Source: http://www.google.com/patents/US7996075?dq=patent:5567455
Timestamp: 2015-05-30 07:48:01
Document Index: 792247207

Matched Legal Cases: ['art.\n10', 'art.\n21', 'Application No. 2005295313', 'Application No. 2584503', 'Application No. 2', 'Application No. 2', 'Application No. 2005']

Patent US7996075 - Monitoring physiological activity using partial state space reconstruction - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsSystems and techniques relating to monitoring physiological activity using partial state space reconstruction. In general, in one aspect, a partial state space is produced using an orthogonal, frequency-independent transform, such as Hilbert transform. The partial state space can then be analyzed using...http://www.google.com/patents/US7996075?utm_source=gb-gplus-sharePatent US7996075 - Monitoring physiological activity using partial state space reconstructionAdvanced Patent SearchPublication numberUS7996075 B2Publication typeGrantApplication numberUS 11/081,401Publication dateAug 9, 2011Filing dateMar 15, 2005Priority dateOct 20, 2004Fee statusPaidAlso published asCA2584503A1, CN101065058A, CN101065058B, EP1802230A2, EP1802230A4, EP1802230B1, EP2428160A2, EP2428160A3, US20060084881, WO2006044919A2, WO2006044919A3Publication number081401, 11081401, US 7996075 B2, US 7996075B2, US-B2-7996075, US7996075 B2, US7996075B2InventorsLev Korzinov, Michael KremliovskyOriginal AssigneeCardionet, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (59), Non-Patent Citations (40), Referenced by (3), Classifications (7), Legal Events (4) External Links: USPTO, USPTO Assignment, EspacenetMonitoring physiological activity using partial state space reconstruction
US 7996075 B2Abstract
Systems and techniques relating to monitoring physiological activity using partial state space reconstruction. In general, in one aspect, a partial state space is produced using an orthogonal, frequency-independent transform, such as Hilbert transform. The partial state space can then be analyzed using state space techniques to identify physiological information for the biological system. The described techniques can be implemented in a distributed cardiac activity monitoring system, including a cardiac monitoring apparatus, and a QRS detector thereof.
obtaining a physiological signal of a biological system of an organism;
generating a transformed signal that is mathematically orthogonal to the physiological signal by applying a frequency-independent transform to the physiological signal;
producing, from the physiological signal and the transformed signal, a partial state space representing dynamics of the biological system, the partial state space being a partial reconstruction of a state space representing system dynamics for the biological system; and
identifying physiological information concerning the organism based on an analysis of signal morphology in the partial state space;
wherein the producing and the identifying are performed by one or more data processing apparatus.
2. The method of claim 1, wherein obtaining the physiological signal comprises receiving a sensed cardiac signal.
3. The method of claim 2, wherein identifying the physiological information comprises classifying heart beats in the sensed cardiac signal.
4. The method of claim 2, wherein identifying the physiological information comprises characterizing a physiological condition of the organism.
5. The method of claim 1, wherein identifying the physiological information comprises detecting a physiological occurrence for the biological system based on a dynamical quantity comprising a value derived from the physiological signal and the transformed signal.
6. The method of claim 5, wherein obtaining the physiological signal comprises receiving an electrically-sensed time series x(t), generating the transformed signal comprises applying Hilbert (H) transform to the time series x(t) to obtain H(x(t)), and producing the partial state space comprises considering x(t) and H(x(t)) together as components of a state vector.
7. The method of claim 6, further comprising generating multiple dynamical quantities from the physiological signal and the transformed signal, and wherein identifying the physiological information comprises assessing the multiple dynamical quantities with respect to one or more predefined physiological aspects of the biological system.
8. The method of claim 7, wherein the multiple dynamical quantities comprise nonlinear transformations of x(t) and H(x(t)) in state space, excepting simple linear combinations of amplitude and phase.
9. The method of claim 5, wherein obtaining the physiological signal comprises receiving a real-time, electrocardiogram time series for an actively monitored human heart, generating the analytic signal comprises applying Hilbert transform directly to the received electrocardiogram time series, and detecting the physiological occurrence comprises assessing the dynamical quantity with respect to one or more predefined physiological aspects of the human heart.
10. The method of claim 1, wherein the frequency-independent transform comprises a nonlinear transform.
11. The method of claim 10, wherein the frequency-independent transform comprises a linear transform and a nonlinear transform.
12. A software product tangibly embodied in a machine-readable medium, the software product comprising instructions operable to cause data processing apparatus to perform operations comprising:
producing, from the physiological signal and the transformed signal, a partial state space representing dynamics of the biological system, the partial state space being a partial reconstruction of a potential full state space for the biological system; and
identifying physiological information concerning the organism based on an analysis of signal morphology in the partial state space.
13. The software product of claim 12, wherein obtaining the physiological signal comprises receiving a sensed cardiac signal.
14. The software product of claim 13, wherein identifying the physiological information comprises classifying heart beats in the sensed cardiac signal.
15. The software product of claim 13, wherein identifying the physiological information comprises characterizing a physiological condition of the organism.
16. The software product of claim 12, wherein identifying the physiological information comprises detecting a physiological occurrence for the biological system based on a dynamical quantity comprising a value derived from the physiological signal and the transformed signal.
17. The software product of claim 16, wherein obtaining the physiological signal comprises receiving an electrically-sensed time series x(t), generating the transformed signal comprises applying Hilbert (H) transform to the time series x(t) to obtain H(x(t)), and producing the partial state space comprises considering x(t) and H(x(t)) together as components of a state vector.
18. The software product of claim 17, the operations further comprising generating multiple dynamical quantities from the physiological signal and the transformed signal, and wherein identifying the physiological information comprises assessing the multiple dynamical quantities with respect to one or more predefined physiological aspects of the biological system.
19. The software product of claim 18, wherein the multiple dynamical quantities comprise nonlinear transformations of x(t) and H(x(t)) in state space, excepting simple linear combinations of amplitude and phase.
20. The software product of claim 16, wherein obtaining the physiological signal comprises receiving a real-time, electrocardiogram time series for an actively monitored human heart, generating the analytic signal comprises applying Hilbert transform directly to the received electrocardiogram time series, and detecting the physiological occurrence comprises assessing the dynamical quantity with respect to one or more predefined physiological aspects of the human heart.
21. The software product of claim 12, wherein the frequency-independent transform comprises a nonlinear transform.
22. The software product of claim 21, wherein the frequency-independent transform comprises a linear transform and a nonlinear transform.
23. A cardiac monitoring apparatus comprising:
an ECG input element;
a state space transformation component responsive to an output of the ECG input element and configured to generate a transformed signal that is mathematically orthogonal to a physiological signal, for a biological system of an organism, by applying a frequency-independent transform to the physiological signal; and
a QRS identification component responsive to an output of the state space transformation component, the QRS identification component comprising one or more dynamical quantity calculators that each produce a value derived from a combination of the physiological signal and the transformed signal in a partial state space representing dynamics of the biological system, the partial state space being a partial reconstruction of a state space representing system dynamics for the biological system.
24. A cardiac monitoring apparatus comprising:
a state space transformation component responsive to an output of the ECG input element; and
a QRS identification component responsive to an output of the state space transformation component, the QRS identification component comprising one or more dynamical quantity calculators;
the apparatus further comprising a pQRST parameter averaging component coupled with the QRS identification component, a noise estimator responsive to the output of the ECG input element, and final QRS decision logic coupled with the QRS identification component, the pQRST parameter averaging component and the noise estimator.
25. The apparatus of claim 24, further comprising a filter coupled between the ECG input element and both the state space transformation component and the noise estimator.
26. The apparatus of claim 25, wherein the output of the ECG input element comprises a split output, the apparatus further comprising an arrhythmia identification component coupled with the split output and with an input of the final QRS decision logic.
27. The apparatus of claim 26, wherein the arrhythmia identification component comprises a ventricular fibrillation detector.
a state space transformation component responsive to an output of the ECG input element and configured to generate a transformed signal that is mathematically orthogonal to a physiological signal, for a biological system of an organism, by applying a frequency-independent transform to the physiological signal;
a QRS identification component responsive to an output of the state space transformation component, the QRS identification component comprising one or more dynamical quantity calculators that each produce a value derived from a combination of the physiological signal and the transformed signal in a partial state space representing dynamics of the biological system, the partial state space being a partial reconstruction of a state space representing system dynamics for the biological system; and
an antenna coupled with the QRS identification component and configured to wirelessly transmit physiological information.
29. The system of claim 28, further comprising a monitoring station configured to receive the transmitted physiological information.
a state space transformation component responsive to an output of the ECG input element;
an antenna coupled with the QRS identification component and configured to wirelessly transmit physiological information; and
a monitoring station configured to receive the transmitted physiological information,
the system further comprising a pQRST parameter averaging component coupled with the QRS identification component, a noise estimator responsive to the output of the ECG input element, and final QRS decision logic coupled with the QRS identification component, the pQRST parameter averaging component and the noise estimator.
31. The system of claim 30, further comprising a filter coupled between the ECG input element and both the state-space transformation component and the noise estimator.
32. The system of claim 31, wherein the output of the ECG input element comprises a split output, the apparatus further comprising an arrhythmia identification component coupled with the split output and with an input of the final QRS decision logic.
33. The system of claim 32, wherein the arrhythmia identification component comprises a ventricular fibrillation detector and an asystole detector.
34. A machine-implemented method comprising:
obtaining a cardiac signal of a heart;
translating the cardiac signal into an embedding space that represents coarse-grained dynamics of the heart, said translating comprising applying Hilbert transform to the cardiac signal; and
employing state space analysis techniques to extract physiological information for the heart from the embedding space;
wherein the translating and the employing are performed by one or more data processing apparatus.
35. The method of claim 34, wherein obtaining the cardiac signal comprises obtaining multiple cardiac signals from independent leads, and translating the cardiac signal comprises applying the Hilbert transform directly to the multiple cardiac signals to form the embedding space having an embedding dimension greater than or equal to twice a number of the independent leads.
36. The method of claim 35, wherein the number of the independent leads is two, and the embedding space has four spatial dimensions.
37. The method of claim 35, wherein obtaining the multiple cardiac signals comprises retrieving the multiple cardiac signals from a database.
38. The method of claim 34, wherein translating the cardiac signal comprises calculating a nonlinear combination of the cardiac signal and the Hilbert transform of the cardiac signal.
39. The method of claim 34, wherein employing state space analysis techniques comprises deriving multiple dynamical quantities from the embedding space.
40. The method of claim 39, wherein the multiple dynamical quantities comprise speed of trajectory in state space, length of trajectory in state space, area integral of a speed vector, and threshold crossings in state space.
41. The method of claim 34, further comprising detecting abnormal heart beats based on the extracted physiological information.
42. The method of claim 41, further comprising estimating a physiological condition based on the detected abnormal heart beats.
43. A software product tangibly embodied in a machine-readable medium, the software product comprising instructions operable to cause one or more data processing apparatus to perform operations comprising:
employing state space analysis techniques to extract physiological information for the heart from the embedding space.
44. The software product of claim 43, wherein obtaining the cardiac signal comprises obtaining multiple cardiac signals from independent leads, and translating the cardiac signal comprises applying the Hilbert transform directly to the multiple cardiac signals to form the embedding space having an embedding dimension greater than or equal to twice a number of the independent leads.
45. The software product of claim 44, wherein the number of the independent leads is two, and the embedding space has four spatial dimensions.
46. The software product of claim 44, wherein obtaining the multiple cardiac signals comprises retrieving the multiple cardiac signals from a database.
47. The software product of claim 43, wherein translating the cardiac signal comprises calculating a nonlinear combination of the cardiac signal and the Hilbert transform of the cardiac signal.
48. The software product of claim 43, wherein employing state space analysis techniques comprises deriving multiple dynamical quantities from the embedding space.
49. The software product of claim 48, wherein the multiple dynamical quantities comprise speed of trajectory in state space, length of trajectory in state space, area integral of a speed vector, and threshold crossings in state space.
50. The software product of claim 43, the operations further comprising detecting abnormal heart beats based on the extracted physiological information.
51. The software product of claim 50, the operations further comprising estimating a physiological condition based on the detected abnormal heart beats.
This application claims the benefit of the priority of U.S. Provisional Application Ser. No. 60/620,864, filed Oct. 20, 2004 and entitled “MONITORING PHYSIOLOGICAL ACTIVITY USING PARTIAL STATE SPACE RECONSTRUCTION”; and this application claims the benefit of the priority of U.S. Provisional Application Ser. No. 60/633,320, filed Dec. 3, 2004 and entitled “MONITORING PHYSIOLOGICAL ACTIVITY USING PARTIAL STATE SPACE RECONSTRUCTION”.
The present application describes systems and techniques relating to monitoring physiological activity of an organism, for example, performing QRS detection on a cardiac signal obtained from a person.
Cardiac monitoring devices can sense the cardiac electrical activity of a living being and identify heart beats. Frequently, identification of heart beats is performed by identifying the R waves in the QRS complex, as can be seen in an electrocardiogram (ECG). The R wave represents ventricular depolarization. The typically large amplitude of this wave in the QRS complex is useful in identifying a heart beat. Traditional automated ECG signal analysis tools typically rely on correlation-based template matching and a number of empirical decision rules that are optimized for certain ECG databases. Many techniques have been developed for performing QRS detection, but further improvements are desirable.
The present disclosure includes systems and techniques relating to monitoring physiological activity using partial state space reconstruction. In general, in one aspect, a partial reconstruction of a state space for a biological system can be produced using a frequency-independent transform, such as Hilbert transform, which produces a transformed signal that is mathematically orthogonal to a physiological signal. The idea of extracting dynamical information from a partially reconstructed state space relies on the observation that full reconstruction does not necessarily improve understanding of the most important features of the physiological activity. The lower dimensional partial reconstruction often contains all the key features required to extract dynamical properties of the physiological system. The partial state space can then be analyzed using state space techniques to identify physiological information. These techniques can be implemented in a distributed cardiac activity monitoring system, including a cardiac monitoring apparatus, and a QRS detector thereof.
One or more of the following advantages may be provided. Dynamical features of the heart can be better and more naturally represented in higher dimensional state space. Hilbert transform can be easily implemented in a form of digital filter with a minimal distortion for spectral characteristic of the underlying signal. Reliable classification of heart beats can be based on their morphology in a higher dimensional space as opposed to a conventional time series representation. For example, ventricular beats can be readily distinguished from normal beats by automated procedures. Moreover, this classification can be accurately performed even when there are a smaller number of leads in the cardiac monitoring system, which can provide advantages in terms of reduced data storage and extended monitoring applications.
Electrical signals obtained from a biological system, such as the heart, are a measure of electric potential created by the biological system, and thus these signals are only representative of the real dynamics of the biological system. The present systems and techniques can enable an automated process to perform what can be considered an inverse problem, similar to what a clinician or physician does when looking at an ECG time series, going from the obtained signals back to the system dynamics, and thereby figuring out what happened in the heart to cause the lead signals to behave as observed.
State space transformation allows a physiological signal to be represented in a very general/invariant form, which can avoid peculiarities associated with particular anatomic and/or electrophysiological features of the subjects. In general, noise has increasingly different/irregular dynamical behavior in higher dimensional space, and thus its detection and estimation becomes an easier task. The risk of false positives, false negatives, or both can be reduced. Using Hilbert transform in combination with state space techniques can result in substantial improvements in identification of signal features. Dynamical quantities of the signal can be calculated, and subsequent analysis operations can be based on these dynamical quantities. Monitoring devices can be improved by using automated analysis based on dynamical quantities to detect when an arrhythmia is happening with a high degree of accuracy and high sensitivity. Effective automation of the detection and diagnosis of heart arrhythmia can thus be achieved using the very nature of the heart dynamical behavior.
FIG. 1 is a flow chart illustrating monitoring of physiological activity using partial state space reconstruction.
FIGS. 4, 8A, 8B and 8C illustrate a state space approach to beat classification based on ventricular depolarization using analytical signal reconstructed using Hilbert transform.
The systems and techniques described here enable partial reconstruction of heart dynamics from one- and two-lead systems. In general, the approach described here is based on the fact that an acquired electrical signal, such as an ECG signal, is a representation, or a projection, of the electrical activity of a biological system (e.g., the heart) onto some lead system. Reconstructing the dynamics of the heart from the available leads' signals can result in more accurate diagnosis of the heart's electrical activity. Partial reconstruction of the heart's dynamics can be performed using only a couple of leads. The systems and techniques described below (e.g., a Mobile Cardiac Outpatient Telemetry System) can result in improved diagnostics without requiring significant additional computational resources. Other advantages can include a more precise detection of fiducial points, used for such calculations as QRS width and QT interval, a more accurate ventricular morphology analysis, and improved stability of the detection algorithm in the presence of noise.
A transformed signal is generated by applying a frequency-independent transform (e.g., a digital version of Hilbert transform) at 120. The transformed signal is mathematically orthogonal to the physiological signal, and the transform is frequency-independent in that it does not favor or amplify some frequencies of the signal over others. This frequency-independence can be particularly useful in analyzing biological signals, such as ECG data, where the frequency spectrum can easily cover a wide range of frequencies. For example, the heart's frequency spectrum can include frequencies as low as 1 hertz and as high 100 hertz.
Moreover, the frequency-independent transform can be a generally noise insensitive transform, such as Hilbert transform. This can be of tremendous value when analyzing signals sensed from biological systems, where the noise component of the signal may be significant. The Hilbert transform can be especially useful in this context, despite that fact that Hilbert transform imposes potential limits on what might otherwise be considered a preferred approach of full scale embedding for the biological system. The present inventors have recognized that a partial state space approach is nonetheless extremely useful given the typical dominance of a few major wave forms in the real-world, sensed physiological signals.
A multi-dimensional partial state space is produced from the physiological signal and the transformed signal at 130. The partial state space is a partial reconstruction of a potential full state space for the biological system, and the full state space represents the dynamics of the biological system. Employing state space techniques, which are specific to the state space representation, to analyze biological system activity has been found to be quite effective, even when working only in a partial state space (i.e., a lower dimensional space).
Even a two dimensional partial state space (the original signal plus its Hilbert transform, with the third dimension of time being implicit) has been found highly effective in QRS detection as described below; and using a lower dimensional space can have significant advantages in terms reducing the complexity of automated analysis (e.g., in some implementations, only a single lead and thus only a single input signal are needed). Using state space techniques on a partial state space to identify physiological information can be very effective in practice because the partial state space retains many properties of the original signal, while also adding properties specific to the state space representation. For example, noise in the original signal tends to have increasingly different/irregular dynamical behavior in higher dimensional space, and thus its detection and estimation can become an easier task in a physiological monitoring device or monitoring station in communication with such a device.
Obtaining the physiological signal can involve receiving an electrically-sensed time series x(t), generating higher dimensional signal can involve applying Hilbert (H) transform to the time series x(t) to obtain H(x(t)), and producing the multi-dimensional partial state space can involve considering x(t) and H(x(t)) together as components of a state vector. These two variables, x(t) and H(x(t)), form a simple partial state space. Such procedure is also called embedding of x(t) into (partial) state space. For an implementation using multiple source signals (e.g., a multi-lead ECG input), x(t) is a multi-dimensional vector, in which case, both x(t) and H(x(t)) are vectors, and the partial state space has dimensions equal to twice that of x(t).
FIG. 2 illustrates a distributed cardiac activity monitoring system 200 in which a cardiac signal is monitored for medical purposes. An organism 210 (e.g., a human patient, including potentially a healthy patient for whom cardiac monitoring is nonetheless deemed appropriate) has a cardiac monitoring apparatus 220 configured to obtain cardiac signals from the patient's heart. The cardiac monitoring apparatus 220 can be composed of one or more devices, such as a processing device and a sensing device. The sensing device can include two independent leads 225, which can receive electrical signals through body surface electrodes as shown (e.g., silver/silver chloride electrodes, which can be positioned at defined locations to aid in monitoring the electrical activity of the heart). As used herein, the term “lead” should be understood as including both a device that is subject to a potential difference that yields a voltage signal, such as an electrode that produces an ECG signal, and a conductor that forms a signal path to any signal amplifier used in the apparatus 220.
The cardiac monitoring apparatus 220 can communicate sensed cardiac signals, cardiac event information (e.g., real-time heart rate data), and additional physiological and/or other information to the monitoring station 240. The cardiac monitoring apparatus 220 can include an implantable medical device, such as an implantable cardiac defibrillator and an associated transceiver or pacemaker and an associated transceiver, or an external monitoring device that the patient wears or even stationary installed. Moreover, the cardiac monitoring apparatus 220 can be implemented using, for example, the CardioNet Mobile Cardiac Outpatient Telemetry (MCOT) device, which is commercially available and provided by CardioNet, Inc of San Diego, Calif.
Output of the filter 315 can be provided to a noise estimator 320 and a state space transformation component 325. The state space transformation component 325 can generate a partial state space as described, such as by applying Hilbert transform directly to the ECG signal and providing both the ECG signal and the transformed ECG signal to a QRS identification component 330. It should be noted that applying the Hilbert transform “directly to” the ECG signal as shown (the intermediate filtering is not considered to negate this direct application of the Hilbert transform as such filtering does not constitute intermediate analytical processing) can have significant advantages in combination with the state space analysis techniques described; Hilbert transform can be applied at the front-end of the algorithm, rather than to some derivative of the cardiac signal. In addition, the state space transformation component 325 can effect noise cancellation in the process of transforming the signal, which can be a result of the partial state space the signal is transformed into.
The QRS identification component 330 is responsive to the output of the state-space transformation component 325 and includes one or more dynamical quantity calculators 335, such as described further below. The QRS identification component 330 can perform signal analysis in the partial state space based on morphology parameters 340 provided to it, and the QRS identification component 330 can be coupled with both a pQRST parameter averaging component 345 and final QRS decision logic 350.
FIG. 4 illustrates a state space approach to beat classification based on ventricular depolarization. A first graph 400 shows an ECG signal 410 and its bandpass filtered version 420, with amplitude being the vertical axis and time being the horizontal axis. The heart cycle includes the traditionally recognized waveforms: the P wave, the QRS complex, the T wave, and the U wave. An abnormal heart beat is included in a time window 430, and a second graph 450 shows this abnormal heart beat presented in a partial state space.
V → ( t ) = S → ( t ) - S → ( t - Δ t ) Δ t , ( 1 ) where {right arrow over (S)}(t) is a vector in the state space with coordinates x(t) and H(x(t)). Length of a trajectory in state space can be defined as a dynamical quantity L(t), which can be calculated as the sum of the point to point distances in state space; this is a nonlinear function of phase trajectory, which can be used to estimate system wandering (random deviation) from expected evolution. Area integral of a speed vector can be estimated as:
A ( t ) = ∑ t = t 0 t 0 + n Δ t  V → ( t ) ⊗ [ V → ( t ) - V → ( t - Δ t ) ]  , ( 2 ) where nΔt is the time interval where area A(t) is calculated. Threshold crossings in state space correspond to selected points in state space at which a trajectory crosses specific planes such as (x(t),0) or (0,H(t)). In general, intersection of phase trajectory and a selected surface is called Poincare mapping, and this mapping can be used to find onsets of state transitions, such as peaks of electrophysiological waves. Although three examples of dynamical quantities are described, it will be apparent that other state space analysis techniques can also be used, such as nearest neighbor techniques, calculation of topological defects, or variations of these.
FIGS. 6 and 7 are block diagrams illustrating an example cardiac processing system 600 and QRS detector 700 employing the systems and techniques described above. The system 600 includes an ECG data acquisition system 610, which employs fewer than ten leads. For example, the system 610 can be a two lead system as described above. The ECG data acquisition system 610 can provide a two-channel sampled ECG signal to a QRS and QT analysis package for processing (e.g., at a sample rate of 250 samples/second). Moreover, the input to the package can include the sampled data, pacemaker spike and invalid lead information (per sample), plus commands and configuration information.
A QRS and VFIB (ventricular fibrillation) detector 620 can analyze the input signal and provide output including QRS location and morphology information (e.g., normal, ventricular or unclassified) and a VFIB signal. An AFIB (atrial fibrillation) detector 630 can check for atrial fibrillation. A QT interval measurement component 640 can measure the QT interval, such as described further below. Moreover, the output of these components can be provided as input to one or more triggers 650 in an arrhythmia analysis system.
A morphology classification stage 740 can employ RR′ analysis (e.g., asymmetry, double notch detection), QS analysis (e.g., beat width), P-wave detection, T-wave detection and a ventricular morphology check. After successful classification, a beat can be assigned certain metrics, which can be used to update beat statistics.
A channel fusion stage 750 can make a final decision on QRS correlation between the channels, quality of the beat (beat versus artifact) and ventricular morphology. At this stage, the channels can be merged into a single output. Moreover, programmable control can be provided over the output of various information associated with the detected beat or channel quality. For example, the output can be set to include beat annotations (e.g., “N”=normal beat, “V”=ventricular beat, “Q”=not classified) and the time stamp corresponding to the detected center of the QRS complex. Extended annotations may include fiducial points (e.g., Q-points, S-points, P-wave location, and T-wave location) as well as channel characteristics (e.g., signal-to-noise ratio, detection confidence and so on).
The VFIB detector 770 can detect ventricular fibrillation/flutter rhythms through analysis of the incoming ECG based on the following criteria: VFIB triggers when QRS-like activity is absent and the ventricular signal is above noise level (VFIB flag is true). If this event happens, then the QRS detector can be run in idle until the VFIB flag is set to false (VFIB is not detected).
The QT interval measurement component 640 from FIG. 7 can measure the QT interval using one of various QT interval definitions. In general, the QT interval is the distance between the Q and the end of the T-wave. The Q point is defined as the beginning of a QRS wave, but the end of the T-wave can be defined in at least two distinct ways: a “tangent” approach and an “amplitude” approach. The amplitude approach defines the end of a T wave as a point where amplitude of the ECG signal becomes less than 0.1 mV.
The tangent approach defines the end of a T wave as a point where the amplitude of an analytical signal becomes smaller than 0.1 mV. The analytical signal is an extension of the original ECG signal into the complex numbers space, such as the following:
A(t)=x(t)+iH(x(t)) (3)
where H(x(t)) is a Hilbert transform of an original ECG signal x(t). A band-limited Hilbert transform (e.g., two different high-pass filters) can be used. Then, the amplitude of the analytical signal can be estimated for both representations, and the one with higher amplitude can be used for the end of T-wave calculations. The Q point can be defined as a point where amplitude of an analytical signal becomes larger than 0.1 mV. Again, a high-pass filter can be used after Hilbert transform (e.g., a high-pass filter with a cut-off frequency of 15 Hz).
It should be noted that using the tangent approach above can result in a significant reduction in the chances of underestimating the QT interval. The amplitude of the analytical signal will in general always be larger (in terms of absolute value) than the amplitude of the signal. Thus, using the tangent approach to defining the end of the T-wave can result in a few extra samples being considered part of the T-wave in some implementations. Moreover, using the tangent approach can result in consistent values being generated independent of QRS axis or an axis of a T-wave, due to the use of the analytical signal representation.
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