Machine Learning Algorithm Development and Metrics Extraction from PPG Signal for Improved Robustness in Wearables

Pedro Veiga, Pedro Veiga, Rui Varandas, Hugo Gamboa

2023

Abstract

Wearable devices application in the digital measurement of health has gained attention by researchers. These devices allow for data acquisition during real-life activities, resulting in higher data availability. They often include photoplethysmography (PPG) sensors, the sensor behind pulse oximetry which is a non-invasive method for continuous oxygen saturation measurements, an essential tool for managing patients undergoing pulmonary rehabilitation and an effective method for assessing sleep-disordered breathing. However, the current market focuses on heart rate measurements and lacks the robustness of clinical applications for SpO2 assessment. The most common obstacle in PPG measurements is the signal quality. Thus, in this work a solution was developed to evaluate the signal in three distinct qualities. A Random Forest classifier achieved accuracy scores of 79%, 80% for the models capable of differentiating between usable and unusable signals, and of 74% and 80% when distinguishing between optimal and suboptimal signals. Multi-class models achieved accuracy scores of 66% and 65%. Three clinically relevant metrics were also extracted from the PPG signal. The heart rate and respiratory rate algorithms resulted in performances similar to the ones found in the literature. However, while promising, more data is needed to reach statistical significance for the SpO 2 measurement.

Paper Citation

in Harvard Style

Veiga P., Varandas R. and Gamboa H. (2023). Machine Learning Algorithm Development and Metrics Extraction from PPG Signal for Improved Robustness in Wearables. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS; ISBN 978-989-758-631-6, SciTePress, pages 178-185. DOI: 10.5220/0011635900003414

in Bibtex Style

@conference{biosignals23,
author={Pedro Veiga and Rui Varandas and Hugo Gamboa},
title={Machine Learning Algorithm Development and Metrics Extraction from PPG Signal for Improved Robustness in Wearables},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={178-185},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011635900003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS
TI - Machine Learning Algorithm Development and Metrics Extraction from PPG Signal for Improved Robustness in Wearables
SN - 978-989-758-631-6
AU - Veiga P.
AU - Varandas R.
AU - Gamboa H.
PY - 2023
SP - 178
EP - 185
DO - 10.5220/0011635900003414
PB - SciTePress