Photoplethysmography (PPG) based healthcare devices have gained enormous interest in the detection of cardiac abnormalities. Limited research has been implemented for myocardial infarction (MI) detection. Moreover, PPG-based detection of angina is still a research gap. PPG signals are not always informative. Therefore, this research work presents the use of PPG signals and their second derivative to evaluate myocardial infarction and angina using a novel set of morphological features. The obtained morphological features are fed onto the feed-forward artificial neural network for the identification of the type of MI and unstable angina (UA). The initial experiments have been carried out on non-ambulatory (public) subjects for feature extraction and later evaluated on ambulatory (self-generated) databases. The intended method attains accuracy, sensitivity, and specificity of 98%, 97%, 98% on the public database and 94%, 94%, 94% on the self-generated database. The result shows that the proposed set of features can detect MI and UA with significant accuracy.

Foreign