Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals

• Published in: Knowledge-based systems 2015-07, Vol.82, p.1-10
• Main Authors: Patidar, Shivnarayan, Pachori, Ram Bilas, Rajendra Acharya, U.
• Format: Article
• Language: English
• Subjects: Centered correntropy; Classification; Coronary artery disease; Heart rate signals; Least squares support vector machine; Tunable-Q wavelet transform
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2015.02.011

Coronary artery disease (CAD) is the narrowing of coronary arteries leading to inadequate supply of nutrients and oxygen to the heart muscles. Over time, the condition can weaken the heart muscles and may lead to heart failure, arrhythmias and even sudden cardiac death. Hence, the early diagnosis of CAD can save life and prevent the risk of stroke. Electrocardiogram (ECG) depicts the state of the heart and can be used to detect the CAD. Small changes in the ECG signal indicate a particular disease. It is very difficult to decipher these minute changes in the ECG signal, as it is prone to artifacts and noise. Hence, we detect the R peaks from the ECG and use heart rate signals for our analysis. The manual inspection of the heart rate signals is time consuming, taxing and prone to errors due to fatigue. Hence, a decision support system independent of human intervention can yield accurate repeatable results. In this paper, we present a new method for diagnosis of CAD using tunable-Q wavelet transform (TQWT) based features extracted from heart rate signals. The heart rate signals are decomposed into various sub-bands using TQWT for better diagnostic feature extraction. The nonlinear feature called centered correntropy (CC) is computed on decomposed detail sub-band. Then the principal component analysis (PCA) is performed on these CC to transform the number of features. These clinically significant features are subjected to least squares support vector machine (LS-SVM) with different kernel functions for automated diagnosis. The experimental results demonstrate better classification accuracy, sensitivity, specificity and Matthews correlation coefficient using Morlet wavelet kernel function with optimized kernel and regularization parameters. Also, we have developed a novel CAD Risk index using significant features to discriminate the two classes using a single number. Our proposed methodology is more suitable in classification of normal and CAD heart rate signals and can aid the clinicians while screening the CAD patients.