Objective: Recent advances in wearable technologies and signal processing have made it possible to perform health monitoring during everyday life activities. Despite the fact that new technologies allow the storage of large volumes of data on small devices, limitations remain when data have to be transmitted or processed with devices with both energy and computational constraints. Approach: This work focuses on the implementation and validation of a photoplethysmogram (PPG) low- complexity analysis method for sensors that acquire a compressed PPG signal through Compressive Sensing (CS) and allows for the accurate detection of the PPG systolic peak in the compressed domain. Three public datasets were used consisting of a total of about 52 hours of PPG signals from 600 patients with normal and abnormal rhythms. Peaks were manually annotated by experts or derived from the annotated synchronized ECG. Main Results: The proposed method achieved a pooled average F1 measure on the three datasets of 91±8% for a 5% compression ratio (CR), 89±10% for CR=70% and 82±12% for CR of 90%. The pooled average F1 measure on the original uncompressed data using an offline open source peak detector is F1 = 91±11%. The proposed method is up to ∼100 times faster with respect to methods using decompression followed by peak detection. Significance: Results demonstrate that it is possible to achieve detection performance, in terms of the F1 measure, comparable with those obtained on the original uncompressed and filtered signal, making the proposed approach appropriate for real-time wearable systems with energy and computation constraints.
