Abstract
Coronary artery disease (CAD) is a life-threatening condition that, unless treated at an early stage, can lead to congestive heart failure, ischemic heart disease, and myocardial infarction. Early detection of diagnostic features underlying electrocardiography signals is crucial for the identification and treatment of CAD. In the present work, we proposed novel entropy called Renyi Distribution Entropy (RdisEn) for the analysis of short-term heart rate variability (HRV) signals and the detection of CAD. Our simulation experiment with synthetic, physiological, and pathological signals demonstrated that RdisEn could distinguish effectively among different subject groups. Compared to the values of sample entropy or approximation entropy, the RdisEn value was less affected by the parameter choice, and it remained stable even in short-term HRV. We have developed a combined CAD detection scheme with RdisEn and wavelet packet decomposition (WPD): (1) Normal and CAD HRV beats obtained were divided into two equal parts. (2) Feature acquisition: RdisEn and WPD-based statistical features were calculated from one part of HRV beats, and student’s t-test was performed to select clinically significant features. (3) Classification: selected features were computed from the remaining part of HRV beats and fed into K-nearest neighbor and support vector machine, to separate CAD from normal subjects. The proposed scheme automatically detected CAD with 97.5% accuracy, 100% sensitivity and 95% specificity and performed better than most of the existing schemes.
Highlights
Plaque accumulation in the inner wall of the coronary arteries causes a blockage in the coronary circulation and the reduction of blood supply to the heart muscles, leading to coronary artery diseases (CAD) (Steinberg and Gotto, 1999)
To test the consistency and stability of the Renyi Distribution Entropy (RdisEn) measurement, we studied the impact of the changing parameter combinations on the RdisEn measurement, using synthetic, physiologic and pathological signals
We compared the performance of RdisEn to that of Approximate Entropy (ApEn), Sample Entropy (SamEn) and Distribution Entropy (DisEn)
Summary
Plaque accumulation (fatty and cholesterol substances) in the inner wall of the coronary arteries causes a blockage in the coronary circulation and the reduction of blood supply to the heart muscles, leading to coronary artery diseases (CAD) (Steinberg and Gotto, 1999). RdisEn for CAD Detection increase in CAD-related death is expected in emerging nations (Acharya et al, 2017c). CAD detection is the key to prevent further heart function damage and save lives. The exercise stress test (EST), which monitors various heart status features, is often used for CAD diagnosis. Not all CAD subjects can achieve the expected heart rate, and many patients may suffer cardiac arrest during EST (Román et al, 1998). Measurement of resting ECG signals can be applied as a non-invasive and preferred method for CAD diagnosis. Since no obvious change in the resting ECG signals is detected among ∼70% of CAD subjects, the manual CAD diagnosis is time-consuming and ineffective (Antanavicius et al, 2008). Computer-aided diagnostic technologies (CADT) for CAD detection have garnered increasing attentions for their ease of operation without the excessive reliance on the personal experience of a doctor, as well as their cost-effectiveness
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