Why the study?
Does Higher Order Spectral analysis combined with an Artificial neural network classifier improve the classification of heart rate variability signals in patients with CHF and MI compared to traditional techniques?
Does Higher Order Spectral analysis combined with an Artificial neural network classifier improve the classification of heart rate variability signals in patients with CHF and MI compared to traditional techniques?
Higher Order Spectral analysis combined with artificial neural networks can highly accurately classify heart rate variability signals to detect congestive heart failure and myocardial infarction.
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High-accuracy HOS-ANN classification may aid HRV-based detection of CHF and MI; extends traditional signal analysis techniques.
In this paper, the heart rate variability signals were utilized to automatically discriminate between subjects with normal sinus rhythm and patients with low heart rate variability such as those suffering from congestive heart failure (CHF) and myocardial infarction (MI) diseases. Traditional techniques and Higher Order Spectral analysis (HOS) were used to extract the main features from the HRV signals. Also, higher order spectra (HOS) estimators are the sum of the estimated bispectrum, bicoherence index and normalized bispectral entropy. An Artificial neural network classifier (ANN) was proposed to compare the classifier performance for automatically classifying the aforementioned diseases. Results have shown that using HOS parameters give high rates for classifying heart diseases. Classification rate reaches to 98.78%.
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Obayya et al. (2009) studied this question.
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