device communicably coupled to the one or more processors 2906 via one or more communication links 2908 (e.g., network(s), cable(s), wireless, satellite, etc.). The one or more communication links 2908 can communicably couple the apparatus 2900 to other devices 2910 (e.g., databases, remote devices, hospitals, doctors, researchers, patients, etc.).
  • devices 2910 e.g., databases, remote devices, hospitals, doctors, researchers, patients, etc.
  • the one or more processors 2906 receive one or more wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization, and one or more baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization via the input/output interface 2902 or the memory 2904, determine a signal change in ventricular repolarization between the wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization and the baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization, and provide the signal change in ventricular repolarization via the input/output interface 2902, wherein the signal change in ventricular repolarization provides an indication whether the wide complex heart beat(s) is from a ventricular source or a supraventricular source.
  • the delivery of the signal change in ventricular repolarization, such as % VT probability, to clinicians provides an practical diagnostic tool that allows them to use their clinical judgement as how to manage the patient.
  • the one or more processors 2906 provide the signal change in ventricular repolarization via the input/output interface 2902 by automatically determining a wide complex heart beat classification for the wide complex heart beat(s) by comparing the signal change in ventricular repolarization to a predetermined value using the one or more processors, and providing the wide complex heart beat classification via the input/output interface 2902.
  • FIGURE 52 a flow chart of a computerized method 5200 of automatically classifying a wide complex heart beat(s) is shown.
  • a computing device having an input/output interface, one or more processors and a memory is provided in block 5202.
  • One or more wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization, and one or more baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization are received via the input/output interface or the memory in block 5204.
  • a signal change in ventricular repolarization between the wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization and the baseline heart beat waveform amplitudes and/or time- voltage areas of ventricular repolarization is determined using the one or more processors in block 3006.
  • the signal change in ventricular repolarization is provided via the input/output interface in block 5208, wherein the signal change in ventricular repolarization provides an indication whether the wide complex heart beat(s) is from a ventricular source or a supraventricular source.
  • a wide complex heart beat classification for the wide complex heart beat(s) is automatically determined by comparing the signal change in ventricular repolarization to a predetermined value in block 5210, wherein the wide complex heart beat classification comprises the ventricular source or the supraventricular source.
  • the wide complex heart beat classification is provided via the input/output interface in block 5212.
  • the signal change in ventricular repolarization can be concomitantly“weighted” with other predictors of VT, SWCT, premature ventricular contractions or ventricular pacing.
  • the method can be implemented using a non- transitory computer readable medium that when executed causes the one or more processors to perform the method.
  • the signal change in ventricular repolarization further provides the indication whether the wide complex heart beat(s) is due to ventricular pacing or premature ventricular contraction (PVC).
  • the wide complex heart beat(s) comprise a wide complex tachycardia (WCT)
  • the ventricular source comprises a ventricular tachycardia (VT)
  • the supraventricular source comprises a supraventricular wide complex tachycardia (SWCT).
  • the signal change in ventricular repolarization comprises a VT probability
  • the wide complex heart beat classification comprises a VT whenever the VT probability is greater than or equal to the predetermined value
  • the wide complex heart beat classification comprises a SWCT whenever the VT probability is less than the predetermined value.
  • the one or more processors select the predetermined value from a range of 0% to 100%.
  • the predetermined value comprises about 1%, 10%, 25%, 50%, 75%, 90% or 99%.
  • the one or more processors provide the signal change in ventricular repolarization by providing a “shock” recommendation signal, a“no shock” recommendation signal, or no signal.
  • the wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization comprise T wave amplitudes and/or time-voltage areas
  • the baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization comprise baseline T wave amplitudes and/or time-voltage areas
  • the T wave amplitudes and/or time-voltage areas and baseline T wave amplitudes and/or time- voltage areas are obtained from an electrocardiogram (ECG) signal, a ventricular electrogram (EMG) signal, a vectorcardiogram (VCG) signal, and/or mathematically- synthesized vectorcardiogram (VCG) signal.
  • ECG electrocardiogram
  • EMG ventricular electrogram
  • VCG vectorcardiogram
  • VCG mathematically- synthesized vectorcardiogram
  • the wide complex heart beat T wave amplitudes and/or time-voltage areas comprise a plurality of measured T wave amplitudes and/or time-voltage areas of a ECG waveform, a EMG waveform, a VCG waveform, and/or a mathematically-synthesized VCG waveform above and below an isoelectric baseline; and the baseline heart beat T wave amplitudes and/or time-voltage areas comprise a plurality of measured T wave amplitudes and/or time-voltage areas of a baseline ECG waveform, a baseline EMG waveform, a baseline VCG waveform, and/or a mathematically-synthesized VCG waveform above and below the isoelectric baseline.
  • the one or more processors receive the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization, and one or more baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization by: receiving a ECG data, a EMG data, a VCG data, and/or a mathematically-synthesized VCG data via the input/output interface or the memory; receiving a baseline ECG data, a baseline EMG data, a baseline VCG data, and/or a baseline mathematically-synthesized VCG data via the input/output interface or the memory; determining the one or more waveform amplitudes and/or time-voltage areas of ventricular repolarization from the ECG data, the EMG data, the VCG data, and/or the mathematically- synthesized VCG data; and determining the one or more baseline waveform amplitudes and/or time-voltage areas of ventricular repolarization by: receiving a
  • the ECG data, the EMG data, the VCG data, and/or the mathematically-synthesized VCG data is generated or recorded before or after the baseline ECG data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically-synthesized VCG data.
  • the ECG data, the EMG data, the VCG data, and/or the mathematically-synthesized VCG data is generated or recorded after the baseline ECG data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically-synthesized VCG data and determining the signal change in ventricular repolarization.
  • the ECG data, the EMG data, the VCG data, and/or the mathematically-synthesized VCG data and the baseline ECG data, the baseline EMG data, the baseline VCG data, and/or the baseline mathematically-synthesized VCG data are generated or recorded using one or more sensors or devices.
  • the one or more sensors or devices comprise a 12-lead ECG device, a continuous ECG telemetry monitor, a stress testing system, an extended monitoring device, a smartphone-enabled ECG medical device, a cardioverter-defibrillator therapy device, a subcutaneous implantable cardioverter defibrillators (S-ICD), an intracardiac or transvenous pacemaker device, an automated external defibrillators (AED), or an automatic implantable cardioverter defibrillator (AICD).
  • a 12-lead ECG device a continuous ECG telemetry monitor
  • a stress testing system e.g., a smartphone-enabled ECG medical device
  • a cardioverter-defibrillator therapy device e.g., a subcutaneous implantable cardioverter defibrillators (S-ICD), an intracardiac or transvenous pacemaker device, an automated external defibrillators (AED), or an automatic implantable cardioverter def
  • the input/output interface, the memory and the one or more processors are integrated into the one or more sensors or devices; or the one or more sensors or devices are integrated into a computing device comprising the input/output interface, the memory and the one or more processors.
  • the one or more processors determine the signal change in ventricular repolarization between the wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization and the baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization by: receiving a wide complex heart beat waveform duration via the input/output interface or the memory; determining a T-wave percent amplitude change (PAC) based on the wide complex heart beat waveform amplitudes of ventricular repolarization and the baseline heart beat waveform amplitudes of ventricular repolarization, and/or a T-wave percent time-voltage area change (PTVAC) based on the wide complex heart beat waveform time-voltage areas of ventricular repolarization and the baseline heart beat waveform time-voltage areas of ventricular repolarization; determining a classification probability based on the wide complex heart beat waveform duration, and the T-wave PAC and/or the T-wave PTVAC; and wherein the signal
  • determining the classification probability is further determined based one or more additional classification predictors.
  • the T-wave PAC comprises a frontal T-wave PAC and a horizontal T-wave PAC
  • the T- wave PTVAC comprises a frontal T-wave PTVAC and a horizontal T-wave PTVAC.
  • the one or more processors determine the signal change in ventricular repolarization between the wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization and the baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization by: receiving a WCT duration via the input/output interface or the memory; the one or more wide complex heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization comprise one or more frontal plane WCT positive T wave amplitudes and/or time-voltage areas, one or more horizontal plane WCT positive T wave amplitudes and/or time-voltage areas, one or more frontal plane WCT negative T wave amplitudes and/or time-voltage areas, and one or more horizontal plane WCT negative T wave amplitudes and/or time-voltage areas; the one or more the baseline heart beat waveform amplitudes and/or time-voltage areas of ventricular repolarization comprise one or more
  • the statistical or machine learning process comprises a linear regression algorithm, a logistic regression model, a linear discriminate analysis algorithm, a Naive Bayes algorithm, a computational model using artificial neural networks, a computational model based on classification or regression trees, a k-nearest neighbors based model, a support vector machine based model, a boosting algorithm, or an ensemble machine learning algorithm.
  • the input/output interface comprises a remote device, and the remote device is communicably coupled to the one or more processors via one or more networks.
  • the one or more processors provide a recommendation to select or exclude a therapy, medication, diagnostic testing or referral for a patient based on the signal change in ventricular repolarization.
  • apparatus comprises a server computer, a workstation computer, a laptop computer, a mobile communications device, a personal data assistant, or a medical device.
  • the words“comprising” (and any form of comprising, such as“comprise” and“comprises”),“having” (and any form of having, such as“have” and“has”),“including” (and any form of including, such as “includes” and“include”) or“containing” (and any form of containing, such as“contains” and“contain”) are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.
  • “comprising” may be replaced with“consisting essentially of” or“consisting of”.
  • the phrase“consisting essentially of” requires the specified integer(s) or steps as well as those that do not materially affect the character or function of the claimed invention.
  • the term“consisting” is used to indicate the presence of the recited integer (e.g., a feature, an element, a characteristic, a property, a method/process step or a limitation) or group of integers (e.g., feature(s), element(s), characteristic(s), propertie(s), method/process steps or limitation(s)) only.
  • words of approximation such as, without limitation,“about”, “substantial” or“substantially” refers to a condition that when so modified is understood to not necessarily be absolute or perfect but would be considered close enough to those of ordinary skill in the art to warrant designating the condition as being present.
  • the extent to which the description may vary will depend on how great a change can be instituted and still have one of ordinary skilled in the art recognize the modified feature as still having the required characteristics and capabilities of the unmodified feature.
  • a numerical value herein that is modified by a word of approximation such as“about” may vary from the stated value by at least ⁇ 1, 2, 3, 4, 5, 6, 7, 10, 12 or 15%.

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Abstract

L'invention concerne un appareil et procédé informatisé qui distingue avec précision une tachycardie ventriculaire et une tachycardie supraventriculaire sans nécessiter une interprétation ou un calcul manuel d'électrocardiogramme ECG, d'électromyogramme (EMG) et/ou de vectocardiogramme (VCG). L'appareil et procédé informatisé fournit trois types de différenciation de battement complexes larges qui peuvent être mis en oeuvre automatiquement au moyen de données fournies par un logiciel d'interprétation d'ECG, d'EMG et/ou de VCG. Le premier type est basé entièrement ou partiellement sur une formule WCT. Le second type est basé entièrement ou partiellement sur un modèle de prédiction VT. Le troisième type est basé entièrement ou partiellement sur une analyse de repolarisation ventriculaire (par exemple, onde T).
PCT/US2019/046713 2018-06-21 2019-08-15 Appareil et procédé pour distinguer de larges battements cardiaques complexes WO2020014715A2 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022018671A1 (fr) * 2020-07-24 2022-01-27 Heartstarter As Défibrillateur mobile

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US4136690A (en) * 1977-10-31 1979-01-30 Del Mar Avionics Method and apparatus for vector analysis of ECG arrhythmias
US6766190B2 (en) * 2001-10-31 2004-07-20 Medtronic, Inc. Method and apparatus for developing a vectorcardiograph in an implantable medical device
ATE502339T1 (de) * 2008-03-28 2011-04-15 Ela Medical Sa Aktives medizingerät, das verbesserte mittel zur unterscheidung zwischen tachykardien ventrikulären ursprungs und tachykardien supraventrikulären ursprungs enthält

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022018671A1 (fr) * 2020-07-24 2022-01-27 Heartstarter As Défibrillateur mobile

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