Utility of non-dimensional feature analysis of the PPG signal for automated screening of myocardial infarction (MI)

These days, the manifold wearable attributes of the photoplethysmogram (PPG) signal acquired via optical means have been proven to be successful for the primary and rapid detection of myocardial infarction (MI) conditions. However, the available, limited set of state-of-the-art PPG-based methods is mostly found to be flawed, either owing to their procedural intricacy, validation over insufficient datasets, or quantification of the outcome in a partial manner. In this research, MI-induced variation is indicated via a unique set of non-dimensional features extracted from the normalized PPG first derivative (FDPPG) segment without utilizing fiducial point detection. This simple set of extracted features that has been popularly used for machine fault diagnosis applications is, in fact, adopted in this research for the first time to categorize between normal and MI subjects via a simple logistic regression classifier. The robust and superior performance of the proposed method can be seen from its mean detection accuracy, sensitivity, and specificity of 97.58 %, 96.77 %, and 98.39 % tested on 62 normal and 62 admitted MI subjects. In view of the available up-to-date research, the methodological simplicity and superior classification accuracy of the proposed method present immense promises for suitable cardiac monitoring applications.