Real-time multi-class signal quality assessment of photoplethysmography using machine learning technique

Abstract

Photoplethysmography (PPG) signal quality assessment (SQA) ensures improved measurements of various surrogate cardiovascular measurements like heart rate, SpO2, blood pressure, cardiac output and many more and as well reduces false alrams in ambulatory measurements. Although PPG SQA (PSQA) is a well researched area, but multiclass prediction of signal quality and its hardware implementation is limited. In this paper, a new non-segmenting approach for multiclass PSQA is presented with an optimal set of seven time-frequency domain features in conjuction with supervised classifiers to identify clean, partly clean and corrupted PPG data. The present work was carried out in three major stages- signal (dataset) acquisition, feature extraction and 3-class classification using random forest classifier. The technique was evaluated with volunteers’ data, MIMIC-III data from PhysioNet, CSL data, and combined data and achieved an overall average accuracy of 96.8% with good (0.98 or above) sensitivity, specificity, F1 score, precision and recall. On-device implementation of the proposed PSQA was accomplished using 7.2 h of PPG dataset on a quad-core Broadcom BCM 2837 controller with 1.2 GHz, supported by 1 GB RAM-based standalone device, with an accuracy of 97.09%. The latency and the peak memory requirement of the implementation was 0.81 s and 19 kB respectively to process 3 s PPG data. The present technique may be integrated as a part of a remote healthcare system using wearable sensors.