Correlation-Boosted Ensemble Local Patterns for Photoplethysmographic Signal Quality Classification

Photoplethysmography (PPG) is a key component in a myriad of continuous and non-invasive health monitoring solutions, increasingly widespread in wearable devices, such as smartwatches and smart rings. Its high susceptibility to noise, such as motion artifacts and ambient light interference, however, can significantly hinder the learning process, as well as the resulting performance, of the deployed models. Given that, a Signal Quality Assessment (SQA) auxiliary module is usually employed in such applications, for upfront selection of the PPG segments that should be used for reliable extraction of physiological information from the user. Most SQA strategies adopted in these devices rely either on (1) Deep Learning (DL) models, capable of obtaining high performance metrics despite being of increased complexity and energy consumption, or (2) Decision Rule-based strategies that can assess signal quality in an increasedly energy-efficient way, albeit with reduced robustness. In this work, we introduce Hexa-SymmLTP-CC, a novel signal quality classifier composed of an ensemble of Local Pattern-based feature extractors followed by a downstream linear binary classifier, which outperforms state-of-the-art solutions, achieving accuracies of 93.93%, 96.06% and 96.55% across three clinical expert-annotated smartwatch and smart ring PPG datasets, while respecting the lightweight restrictions for wearable-based real-time monitoring applications.