AI-based Models to Predict the Heart Rate Using PPG and Accelerometer Signals During Physical Exercise

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Ribeiro, L (Author) Other; Oliveira, HP (Author) FCUP; Hu, X (Author) Other; Pereira, T (Author) Other

Conference proceedings International

Title: Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Pages: 4398-4403

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Istanbul, 5 December 2023 through 8 December 2023

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: P-00Z-WKF

DOI: 10.1109/bibm58861.2023.10385871

Abstract (EN): PPG signal is a valuable resource for continuous heart rate monitoring; however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5