Machine learning analysis of integrated ABP and PPG signals towards early detection of coronary artery disease.

Every year, Coronary Artery Disease (CAD) claims lives of over a million people. CAD occurs when the coronary arteries, responsible for supplying oxygenated blood to the heart, get occluded due to plaque deposits on their inner walls. The most critical fact about this disease is that it develops gradually over the years and by the time symptomatic changes such as angina or shortness of breath appear, the disease has already become severe. The overall aim of the proposed work is to detect CAD efficiently in its early stage while utilizing (radial) arterial blood pressure (ABP) along with photoplethysmogram (PPG) signals so that necessary clinical measures may be taken timely. To achieve this objective, firstly, ABP and PPG data of 73 CAD and 64 non-CAD (not suffering from any cardiac condition) subjects have been collected from MIMIC-II waveform database with matched subset. Secondly, the collected data is pre-processed using band pass filters having bandwidths of 2.5 to 16 Hz and 1.5 to 16 Hz for ABP and PPG respectively. Thirdly, nineteen features have been extracted from each of the two signals; some of the key features include mean of pulse duration, mean of rising slope and ratio of low frequency to high frequency. Finally, extensive analysis on CAD and non-CAD classification is carried out on the basis of extracted features while employing state-of-the-art classifiers such as support vector machines (SVM), K-nearest neighbors (KNN) and neural networks(NN). The numerical experiments have led to the interpretation that neural network outperforms other classifiers, claiming an accuracy of about 90%. Moreover, accuracy of the proposed approach is found to be better than the state-of-the-art works reported in literature where one of or combinations of cardiovascular signals, namely, electrocardiogram (ECG), phonocardiogram (PCG) and photoplethysmogram (PPG) have been utilized for the CAD detection.