Enhanced premature ventricular contraction pulse detection and classification using deep convolutional neural network.

Access to accurate and precise monitoring systems for cardiac arrhythmia could contribute significantly to preventing damage and subsequent heart disorders. The present research concentrates on using photoplethysmography (PPG) and arterial blood pressure (ABP) with deep convolutional neural networks (CNN) for the classification and detection of fetal cardiac arrhythmia or premature ventricular contractions (PMVCs). The framework for the study entails (Icentia 11k) a public dataset of ECG signals consisting of different cardiac abnormalities. Following this, the weights obtained from the Icentia 11k dataset are transferred to the proposed CNN. Finally, fine-tuning was carried out to improve the accuracy of classification. Results obtained showcase the capacity of the proposed method to detect and classify PMVCs into three types: Normal, P1, and P2 with an accuracy of 99.9%, 99.8%, and 99.5%.

Smart automated heart health monitoring using photoplethysmography signal classification.

This paper proposes a smart, automated heart health-monitoring (SAHM) device using a single photoplethysmography (PPG) sensor that can monitor cardiac health. The SAHM uses an Orthogonal Matching Pursuit (OMP)-based classifier along with low-rank motion artifact removal as a pre-processing stage. Major contributions of the proposed SAHM device over existing state-of-the-art technologies include these factors: (i) the detection algorithm works with robust features extracted from a single PPG sensor; (ii) the motion compensation algorithm for the PPG signal can make the device wearable; and (iii) the real-time analysis of PPG input and sharing through the Internet. The proposed low-cost, compact and user-friendly PPG device can also be prototyped easily. The SAHM system was tested on three different datasets, and detailed performance analysis was carried out to show and prove the efficiency of the proposed algorithm.