DEEP LEARNING APPROACH FOR HEART RATE PREDICTION USING PPG SIGNAL
Date
2023-04
Journal Title
Journal ISSN
Volume Title
Publisher
I.O.E. Pulchowk Campus
Abstract
Photoplethysmography (PPG) is a low-cost optical device that measures changes in blood
volume in the microvascular tissue bed from the skin’s surface. It has been used in commercial
medical devices to gauge peripheral vascular disease and autonomic function by monitoring
blood pressure, heart rate, and oxygen saturation. Due to the presence of motion artifact
during exercises, there arises difficulty in measuring heart rate from PPG signal. A machine
learning based approach is used to monitor heart rate (HR) using wrist-type photoplethysmography
(PPG) signals in this paper. By combining 1D CNN and a bidirectional LSTM,
the model get benefit from the strengths of both architectures, capturing both local and
long-term patterns in the input data. The proposed model exhibits average absolute error
of less than 1.5 bpm for all the training and test datasets. The model shows the promising
result with less than 300 thousands network parameters.
Description
Photoplethysmography (PPG) is a low-cost optical device that measures changes in blood
volume in the microvascular tissue bed from the skin’s surface. It has been used in commercial
medical devices to gauge peripheral vascular disease and autonomic function by monitoring
blood pressure, heart rate, and oxygen saturation.
Keywords
Sequence models,, Motion Artifact,, LSTM