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Classification of coronary artery diseased and normal subjects using multi-channel phonocardiogram signal

Samanta Pranab, Pathak Akanksha, Mandana Kayapanda, Saha Goutam

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 2

426--443

Coronary artery disease (CAD) is one of the leading causes of mortality and morbidity. There is a need to develop a simple, reliable, and non-invasive screening tool to diagnose CAD. Prior studies reported that turbulent blood flow due to stenosed coronary arteries causes weak CAD murmurs. Analysis of phonocardiogram (PCG) signals can be useful to detect these murmurs. In this work, we propose a new multi-channel PCG-based system to classify CAD-affected and normal subjects, and it does not require any additional reference signal, such as an electrocardiogram (ECG) signal. The proposed system simultaneously acquires PCG signals from four different auscultation sites on the chest. It extracts five different features from time and frequency domains of the PCG signals. The two-class classification is done in a machine learning framework by employing an artificial neural network (ANN) classifier. The classification performances are evaluated for each channel as well as for their combinations. Experimental results show that the proposed sub-band-based spectral features perform well for both clean and noisy data. An accuracy of 82.57% is obtained using the combination of the signals acquired from tricuspid, mitral, and midaxillary regions. The multi-channel system gives more than 4% relative improvement over the best performance obtained by its single-channel counterpart, and the proposed features outperform earlier used features.

Computer-aided obstructive sleep apnea screening from single-lead electrocardiogram using statistical and spectral features and bootstrap aggregating

Hassan A. R., Haque M. A.

Biocybernetics and Biomedical Engineering

2016

Vol. 36, no. 1

256--266

Automatic sleep apnea screening is important to alleviate the onus of the physicians of analyzing a large volume of data visually. Again, the push towards low-power, portable and wearable sleep quality monitoring systems necessitates the use of minimum number of recording channels to enhance battery life. So, there is a dire need of an automated apnea detection scheme based on single-lead electrocardiogram (ECG). Most of the existing works are based on multiple channels of physiological signals or yield poor performance. The effect of various classification models on algorithmic performance is also poorly explored. In the present work, we propose a statistical and spectral feature based sleep apnea identification scheme that utilizes single-lead ECG signals. Bootstrap aggregating is employed to perform classification. The efficacy of the selected features is demonstrated by intuitive, statistical and graphical analyses. Optimal choices of classifier parameters are also expounded. The performance of the proposed algorithm is evaluated for various classifiers. The performance of our method is also compared to that of the state-of-the-art ones. The proposed method yields accuracy, sensitivity and specificity of 85.97%, 84.14% and 86.83% respectively on a widely used benchmark data-set. Experimental findings backed by statistical and graphical analyses suggest that the proposed method performs better than the existing ones in terms of accuracy, sensitivity, specificity and computational cost.