WO2005096170A1 - Analyse d'electrocardiogramme-signal pour prediction de sortie de choc - Google Patents

Analyse d'electrocardiogramme-signal pour prediction de sortie de choc Download PDF

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Publication number
WO2005096170A1
WO2005096170A1 PCT/GB2005/000477 GB2005000477W WO2005096170A1 WO 2005096170 A1 WO2005096170 A1 WO 2005096170A1 GB 2005000477 W GB2005000477 W GB 2005000477W WO 2005096170 A1 WO2005096170 A1 WO 2005096170A1
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Prior art keywords
ecg
scalogram
signal
defibrillation
wavelet
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PCT/GB2005/000477
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English (en)
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Paul Stanley Addison
James Nicholas Watson
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Cardiodigital Limited
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Publication of WO2005096170A1 publication Critical patent/WO2005096170A1/fr

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    • 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
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
    • 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
    • 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
    • A61B5/347—Detecting the frequency distribution of signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/38—Applying electric currents by contact electrodes alternating or intermittent currents for producing shock effects
    • A61N1/39—Heart defibrillators
    • A61N1/3925—Monitoring; Protecting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing

Definitions

  • the present invention relates to a method of signal analysis, and in particular to a method for the prediction of the likelihood of successful defibrillation for subjects exhibiting ventricular fibrillation; and an associated signal analysis apparatus and defibrillator .
  • Techniques used to analyse the VF waveform include: amplitude [viii,ix] which has not proved to be a reproducible marker of defibrillation success [x,xi], Fourier- based spectral analysis [xii, iii, xiv, xv, xvi] [US Patent 5,683,424: Brown et al., US Patent 6,171257 Bl: Weil et al] and techniques from non-linear dynamics such as fractal [xvii,xviii] [US Patent 6,438,419: Callaway et al] and phase-delay [xix] which in practice can often be shown to be related to previously investigated methods [xx] .
  • Wavelet transform analysis is especially valuable because of its ability to elucidate simultaneously local spectral and temporal information from a signal [xxi] .
  • An improvement in the predictability of shock outcome is observed using wavelet techniques due to this fundamental difference between the wavelet transform and alternative prior art previously mentioned. While these alternatives, in some form or another, characterize an aspect of the behaviour of the signal over a period of time, however short, the wavelet method can identify pertinent information in time. Hence, temporal partitioning of salient aspects of the transformed ECG becomes achievable prior to any classification step.
  • the identification of coherent structure in VF signals using wavelet transform-based techniques has been previously reported [xxii, xxiii, xxiv] [International Patent Application WO 01/82099 Al : Addison and Watson] .
  • a method of signal analysis comprising the steps of deriving an electrocardiogram (ECG) signal; deriving a wavelet scalogram of the ECG; analysing the scalogram using ridge following techniques; and deriving an output from the analysis, said output representing a temporal population statistic of the ECG signal.
  • ECG electrocardiogram
  • the ECG signal is derived from a subject whose heart is in Ventricular Fibrillation (VF) or Ventricular Tachycardia (VT) .
  • VF Ventricular Fibrillation
  • VT Ventricular Tachycardia
  • said ridge following techniques include modulus maxima techniques.
  • atypical coefficient values pertaining to artefact are identified and removed.
  • said analysis includes temporal statistical analysis, wherein temporal features are derived from a temporal population statistic calculated over one or more of the scalogram scales.
  • the temporal statistical analysis includes an entropy measure of the form
  • said entropy measure is computed from coefficients associated with wavelets with characteristic frequencies above that of the typical fibrillating frequency of the heart.
  • said population statistics includes the median coefficient value of a number of the highest coefficient values from one or more of the scalogram scales.
  • said population statistics includes the median coefficient value of the three highest values.
  • said population statistics are computed from coefficients associated with wavelets with characteristic frequencies above that of the typical fibrillating frequency of the heart.
  • said temporal population statistic is used as an indication on the likelihood of the future success of a defibrillation event.
  • a method of selectively delivering a defibrillation shock to a subject whose heart is in Ventricular Fibrillation (VF) or Ventricular Tachycardia (VT) comprising the steps of a. connecting electrodes to a patient; b. deriving analogue input signals from said electrodes to derive the electrocardiogram (ECG) ; c. sampling said ECG to derive a digitised signal; d . deriving the wavelet scalogram of said ECG; e. analysing said wavelet scalogram using ridge following techniques to determine the likely outcome of defibrillation; and f . guiding the resuscitation procedure accordingly.
  • ECG electrocardiogram
  • the step (e) of analysing said wavelet scalogram is performed in accordance with the method of the first aspect.
  • apparatus for signal analysis comprising: sensor means suitable to derive an electrocardiogram (ECG) signal from a subject; signal processing means suitable for deriving a wavelet scalogram from the ECG; and an outcome prediction unit suitable for performing ridge following techniques on the scalogram and to derive an output from the analysis, said output representing a temporal population statistic of the ECG signal.
  • ECG electrocardiogram
  • a defibrillator for selectively delivering a defibrillation shock to a subject whose heart is in Ventricular Fibrillation • (VF) or Ventricular Tachycardia (VT) comprising the apparatus of the third aspect.
  • the defibrillator further comprises a user interface which includes a display means.
  • the outcome prediction unit is arranged to perform the method of the first aspect.
  • the defibrillator further comprises decision means arranged to selectively apply either a first method according to the first or second aspects of the invention in a case where the defibrillator' s computational power exceeds a predetermined threshold, or a second method according to the first or second aspects of the invention in a case where the defibrillator' s computational power is less than or equal to a predetermined threshold.
  • the current invention provides an improved method for predicting the immediate success of a defibrillation attempt.
  • novel markers obtained from the wavelet transform scalogram for use in the prediction of shock outcome are defined and the methodology for their derivation specified.
  • the wavelet transform of a signal x (t) is defined as
  • ⁇ (t) is the complex conjugate of the wavelet function ⁇ (t)
  • a is the dilation parameter of the wavelet
  • b is the location parameter of the wavelet.
  • the scalogram is the time-scale half-space generated by plotting ⁇ T (a ,b) ⁇ , the modulus of wavelet transform coefficient value, for varying scales and. locations.
  • the scalogram may also be said to mean any suitably scaled power of ⁇ T (a ,b)
  • a key advantage of wavelet techniques is the variety of wavelet functions available thus allowing the most appropriate to be chosen for the signal under investigation. This is in contrast to Fourier analysis which is restricted to one feature morphology: the sinusoid. In some embodiments the Morlet wavelet is used. This is defined as:
  • f 0 is the characteristic, or central, frequency of the mother wavelet and is chosen to best accentuate the features under investigation.
  • temporal behaviour of signal features can be quantified from the scalogram.
  • These temporal features can be derived from any intermittency measure calculated over one or more of the scalogram scales. The efficacies of these measures are enhanced through the reduction of the scalogram to its turning points in b for each scale a: the modulus maxima of the scalogram.
  • a novel wavelet-entropy marker is used as a metric of the temporal behaviour of the signal .
  • the wavelet-entropy at a scale a ' is defined as :
  • the scales over which the marker is calculated and the intermittency metric derived is dependent upon • the design characteristics of the defibrillator, such as analogue ECG signal conditioning (e.g. band-pass filtering, comb filtering) ; digital sampling rate; electrode size; electrode location; skin/electrode interface resistance. In the preferred embodiment a priori knowledge of these characteristics are used to identify optimal processing paths for calculating the marker.
  • the scales from which the metric is extracted will be of the order of that associated with a central frequency of around 45Hz.
  • a probability of successful defibrillation can be derived using standard techniques (e.g. Bayesian) or a simple threshold rule can be applied to identify whether defibrillation should be attempted. Both techniques require empirical data to identify the best course of action.
  • a linear threshold is derived from historical data to identify those patients that would benefit from defibrillation, with 95% certainty, while the remainder receive an alternative therapy (e.g. CPR).
  • Figure 1 is a schematic diagram of a defibrillator with reference to the current invention.
  • the COP analysis block returns the likelihood of successful defibrillation to the embedded controller having previously been passed a digitised ECG to analyse;
  • Figure 2 is a flow diagram outlining the therapeutic decision process with respect to the additional information supplied by the invention
  • Figure 3 shows raw ECG data with its associated wavelet scalogram beneath (a) and the modulus maximal plot of this scalogram (b) with its filtered equivalent beneath (c) ;
  • FIG 4 is a flow diagram of the invention's methodology for identifying the most effective therapy to be applied
  • Figure 5 shows the Receiver Operator Characteristic (ROC) curve resulting from the high computation route of the invention's method. (Specificity 62% ⁇ 2% at sensitivity 97% ⁇ 3%) ; and
  • FIG. 6 shows the Receiver Operator Characteristic (ROC) curve resulting from the low computation route of the invention's method. (Specificity 61%+4% at sensitivity 97% ⁇ 3%)
  • Figure 1 shows a schematic block diagram indicating the usage of an outcome prediction unit (5) within a defibrillator (2) .
  • the analogue electrocardiogram (ECG) of a patient in ventricular fibrillation (1) is detected through sensors (3) and monitoring circuit (9) where it is digitised.
  • the embedded controller (4) passes selected regions of the ECG to the outcome prediction unit (5) for analysis. In the preferred embodiment these regions will be of around 5 seconds in length with a sample rate of at least 100Hz and be from an artefact free region of trace.
  • the outcome prediction unit (5) passes back an indication of the likelihood of successful defibrillation either as a probability of successful defibrillation or as a direct command to defibrillate.
  • outcome prediction decision process (11) provides an additional conditional (14) allowing a route to CPR (12) rather than defibrillation (15) .
  • the continuous wavelet transform of equation 1 is applied employing the Morlet wavelet of equation 2.
  • Figure 3(a) shows an example of a section of digitised ECG with its associated wavelet scalogram beneath. The dark islands in the plot (a selection of which are shown at (28) ) indicate the location and morphology of high energy features within the ECG. Temporal measures such as that described by equation 3 can be applied to the scalogram at this stage.
  • the step of reducing the scalogram to its coefficient turning points in scales across time, as defined by equation 4 below, is carried out. An example of this so called Modulus Maxima is shown in Figure 3(b) .
  • an outcome prediction unit can be arranged to selectively operate the first or second method, as shown in figure .
  • the path taken is dependent upon the available computing power; conditional (16) in figure 4.
  • conditional (16) in figure 4 Where computing power (17) allows, a full scalogram (18) is generated from the passed ECG (20) over 150 scales ranging from those wavelets with a central frequency of 1Hz to those wi-th a central frequency of 45Hz (19) . It will be appreciated that a different number of scales over a different frequency range could also be considered.
  • the modulus maxima for each scale are derived and the turning points characterised as continuous ridges across scales (21) . There exist many standard techniques for ridge following.
  • non-zero coefficient points in successive scales are defined as belonging to "the same continuous ridge where their amplitude values are of the same order (for example within 10%) and when the higher dilation point is located within the temporal support of the lower dilation point. Where two or more points satisfy these criteria the points on successive scales that are closest to each other in location are deemed to be of the same continuous ridge.
  • Shorter, low amplitude, ridges with components only in the low dilation (i.e. top) region of the scalogram may be assumed to be electrical noise and removed. The remainder of the scalogram may then be analysed without the loss in performance associated with such noise.
  • any probabilistic classifier or heteroassociative function approximation method may be employed at this stage to generate a system capable of predicting a probability of defibrillation outcome.
  • the median coefficient value of a scale above that of the typical fibrillating frequency of the heart for the 3 longest ridges is taken as a marker to indicate the likelihood of successful defibrillation (25) .
  • this scale is associated with wavelets with a central frequency of 45Hz. It will be appreciated that the median value of a different number of ridges, for example five, may be chosen.
  • the method of characterisation is that of a linear threshold classifier trained on previously collected data.
  • a decision (26) is taken to defibrillate.
  • a low computational complexity path may be followed (16) .
  • a limited scalogram (22) is generated from the passed ECG (20) over a single scale above that of the typical fibrillating frequency of the heart (23). Typically this scale is associated with wavelets with a central frequency of 45Hz. The modulus maxima for this scale is then derived (24) .
  • the entropy measure of equation 3 can be applied on the turning points to indicate the likelihood of successful defibrillation.
  • statistical measures that may be applied to predict outcome from scale turning points either individually or in combination. These may include: peak, median, mean or sum of all the coefficient values of the scale or percentile thereof. The chosen measure will reflect the characteristics of the defibrillator within which the method is embodied. As before, when the marker value exceeds a threshold derived empirically from historic data (27) a decision (26) is taken to defibrillate .
  • This example of the invention's efficacy uses a human out-of-hospital data set containing 878 pre- shock ECG traces all of at least 10 second duration from 110 patients with cardiac arrest of cardiac etiology.
  • the data was recorded from the Medical Device Module of a Laerdal Heartstart 3000 defibrillator.
  • a full review of the data acquisition procedure and statistics can be found in [v] .
  • ⁇ successful defibrillation' as those attempts which result in a pulse and co-ordinated electrical activity sustained for a period greater than thirty seconds and originating within a minute of the applied shock.
  • FIG. 5 shows the receiver operator characteristic (ROC) curve indicating system performance when using the high computational complexity path of the method.
  • ROC receiver operator characteristic
  • FIG. 6 shows the ROC curve indicating system performance when using the low computational complexity path of the method.
  • the system performance was obtained though the wavelet entropy value of the scale associated with a central frequency of around 45Hz.
  • the central frequency of the mother wavelet is 0.87 in this case.
  • the system performance achieved in this example is: specificity of 61% ⁇ 4% at sensitivity 97% ⁇ 3%.
  • an alternative therapy such as CPR applied with 97% of those patients which would benefit from defibrillation still receiving this therapy.

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Abstract

L'invention prévoit la survenue de tentative de défibrillation pour des patients ayant subi un arrêt cardiaque hors de l'hôpital. Dans le procédé de cette invention, la transformée d'ondelettes en continu, les techniques maximales de module et l'analyse statistique temporelle sont appliquées afin de heurter au préalable des segments de tracé d'électrocardiogramme pour des patients en fibrillation ventriculaire (VF). Des marqueurs de temps-fréquence sont extraits de la transformée et un seuil linéaire est dérivé à partir d'un ensemble de formation afin d'obtenir une prédiction haute sensibilité d'une défibrillation réussie. L'utilisation du marqueur d'ondelettes est associée à une valeur à haute spécificité à des sensibilités élevées par rapport à des procédés connus à ce jour. L'invention offre un indice amélioré pour l'identification de patients pour lesquels le heurtage est inutile et pour lesquels une thérapie alternative pourrait être pris en considération.
PCT/GB2005/000477 2004-03-31 2005-02-10 Analyse d'electrocardiogramme-signal pour prediction de sortie de choc WO2005096170A1 (fr)

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WO2006085120A1 (fr) * 2005-02-10 2006-08-17 Cardiodigital Limited Ameliorations liees a l'analyse de signaux
WO2010001242A1 (fr) * 2008-06-30 2010-01-07 Nellcor Puritan Bennett Ireland Systèmes et procédés de sélection de crête dans des scalogrammes de signaux
WO2010001232A1 (fr) * 2008-06-30 2010-01-07 Nellcor Puritan Bennett Ireland Systèmes et procédés de détermination d'un effort
US7944551B2 (en) 2008-06-30 2011-05-17 Nellcor Puritan Bennett Ireland Systems and methods for a wavelet transform viewer
US8077297B2 (en) 2008-06-30 2011-12-13 Nellcor Puritan Bennett Ireland Methods and systems for discriminating bands in scalograms
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US8226568B2 (en) 2008-07-15 2012-07-24 Nellcor Puritan Bennett Llc Signal processing systems and methods using basis functions and wavelet transforms
US8235911B2 (en) 2008-07-15 2012-08-07 Nellcor Puritan Bennett Ireland Methods and systems for filtering a signal according to a signal model and continuous wavelet transform techniques
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US8761855B2 (en) 2008-07-15 2014-06-24 Nellcor Puritan Bennett Ireland Systems and methods for determining oxygen saturation
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CN107729861A (zh) * 2017-10-30 2018-02-23 太原理工大学 基于随机森林的j波检测方法
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JP2018529470A (ja) * 2015-09-30 2018-10-11 ハート テスト ラボラトリーズ，インコーポレイテッド 定量的心臓検査
IT201800005089A1 (it) * 2018-05-04 2019-11-04 Cardiac activity measurement system and method thereof.

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111803054B (zh) * 2020-06-12 2023-04-07 中国人民解放军陆军军医大学 一种实时评估室颤信号质量和除颤成功率的方法与仪器

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6171257B1 (en) * 1998-09-25 2001-01-09 The Institute Of Critical Care Medicine Method and system for predicting the immediate success of a defibrillatory shock during cardiac arrest
WO2001082099A1 (fr) * 1999-05-01 2001-11-01 The Court Of Napier University Procede pour l'analyse de signaux medicaux
US20030163057A1 (en) * 2002-02-22 2003-08-28 Flick James T. Method for diagnosing heart disease, predicting sudden death, and analyzing treatment response using multifractial analysis

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6171257B1 (en) * 1998-09-25 2001-01-09 The Institute Of Critical Care Medicine Method and system for predicting the immediate success of a defibrillatory shock during cardiac arrest
WO2001082099A1 (fr) * 1999-05-01 2001-11-01 The Court Of Napier University Procede pour l'analyse de signaux medicaux
US20030163057A1 (en) * 2002-02-22 2003-08-28 Flick James T. Method for diagnosing heart disease, predicting sudden death, and analyzing treatment response using multifractial analysis

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
ADDISON P S ET AL: "EVALUATING ARRHYTHMIAS IN ECG SIGNALS USING WAVELET TRANSFORMS REAL-TIME ANALYSIS OF THE VENTRICULAR FIBRILLATION WAVEFORM CAN REVEAL HIDDEN STRUCTURES", IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, IEEE INC. NEW YORK, US, vol. 19, no. 5, September 2000 (2000-09-01), pages 104 - 109, XP000963072, ISSN: 0739-5175 *
ADDISON P S ET AL: "Wavelet power spectrum-based prediction of successful defibrillation from ventricular fibrillation", PROCEEDINGS OF THE 23RD. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. 2001 CONFERENCE PROCEEDINGS. (EMBS). INSTANBUL, TURKEY, OCT. 25 - 28, 2001, ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN M, vol. VOL. 1 OF 4. CONF. 23, 25 October 2001 (2001-10-25), pages 1812 - 1815, XP010594786, ISBN: 0-7803-7211-5 *
ROMERO LEGARRETAL I ET AL INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS: "R-wave detection using continuous wavelet modulus maxima", COMPUTERS IN CARDIOLOGY 2003. THESSALONIKI, GREECE, SEPT. 21 - 24, 2003, NEW YORK, NY : IEEE, US, vol. VOL. 30, 21 September 2003 (2003-09-21), pages 565 - 568, XP010698967, ISBN: 0-7803-8170-X *

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WO2006085120A1 (fr) * 2005-02-10 2006-08-17 Cardiodigital Limited Ameliorations liees a l'analyse de signaux
US8983588B2 (en) 2005-02-10 2015-03-17 Cardiodigital Limited Signal analysis
US8077297B2 (en) 2008-06-30 2011-12-13 Nellcor Puritan Bennett Ireland Methods and systems for discriminating bands in scalograms
WO2010001232A1 (fr) * 2008-06-30 2010-01-07 Nellcor Puritan Bennett Ireland Systèmes et procédés de détermination d'un effort
JP2011526515A (ja) * 2008-06-30 2011-10-13 ネルコー ピューリタン ベネット アイルランド 信号のスカログラムにおけるリッジ選択のためのシステムおよび方法
US9113815B2 (en) 2008-06-30 2015-08-25 Nellcor Puritan Bennett Ireland Systems and methods for ridge selection in scalograms of signals
US8295567B2 (en) 2008-06-30 2012-10-23 Nellcor Puritan Bennett Ireland Systems and methods for ridge selection in scalograms of signals
US9378332B2 (en) 2008-06-30 2016-06-28 Nellcor Puritan Bennett Ireland Processing and detecting baseline changes in signals
WO2010001242A1 (fr) * 2008-06-30 2010-01-07 Nellcor Puritan Bennett Ireland Systèmes et procédés de sélection de crête dans des scalogrammes de signaux
AU2009265259B2 (en) * 2008-06-30 2013-09-26 Nellcor Puritan Bennett Ireland Systems and methods for determining effort
US7944551B2 (en) 2008-06-30 2011-05-17 Nellcor Puritan Bennett Ireland Systems and methods for a wavelet transform viewer
US8483459B2 (en) 2008-06-30 2013-07-09 Nèllcor Puritan Bennett Ireland Systems and methods for ridge selection in scalograms of signals
US8433524B2 (en) 2008-07-15 2013-04-30 Nellcor Puritan Bennett Ireland Low perfusion signal processing systems and methods
US8761855B2 (en) 2008-07-15 2014-06-24 Nellcor Puritan Bennett Ireland Systems and methods for determining oxygen saturation
US8385675B2 (en) 2008-07-15 2013-02-26 Nellcor Puritan Bennett Ireland Systems and methods for filtering a signal using a continuous wavelet transform
US9259188B2 (en) 2008-07-15 2016-02-16 Nellcor Puritan Bennett Ireland Methods and systems for filtering a signal according to a signal model and continuous wavelet transform techniques
US8358213B2 (en) 2008-07-15 2013-01-22 Covidien Lp Systems and methods for evaluating a physiological condition using a wavelet transform and identifying a band within a generated scalogram
US10265005B2 (en) 2008-07-15 2019-04-23 Nellcor Puritan Bennett Ireland Systems and methods for determining oxygen saturation
US8660625B2 (en) 2008-07-15 2014-02-25 Covidien Lp Signal processing systems and methods for analyzing multiparameter spaces to determine physiological states
US8679027B2 (en) 2008-07-15 2014-03-25 Nellcor Puritan Bennett Ireland Systems and methods for pulse processing
US8285352B2 (en) 2008-07-15 2012-10-09 Nellcor Puritan Bennett Llc Systems and methods for identifying pulse rates
US9066691B2 (en) 2008-07-15 2015-06-30 Nellcor Puritan Bennett Ireland Using a continuous wavelet transform to generate a reference signal
US8786444B2 (en) 2008-07-15 2014-07-22 Nellcor Puritan Bennett Ireland Systems and methods for determining signal quality of a physiological signal using a wavelet transform and an identified noise floor
US8235911B2 (en) 2008-07-15 2012-08-07 Nellcor Puritan Bennett Ireland Methods and systems for filtering a signal according to a signal model and continuous wavelet transform techniques
US9031627B2 (en) 2008-07-15 2015-05-12 Nellcor Puritan Bennett Ireland Signal processing systems and methods using multiple signals
US8226568B2 (en) 2008-07-15 2012-07-24 Nellcor Puritan Bennett Llc Signal processing systems and methods using basis functions and wavelet transforms
US8082110B2 (en) 2008-07-15 2011-12-20 Nellcor Puritan Bennett Ireland Low perfusion signal processing systems and methods
US9011347B2 (en) 2008-10-03 2015-04-21 Nellcor Puritan Bennett Ireland Methods and apparatus for determining breathing effort characteristics measures
US9155493B2 (en) 2008-10-03 2015-10-13 Nellcor Puritan Bennett Ireland Methods and apparatus for calibrating respiratory effort from photoplethysmograph signals
US8364225B2 (en) 2009-05-20 2013-01-29 Nellcor Puritan Bennett Ireland Estimating transform values using signal estimates
US8636667B2 (en) 2009-07-06 2014-01-28 Nellcor Puritan Bennett Ireland Systems and methods for processing physiological signals in wavelet space
US8478376B2 (en) 2009-07-30 2013-07-02 Nellcor Puritan Bennett Ireland Systems and methods for determining physiological information using selective transform data
US8954127B2 (en) 2009-07-30 2015-02-10 Nellcor Puritan Bennett Ireland Systems and methods for estimating values of a continuous wavelet transform
US8594759B2 (en) 2009-07-30 2013-11-26 Nellcor Puritan Bennett Ireland Systems and methods for resolving the continuous wavelet transform of a signal
US8346333B2 (en) 2009-07-30 2013-01-01 Nellcor Puritan Bennett Ireland Systems and methods for estimating values of a continuous wavelet transform
US8628477B2 (en) 2009-07-31 2014-01-14 Nellcor Puritan Bennett Ireland Systems and methods for non-invasive determination of blood pressure
US8855749B2 (en) 2009-09-24 2014-10-07 Covidien Lp Determination of a physiological parameter
US8400149B2 (en) 2009-09-25 2013-03-19 Nellcor Puritan Bennett Ireland Systems and methods for gating an imaging device
US9050043B2 (en) 2010-05-04 2015-06-09 Nellcor Puritan Bennett Ireland Systems and methods for wavelet transform scale-dependent multiple-archetyping
US8834378B2 (en) 2010-07-30 2014-09-16 Nellcor Puritan Bennett Ireland Systems and methods for determining respiratory effort
US9113830B2 (en) 2011-05-31 2015-08-25 Nellcor Puritan Bennett Ireland Systems and methods for detecting and monitoring arrhythmias using the PPG
US9597022B2 (en) 2011-09-09 2017-03-21 Nellcor Puritan Bennett Ireland Venous oxygen saturation systems and methods
US9974468B2 (en) 2013-03-15 2018-05-22 Covidien Lp Systems and methods for identifying a medically monitored patient
US10022068B2 (en) 2013-10-28 2018-07-17 Covidien Lp Systems and methods for detecting held breath events
CN103750835A (zh) * 2013-11-12 2014-04-30 天津工业大学 一种心电信号特征检测算法
US9955894B2 (en) 2014-01-28 2018-05-01 Covidien Lp Non-stationary feature relationship parameters for awareness monitoring
US10786198B2 (en) 2014-01-28 2020-09-29 Covidien Lp Non-stationary feature relationship parameters for awareness monitoring
JP2018529470A (ja) * 2015-09-30 2018-10-11 ハート テスト ラボラトリーズ，インコーポレイテッド 定量的心臓検査
US10561327B2 (en) 2015-09-30 2020-02-18 Heart Test Laboratories, Inc. Quantitative heart testing
US11445968B2 (en) 2015-09-30 2022-09-20 Heart Test Laboratories, Inc. Quantitative heart testing
CN107729861A (zh) * 2017-10-30 2018-02-23 太原理工大学 基于随机森林的j波检测方法
IT201800005089A1 (it) * 2018-05-04 2019-11-04 Cardiac activity measurement system and method thereof.

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