The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
In the last few decades, various electrical or electro-mechanical systems have been invented that assist the human physiological system. Some artificial systems like electrocardiogram (ECG), electroencephalograph (EEG), electromyogram (EMG), and stethoscope only collect physiologically relevant information for further analysis by human experts. Others like hearing aids and cochlear implants assist our physiological system by pre-processing input signals to compensate for a defect in the physiological system. In both mentioned artificial systems the processes involved are not controlled by the physiological system, nor do they control the physiological system. The third class of artificial systems are systems that control a physiological system by providing inputs to it and measuring its output to achieve a desired performance by the physiological system, which are referred to as “physiological system management” (PSM). The PSM systems can also collect, store and process physiological information. Examples of these systems are Cardiac Rhythm Management (CRM) systems, Epilepsy and Seizure prediction and control, and Automatic Drug Delivery (ADD) systems. The CRM systems, such as pacemakers and Implantable Cardioverter Defibrillators (ICDs), control the heart cardiac activity by monitoring and analyzing heart activity and providing device therapy in form of electrical stimulus when needed. The ADD systems also monitor one or more physiological parameter (such as blood glucose levels) and deliver proper drug therapy when necessary.
The process in the PSM generally starts with physiological information collection (sensing). The information might be directly processed to tune a set of physiological stimuli if needed. In some situations, the information might be transformed to some other form (such as frequency-domain, time-frequency domain, wavelet features) in order to simplify the decision-making and control process. As a result, certain features might also be extracted from the physiological information. The ultimate goal of the signal transformation and feature extraction is to achieve an optimal representation of the underlying physiological phenomena that is distinctively representative of each phenomenon as much as possible, and is also minimal in terms of computations and storage. Once proper features are obtained that efficiently discriminate various physiological phenomena (for example various causes of heart arrhythmia), a control mechanism can operate to generate proper therapy to the physiological system (like a pacemaking stimulus to the heart) and further continue the control process until the physiological system achieves the desired response.
Evidently, the processing in the PSM has to be on-line and in synchrony with the physiological system. Also, many types of PSM systems are implanted inside the human body, or are carried by wearer. As a result, power consumption and/or physical size pose serious constraints on most PSM systems. For example ICDs and pacemakers have to operate reliably inside the body with a minimum power for many years. This by itself severely limits the choice of signal processing, control strategy, and device therapy in the PSM systems. While complicated methods exist for off-line physiological signal processing, they might not be applicable to the PSM systems due to their demand on battery power, and physical size. In the meantime, it is crucial to adapt an optimal domain transformation and feature extraction methodology since the whole control strategy and system performance depends on proper signal representation. It is a major challenge in PSM design to come up with optimal signal representation given power and size constraints.
Various time-domain, frequency-domain, and time-frequency transformation methods have been proposed for PSM systems and for physiological signal processing in general.
Signal processing applied to ECG signal has been an active field of research for more than 30 years. Specially, QRS detection has been the main focus of the research. A through review of the research in this field is provided in [Ref. 5]. Afonso et al. [Refs. 6, 7, 8] discloses methods which are based on offline subband processing of the ECG signal after decomposition of the signal into subbands by a perfect reconstruction (PR) QMF filterbank.
In CRM systems, the input signals include ECG, heartbeat (in form of sound or pressure), and electrogram (EGM) obtained via intracardiac sensors. Often signals are converted to an electrical time-domain waveform. Human experts are able to analyze and interpret the waveforms to achieve a diagnosis. However, for an artificial system, direct interpretation is not often possible. Rather the signal has to be further analyzed to extract certain features that are understandable by the signal processing and control mechanism. This is termed signal transformation and feature extraction. For example, the ECG signal may be analyzed to extract the periodicity information, and detect patterns of P, QRS, and T waves from time-domain data. Alternatively, a frequency transformation followed by spectral analysis or a type of time-frequency analysis such as wavelet transform maybe employed to detect P, QRS, and T-waves and to analyze the periodicity information.
A normal cardiac rhythm sensed by EGM is composed of P-waves (due to atrial depolarization), R-wave (due to ventricular depolarization) and T-wave (due to ventricular repolarization). While various arrythmias might occur alone or together in the heart, it is essential to be able to (at least) distinguish between three classes: 1) arrythmias that are ventricular-based (such as ventricular tachycardia (VT) and ventricular fibrillation (VF)), and 2) arrythmias that are supra-ventricularly initiated such as atrial-based arrythmias (atrial tachycardia (AT) and atrial fibrillation (AF)) and generally supra-ventricular tachycardia (SVT), 3) Sinus Rhythm (SR) that could be high due to physical activity or stress. While VT and VF are potentially deadly and may be treated with defibrillation (powerful shock delivery), SVT is less dangerous and could be treated with cardioversion. Inaccurate detection of serious ventricular events will lead to improper device therapy (IDT). IDTs are mostly caused by sinus tachycardia, atrial flutter, and atrial fibrillation. Other causes include myopotentials (contraction of the upper thorax muscles and diaphragm) and T-wave oversensing [Refs. 1, 2, 3]. False detection of VT and VF leads to IDT that is painful to the patient and depletes the ICD battery power more quickly. IDT is also potentially harmful to the patient as it puts the patient at risk of device-induced VT (proarrhythmia) that might be dangerous and hard to detect by the ICD [Refs. 1, 2]. As a result, reduction of IDT is a serious issue facing the ICDs. On the other hand, improper classification of VT/VF as SVT could have deadly consequences due to lack of proper device therapy.
Also, recently device therapy for atrial-based arrythmias has become more common. Due to all of these, dual-chamber ICDs have been proposed that sense from both ventricular and atrial chambers and deliver simultaneous therapies to one or both of the two [Refs. 2, 3]. Moreover, four chamber ICDs have been used to treat the four chambers of the heart. It has been discussed on whether or not the dual chamber ICDs have led to improved performance in terms of sensitivity (whether an event such as VT/VF is detected all the time) and specificity (whether all the events detected as one such VT/VF were indeed the correct one). For example, US patent application 2004/0172067 A1, by Saba discloses a dual-chamber ICD for reduction of IDT as well as for improving the detection performance. On the other hand, T. Kurita et al. confirms the results of three other previous studies that compared to single-chamber ICDs, dual-chamber ICDs fail to reduce the IDTs [Ref. 4]. According to them, the rate of IDTs in modern devices remains at a range of 13%-15%. This is comparable to the 11%-25% rate of IDT reported in [Ref. 1] and their references.
Dual-chamber ICDs can achieve almost 100% correct VTNF detection (VT sensitivity). However, they face other problems such as atria double-counting due to far-field sensing (of R-waves by atrial sensor), atrial undersensing of AF, and ventriculoatria (VA) conduction (retrograde conduction from ventricle to atrium) in VT periods [Ref. 1]. Their sensitivity for supraventricular arrhythmias has been reported to be around 61% as compared to 79% for signal-chamber ICDs. Reference [Ref. 1] concludes that dual-chamber ICDs provide limited but not dramatic reduction in IDTs. As the technology is very complex and the number of available signals is increasing, more capable signal processing is needed. With current methods, correct programming of the dual-chamber ICDs is both difficult and time consuming [Ref. 1]. D. Pfeiffer et al. [Ref. 2] suggests that “over-defibrillation” might grow into a major problem for dual-chamber ICDs in the next few years.
To conclude, it is desirable to improve both the specificity and the sensitivity of event detection (specially VT/SVT/SR discrimination) in ICDs and to reduce IDT.
Many researches have done to detect various events related to the heart performance. Once one or more events such as various arrhythmia are detected, proper action is decided upon. Actions include device therapy such as cardioversion and shock delivery, drug delivery, recording heart activity, or sending warning signals to the patient or a device out of ones body. Early inventions disclosed methods that analyzed the time-domain EGM signals obtained from the intracardiac sensors. Processing methods for implantable anti-arrythmia devices is presented in the U.S. Pat. No. 5,545,186 by Olson et al. Usually time-domain periodicity and waveform morphology analysis is the method of choice for detection of arrhythmia. Various rules and algorithms are presented to event detection and decision making after detection. Morphologic analysis of the QRS complex (also called R-wave in EGM signal) is also suggested as in U.S. Pat. No. 5,447,519 by Peterson. There is evidence that the shape and width of the QRS complex are associated with distinct arrhythmia. Thus, many inventors have tried to exploit this for more accurate event detection. Peterson suggests methods based on discriminating between polymorphic and monomorphic QRS complexes. Time-domain methods that analyze the details of the EGM signal are disclosed in U.S. Pat. No. 5,957,857 by Hartley. Similar time-domain methods are present in U.S. Pat. No. 5,411,529 by Hudrlik.
US Patent application 2003/0204215 A1 by Gunderson et al. discloses remedies to an important issue facing the ICDs, i.e. the oversensing problem. Oversensing means detection of events other than the P-wave, R-wave, or T-wave that occur during the cardiac rhythm. Oversensing could be the result of cardiac or non-cardiac signals. In cardiac oversensing, R-waves or T-waves might be sensed twice (double counted as termed by Gunderson). In non-cardiac oversensing, signals of non-cardiac origin (noise due to myopotentials from muscles tissues, lead fracture and insulation failure, electromagnetic interferences (EMI), etc.) contaminate the cardiac signal and cause a false cardiac detection. In dual-chamber ICDs, far-field signals are potential problems. For example strong R-wave sensed at the right ventricular EGM (VEGM) might interfere with weaker P-waves sensed at the atrial EGM (AEGM) causing far-field R-wave oversensing. Gunderson et al. describe in detail the oversensing problem, and disclose methods of dealing with it, including morphology analysis through template-matching of the EGM signal to verify specific types of oversensing. It is desirable to develop methods of signal processing that are capable of reducing oversensing.
While time-domain and morphological processing of cardiac signals (both ECG and EGM) together with template-matching techniques have had limited success, researchers have been investigating alternative methods for performance improvement. Due to the complex time-varying nature of the cardiac signals, more complicated signal processing techniques have been applied to the signals. However, computation and memory demands of more advanced techniques have prohibited them from being implemented on implantable devices that have very limited computation and processing power available. As a result, most inventions have been limited to off-line processing of ECG or recorded EGM signals.
U.S. Pat. No. 5,109,862 by Kelen et al., discloses methods of employing short-time Fourier Transform (STFT) and Spectrograms to analyze ECG signals to be able to detect abnormalities in the heart and other physiological systems. U.S. Pat. No. 5,425,373 by Causey, discloses methods of off-line signal processing applied to EGM signals. Various signal processing strategies including spectral analysis have been disclosed.
U.S. Pat. No. 5,957,866, by Shapiro et al., discloses the use of short-term fast Fourier transform (FFT), wavelet transform, or any other time-frequency analysis applied to body sounds including the heart beat. Noticing the time-varying behavior of heart and the possibility of time-overlap of multiple cardiac events, U.S. Pat. No. 5,778,881, by Sun et al., discloses the use of Wavelet transform for feature extraction combined with hidden Markov models (HMM's) for modeling various heart events represented in the EGM signal. The methods of course are too complicated for low-resource implementation.
Complicated signal processing techniques have been applied in external defibrillators as disclosed in U.S. Pat. Nos. 6,263,238 B1 and 6,064,906. Methods include spectral analysis, coherence analysis, cepstral processing, FFT, Wavelet transform, and auto/cross-correlation analysis.
US patent application 2002/0058968 A1 by Sun et al., discloses methods that can use frequency domain analysis or correlation methods in an implantable device. They maintain an adaptive table of therapy results to be able to choose the most appropriate therapy from a library of various applied or designed therapies.
Due to the complexity and variability of EGM (and any cardiac) signal, cardiac event detection is a complicated task. Experts have realized that it makes sense to employ more complicated signal processing techniques including better feature extraction and improved modeling techniques for the task. As a result, more advanced signal processing methods have been proposed for cardiac event detection. The methods can be combined with traditional time-domain and morphology analysis techniques.
As explained, feature extraction, modeling, and event detection are complicated methods that have been applied to other signal processing applications such as audio, radar, sonar, and image processing. US patent application 2003/0013974 A1, by Natarajan et al., discloses methods of EGM signal processing to detect myocardial ischemia and/or infraction (MI/I). Various signal processing techniques such as spectral analysis, wavelet transform, and time-frequency analysis are proposed to detect MI/I conditions in the heart. US patent application 2004/0127945 A1, by Collins et al., discloses methods of signal processing for dual-chamber ICDs. The methods include wavelet transform for feature extraction, and cross-correlation for template matching. Finally, U.S. Pat. No. 6,434,417 B1, by Lovett, discloses methods of decomposing cardiac signals into subband components by orthogonal filters. Statistical features are extracted from subband signals and are used for event detection. The employ real-valued subband filters and process real-valued subband signals.
Parallel to development of signal processing for cardiac event detection and cardiac control, inventors have considered event detection and control of autonomic nervous system. The idea is based on the fact that the autonomic nervous system partially controls the heart activity (both rhythm and conduction). As a result, monitoring the heart rate variability (HRV) reveals the balance or imbalance of the autonomic system. Inversely, controlling (electrically or chemically) the autonomic nervous system affects the heart operation and might help prevent or stop cardiac arrhythmia. US patent application 2003/0181951 A1 by Cates, discloses methods that electrically stimulate the autonomic nervous system when an autonomic imbalance is predicted based on heart rate or HRV. While low frequency content of HRV is known to be due to both parasympathetic (or vagal) and sympathetic activity, high frequency content is mostly related to only parasympathetic activity. The ratio of the two frequency contents (LF/HF) thus can be used as a measure of autonomic balance. Cates discloses employment of interval analysis (between the R-waves) to measure the ratio.
US patent application 2004/0098061, by Armoundas et al., discloses methods that combine automatic drug delivery (ADD) with common ICD functions based on beat-to-beat variability in the morphology of the EGM signal.
As reviewed above, there are major problems in current CRM systems and specifically in ICDs and pacemakers that need to be addressed.
Accurate event detection in single-chamber and dual chamber ICDs is desirable. Methods should increase both sensitivity and specificity of detection, to be able to provide safe and reliable device therapy, while reducing IDT, over-sensing, and under-sensing as much as possible.
At the same time, it is desirable that methods of feature extraction, event detection, and decision making be simple enough to be implemented on ultra-low resource platforms such as implantable devices.
Processing delay of such methods should be maintained within acceptable ranges for CRM systems.
Feature extraction and event detection methods should provide robustness to various noises and interferences from internal (to human body) and external sources.
It is also desirable that feature extraction of cardiac signal be compatible with signal compression for signal recording and telemetry.
It is well known that epilepsy affects about 1% of the population in the industrialized countries [Refs. 9, 10, 11]. As such, major resources have been dedicated in detection and more importantly prediction of seizures. Upon positive prediction, various therapies such as device therapy, automatic drug delivery (ADD), or human intervention could be applied. Currently, epilepsy is analyzed based on electroencephalogram in various forms such as noninvasive scalp EEG, and invasive Intracranial EEG (IEEG) also known as electrocorticogram (ECoG).
A review of major methods of seizure detection is provided in references [Refs. 9, 10, 11]. To summarize, methods applied depend on how seizure is theorized to be generated and how the EEG signal is classified. The classic theory has been to present the EEG signal (obtained by multiple electrodes) as a multivariate random process. Accepting this view, linear signal processing methods such as autocorrelation-based (or equivalently Fourier-based and spectral methods) are applied to the EEG signal. The newer and more modern view has been to regard the EEG signal as a manifestation of a deterministic chaotic process, with a lower dimensionality during and around seizure periods. Based on this view, nonlinear signal processing and particularly those based on chaos theory are employed. However, most recently researchers [Refs. 9, 10, 11] increasingly avoid simple classification of seizure and its representation in the EEG domain as one of the mentioned two classes (random or deterministic chaotic). They believe while there is considerable nonlinearity in the EEG process, there are also random components in the signal. Also, there is no conclusive evidence to characterize the nonlinearity in the EEG signal as low-dimensional chaotic process. Still the nonlinear approaches seem to yield useful results provided that their limitations are taken into account [Ref. 12]. A thorough review of seizure prediction and control is provided in [Ref. 13]. As noted in [Ref. 13], effective prediction and control of epileptic seizures is an open research topic.
U.S. Pat. No. 5,743,860 by Hively et al. discloses methods of seizure detection based on nonlinear chaotic-based time series analysis such as correlation dimension and minimum mutual information. U.S. Pat. No. 6,549,804 by Osorio et al, discloses a combination on nonlinear filtering methods to perform real-time analysis of the EEG signal for seizure prediction. U.S. Pat. No. 6,735,467 B2 by Wilson, discloses methods of seizure detection based on neural networks and clustering algorithms. U.S. Pat. No. 6,061,593 by Fischell et al. discloses methods seizure prediction based on d-c shift detection in the EEG signal. The method is geared towards an implantable device for epilepsy control. They further elaborate on their implantable device in U.S. Pat. No. 6,473,639 B1.
It is therefore desirable to provide a system and method which is applicable to a wide range of physiological systems to efficiently and appropriately manage the physiological systems.
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