Computerized approach for cardiovascular risk level detection using photoplethysmography signals

Cardiovascular disease (CVD) is the leading cause of death globally. In order to decrease the medical cost for treating the heart related pathologies, this paper proposes a computer-aided diagnostic system to classify various risk level of cardiovascular disease utilizing inexpensive and non-obtrusive diagnostic tool called photoplethysmography (PPG). In this study, features such as singular value decomposition (SVD), statistical features and wavelets (Haar, Daubechies, and Symlet) are extracted from the photoplethysmography signals. These feature vectors are then applied to the softmax discriminant classifier (SDC) and Gaussian mixture model classifier (GMM) for classification of various risk phases of CVDs. The classification performance of the proposed model incorporating SDC with SVD and statistical feature vectors increases with sensitivity of 97.24%, specificity of 99.09% and an accuracy of 97.88%.The method presented in this paper assist cardiologists to validate their diagnosis.