Surgery – Diagnostic testing – Cardiovascular
Reexamination Certificate
2000-01-31
2002-05-21
Nasser, Robert L. (Department: 3736)
Surgery
Diagnostic testing
Cardiovascular
C600S500000, C600S300000
Reexamination Certificate
active
06390986
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention generally relates to a method for screening individuals for autonomic neuropathy (AN), a complication of diabetes mellitus, using cepstral encoding of heart rate (HR) signals. More specifically, the present invention relates to a method for screening individuals for AN using linear predictor-derived cepstral encoding of HR signals obtained from individuals in a supine position.
2. Related Art
Once diabetic AN becomes clinically evident, the estimated 5-year mortality is approximately 50%. Thus, the early detection of autonomic dysfunction is important for effective therapeutic intervention. Thorough diagnosis and evaluation requires a patient history and neurologic examination, that can include nerve conduction studies, needle EMG measurements, vibratory perception thresholds, and a widely accepted battery of cardiovascular function tests evaluated as the Ewing score. Specialized electronic devices also offer a means for clinical assessment. Other indicators are typically used for standard clinical assessment, including impaired vision (retinopathy), and numbness or tingling of the extremities (impaired circulation). However, it would be desirable to employ an accurate, non-invasive method for detecting AN in diabetics. Works of others in this and related areas include the following:
Patterns found in the surface electrocardiogram (ECG) have been studied extensively for the purpose of classifying abnormal waveform profiles. Automated ECG classification has been successfully implemented in clinical practice with useful results for screening, diagnosis, and monitoring. Abenstein, “Algorithms for Real Time Ambulatory ECG Monitoring,” Biomed Sci Instrum., 14:73-79 (1978). Representation of the ECG pattern has included Fourier analysis, complex cepstrum, and the autoregressive (AR/ARMA) model. Murthy, et al., “Homomorphic Analysis and Modeling of ECG Signals,” IEEE Trans. Biomed Eng., BME-26(5):330-344 (1979), Mukhopadhyay, et al., “Parametric modeling of ECG Signal,” Med. Biol. Eng. Comput., 4:171-173(1996). Classification approaches have included frequency analysis, template matching cluster analysis, and most recently, neural networks. Silipo, et al., “Neural and Traditional Techniques in Diagnostic ECG Classification, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing—Proceedings, 1:123-126 (1997).
Curcie, et al., “Recognition of Individual Heart Rate Patterns with Cepstral Vectors,” Biological Cybernetics, Vol. 77, pages 103-109 (1997), shows that LP-derived cepstral coefficients can be used in a weighted nearest-mean classifier to successfully discriminate HRV (heart rate variability) tachograms for individuals, and that cepstral distance can be used to discriminate between normal and cardiac patient groups.
Bannister, “Autonomic Failure: A Textbook of Clinical Disorders of the Autonomic Nervous System”, (1988), Oxford/New York, Oxford University Press, reported that, as a complication of diabetes mellitus, AN is characterized by widespread degeneration of the small nerve fibers of both the sympathetic and parasympathetic tracts.
Stys, et al., “Current Clinical Applications of Heart Rate Variability”, Clinical Cardiology, Vol. 21, No. 10, pages 719-724, (1998), noted that spectral analysis of heart rate variability is widely considered to be a reliable noninvasive test for quantitative clinical assessment of cardiac-autonomic regulation.
The onset of cardiac AN, such as that encountered by long term diabetics, has been shown to reflect reductions in energies in both the LF (low frequency) and HF (high frequency) spectral bands, and total power compared to controls, Freeman, et al., “Spectral Analysis of Heart Rate in Diabetic Autonomic Neuropathy”, Archives of Neurology Vol. 48, Pages 185-190 (1991); Weise et al., “Age Related Changes in Heart Rate Power Spectra in Diabetic Man During Orthostasis”, Diabetes Research and Clinical Practice, Vol. 11, pages 23-32 (1991); Bellavere, et al., “Power Spectral Analysis of Heart-Rate Variations Improves Assessment of Diabetic Autonomic Neuropathy,” Diabetes, Vol. 41, pages 633-640 (1991); and Howorka,et al., “Optimal Parameters of Short-Term Heart Rate Spectrogram for Routine Evaluation of Diabetic Cardiovascular Autonomic Neuropathy,” Journal of Autonomic Nervous System, Vol. 69, No. 2-3, pages 164-172 (1998).
The reduced change in autonomic balance during orthostatic load, measured via spectral band indices, is a clinical indicator of AN and dysautonomia. In studies, the increase in LF and LF/HF and the decrease in HF produced by tilt were found to be significantly lower in diabetics, than in controls, Pagani, et al., “Spectral Analysis of Heart Rate Variability in the Assessment of Autonomic Diabetic Neuropathy,” Journal of the Autonomic Nervous System, Vol. 23, No. 2, pages 143-153 (1998); Lagi, et al., “Power Spectrum Analysis of Heart Rate Variations in the Early Detection of Diabetic Autonomic Neuropathy, Clinical Autonomic Research, Vol. 4, No. 5 , pages 245-248 (1994). Abnormal autonomic response to tilt in diabetics with a higher severity of cardiac AN has been shown to include a decrease in LF/HF ratio, Pagani, et al.
Signal classification techniques based on HRV indices have recently been applied to investigating HRV. Raymond et al., “Classification of Heart Rate Variability in Patients with Mild Hypertension,” Australasian Physical and Engineering Sciences in Medicine, Vol. 20, No. 4, pages 207-213 (1997), used spectral and time domain indices during rest and isometric handgrip as features in a Bayesian classifier to detect hypertension. Raymond et al. “Visualization of Heart Rate Variability Data Using Topographic Mappings, Computers in Cardiology,” (1998), further used topographic mapping of HRV log spectral distance measures to demonstrate a clustering effect corresponding to both tilt, and the presence or absence of beta blockade in healthy subjects.
Prognostic classifiers offer a potentially powerful clinical tool for identifying the onset of cardiac neuropathy. Curcie et al., Heart Rate Complexity as a Diagnostic of Autonomic Neuropathy from Insulin Dependent Diabetes Mellitus,” PACE, April, (1998) showed that fractal dimension, a nonlinear measure of signal complexity, could be used on supine HR records to predict the outcome of tilt in diabetics, a possible indicator of AN, as measured by the change in the LF/HF index.
The onset of cardiac autonomic neuropathy progresses independently from somatic neuropathy, and though no connection was found between power spectrum analysis and somatic neuropathy, spectral indices are sensitive enough to detect cardiac autonomic neuropathy in diabetics where standard methods sometimes fail. Thus, an encoding and classification method capable of comprehensively featuring relevant spectral information would be useful for the early detection of diabetes-induced cardiac AN.
None of the work by others has resulted in a method for screening diabetics for AN using cepstral vector analysis of heart rate signals. The method of the present invention demonstrates that the LP-cepstral discriminant classifier is useful and reliable for quick, noninvasive assessment (screening) of neuropathy in diabetics, while avoiding the stresses to the patient that are associated with tilt table testing.
OBJECTS AND SUMMARY OF THE INVENTION
A principal object of the present invention is the provision of a method for detecting AN in patients.
Another object and advantage of the present invention is the provision of a method for detecting AN in patients using heart rate variability indications from the patient and from a control group.
Even another object and advantage of the present invention is the provision of a method for detecting AN which encodes the heart rate signals with linear-predictive cepstral encoding to create heart rate cepstral vectors (HRCV).
Still another object and advantage of the present invention is the provision of a method in which cepstral encoding of HRV provides a new identifi
Craelius William
Curcie David J.
Nasser Robert L.
Rutgers The State University of New Jersey
Wolff & Samson
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