Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/026—Measuring blood flow
  • A61B5/029—Measuring blood output from the heart, e.g. minute volume
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/02028—Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/021—Measuring pressure in heart or blood vessels
  • A61B5/02108—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics
  • A61B5/02125—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics of pulse wave propagation time
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/024—Measuring pulse rate or heart rate
  • A61B5/02416—Measuring pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/026—Measuring blood flow
  • A61B5/0295—Measuring blood flow using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
  • A61B5/346—Analysis of electrocardiograms
  • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
  • A61B5/352—Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7235—Details of waveform analysis
  • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
  • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
  • A61B8/06—Measuring blood flow
  • A61B8/065—Measuring blood flow to determine blood output from the heart
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
  • A61B8/44—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device
  • A61B8/4416—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device related to combined acquisition of different diagnostic modalities, e.g. combination of ultrasound and X-ray acquisitions
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
  • A61B8/48—Diagnostic techniques
  • A61B8/488—Diagnostic techniques involving Doppler signals
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
  • A61B8/06—Measuring blood flow
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
  • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
  • A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
  • A61B8/5223—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data for extracting a diagnostic or physiological parameter from medical diagnostic data

Definitions

• This invention relates to an apparatus and method for estimating one or more hemodynamic parameters.
• hemodynamic parameters are important to measure and monitor. These include for example central hemodynamic parameters such as cardiac output (CO), stroke volume (SV), and their variation over time.
• CO cardiac output
• SV stroke volume
• Non-invasive measurement methods exist, including for example use of ultrasound sensing means to measure blood flow through major arteries, for example using Doppler ultrasound.
• Other methods of measuring blood flow are also known such as using blood pressure measurements to indirectly determine blood flow.
• Fig. 1 schematically illustrates blood flow from the heart 12 through the various arterial branches 14a-14n of the circulatory system.
• the volumetric blood output from the heart in each heart beat is the stroke volume (SV), and the volumetric output per minute is the cardiac output (CO).
• the controller is adapted to process the blood flow measurement for the at least one arterial path, and the time difference measure, AT, or parameters derived therefrom, for both the central and peripheral arterial path, with the transfer function to generate the estimate for the at least one hemodynamic parameter.
• the controller is preferably further adapted to generate a data output indicative of the estimate of the at least one hemodynamic parameter.
• This may be a data packet comprising data representative of the estimate, or a series of values thereof derived over a period of time, ready for data export, for example to a datastore or a user interface, or along a network communication channel.
• the method may comprise transmitting the data output to a further module or device, e.g. to a user interface.
• this can be detected as the time of occurrence of the QRS complex in an ECG signal. It may correspond to the time of occurrence of a particular reference point within the QRS complex, such as one of the Q, R, and S peaks (e.g. the R-peak, which is the largest peak). It may correspond to the time of occurrence of the start of the QRS complex.
• ECG to detect this event is not essential and other means such as use of an accelerometer or inductive sensing means or radar sensing means can alternatively be used.
• the obtaining of the time difference measure the obtaining of the time difference measure
• the obtaining of the PTT measurement for each arterial path may comprise: obtaining a PAT measurement for each arterial path, obtaining an estimate of a pre-ejection period (PEP) duration, and determining the PTT measurement for each arterial path by subtracting the PEP duration from the PAT measurement for each arterial path.
• PEP pre-ejection period
• the hemodynamic parameter may be determined based on variation in the time difference values, DT, over time for each arterial path.
• the controller may be configured to obtain a plurality of time difference measurements, DT, for each of the arterial paths, corresponding to different heart cycles, and determine a measure indicative of variation in the DT values over time for each arterial path, and determine the estimate of the hemodynamic parameter based on the variation in DT values.
• the at least one hemodynamic parameter calculated by the controller may include at least one of: cardiac output, stroke volume, and stroke volume variation (SVV).
• Other examples of hemodynamic parameters which could be derived include: blood velocity, stroke volume variation, systolic velocity, diastolic velocity, blood pressure.
• the first, second and third sensor means may correspond to different respective sensor devices, or one or more of the sensor means may correspond to the same sensor device.
• the determining may for example comprise providing the blood flow measurement for the at least one arterial path, and the measures indicative of the time difference, DT, for both the central and peripheral arterial path, or parameters derived therefrom, as inputs to a pre-determined transfer function, and wherein the transfer function is adapted to generate the estimate for the at least one hemodynamic parameter based on the inputs and a pre-determined functional relationship between the inputs and the at least one hemodynamic parameter.
• Fig. 6 schematically illustrates processing workflow according to a further example embodiment
• Embodiments of the present invention are based on the realization that the time taken for blood to travel from the heart along a pre-determined length of an arterial path (in other words, how fast cardiac pulses travel through branches of the vascular tree) is closely dependent upon vascular tone of the arterial path. The two are correlated with one another. In view of this, this time duration can be used as a proxy measure for vascular tone in the arterial path.
• a similar set of inputs is also received for the peripheral arterial path.
• a time difference, AT_peri between these two events can be calculated for the peripheral arterial path.
• a transfer function machine learning and/or statistical methods can for example be applied to a labeled dataset. For example, multi-parametric regression has been used successfully in trials to estimate such a transfer function based on a clinical dataset. Given sufficient data, the transfer function can also be improved using patient metadata, e.g. gender, BMI, and other patient personal information. Other approaches to providing a transfer function may include for instance use of support vector machines or Naive Bayes models.
• Such methods comprise obtaining a training dataset, comprising training input data entries and corresponding training output data entries.
• the training input data entries in this case would be the acquired inputs for the transfer function, i.e. at least the measured DT, PAT, PTT or DRAT values for each of the two arterial paths, and the at least one blood flow measurement. Additional inputs could also be included in some embodiments.
• the training output data entries would correspond to the hemodynamic parameter being sought, e.g. cardiac output, stroke volume, and/or stroke volume variation, et cetera. To build up the training data, the hemodynamic parameter would be measured manually, e.g. using an invasive method, when acquiring the training data entries, so that its accuracy is assured.
• the ejection event in this case may correspond to the point of electrical activation of the heart. It corresponds to the point at the beginning of the pre-ejection period, where the heart pulse first begins.
• the time of arrival of the pulse wave at the pre-determined location along the arterial path may be detected using a PPG sensor in some examples.
• an ultrasound sensing means may be used to detect the arrival of the pulse wave, e.g. an ultrasound transducer unit configured to acquire Doppler ultrasound data at the pre-determined pulse arrival location.
• DT for each arterial path may comprise obtaining a pulse transit time (PTT) measurement for each arterial path.
• the pulse transit time is the time between ejection of the blood into the aorta and the arrival of the corresponding pulse-wave at the downstream measurement location. In other words it is the time between the end of the pre-ejection period (PEP) and the arrival of the pulse wave at the pre-determined location along the arterial path.
• an ultrasound sensing means may further be used, and arranged for example to perform ultrasound sensing at the neck of the subject, for example at a location along the central arterial path (e.g. over the carotid artery).
• This ultrasound sensing means may comprise one or more ultrasound transducers, and a dedicated ultrasound processing unit for processing the acquired ultrasound data.
• the ultrasound data may be Doppler ultrasound data.
• the time of arrival of the pulse wave at the defined location along the central arterial path (“pulse arrival_cen”) may be detected using the ultrasound sensing means.
• Doppler ultrasound data provides an indication of blood flow through the arterial path, and thus arrival of a pulse wave at the given location can be detected by a sudden increase in flow, or the beginning of an upward slope in flow rate.
• Fig. 5 schematically outlines the processing workflow for a second example set of embodiments.
• PEP pre-ejection period
• PTT cen, PTT_peri, and the blood flow measurement for the at least one arterial path are then fed by the controller to the processing algorithm or computation or transfer function 21 and used to derive the estimate of the hemodynamic parameter 24 (e.g. cardiac output or stroke volume).
• the hemodynamic parameter 24 e.g. cardiac output or stroke volume
• the controller may be configured to obtain a plurality of time difference measurements, AT, for each of the arterial paths, corresponding to different heart cycles, and determine a measure indicative of variation in the AT values over time for each arterial path, and determine the estimate of the hemodynamic parameter 24 based on the variation in AT values.
• the controller may continuously or recurrently re-acquire a AT value for each arterial path to thereby monitor the variations in the AT values as a function of time.
• the controller uses values of the increase or decrease in PAT between heart beats as the parameter for calculating the hemodynamic parameter, not the absolute PAT values.
• the performance or accuracy of a generated machine learning model can be assessed by running the model on a test dataset after training, and assessing the error between the output predicted values generated by the model and the actual ground truth values.
• the performance measurements can include a goodness-of-fit of linear regression (R 2 ), a standard error (SE), the t-statistic (tStat) of the estimate, the p-value, the root mean square error (RMSE) obtained from a correlation scatter plot, and/or a coefficient of reproducibility (rpc) obtained from a Bland-Altman plot.
• the transfer function may be adapted to accept as input parameters the PPT cen and PPT_peri values, or a function, e.g. quotient, thereof, in combination with the blood flow measure (see explanation above for further details).
• the transfer function may be adapted to accept as input parameters the APAT cen and APAT_peri values, or a function, e.g. quotient thereof, in combination with the blood flow measure (see explanation above for further details).
• the transfer function may be adapted to accept as input parameters a measure of a pulse arrival time at a defined location along each of a peripheral and arterial path, a measure of a time of heart ejection, , and the measure of blood flow.
• the transfer function may accept a greater number of input parameters, i.e. has a greater number of independent variables, each with a respective weighting.
• Additional input parameters might include for example parameters derived from the blood flow and/or AT measurements, and/or patient metadata such as gender and body mass index.
• the apparatus comprises an ECG sensor arrangement 44 for detecting a heart ejection event.
• the apparatus further comprises at least one PPG sensor 46 for optically coupling to a location along an arterial path of a subject, for detecting time of arrival of a blood pulse wave from the heart at said location.
• controller 22 can be implemented in numerous ways, with software and/or hardware, to perform the various functions required.
• a processor is one example of a controller which employs one or more microprocessors that may be programmed using software (e.g., microcode) to perform the required functions.
• a controller may however be implemented with or without employing a processor, and also may be implemented as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions.
• a single processor or other unit may fulfill the functions of several items recited in the claims.

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• 2020
  • 2020-06-11 EP EP20179433.6A patent/EP3922175A1/en not_active Withdrawn
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