Year: 2024
Type: article
Abstract: Consumer-grade heart rate (HR) sensors are widely used for tracking physical and mental health status. We explore the feasibility of using Polar H10 electrocardiogram (ECG) sensor to detect and predict cigarette smoking events in naturalistic settings with several machine learning approaches. We have collected and analyzed data for 28 participants observed over a two-week period. We found that using bidirectional long short-term memory (BiLSTM) with ECG-derived and GPS location input features yielded the highest mean accuracy of 69% for smoking event detection. For predicting smoking events, the highest accuracy of 67% was achieved using the fine-tuned LSTM approach. We also found a significant correlation between accuracy and the number of smoking events available from each participant. Our findings indicate that both detection and prediction of smoking events are feasible but require an individualized approach to training the models, particularly for prediction.
Source: PubMed
Authors: Hang Yu, Michael Kotlyar, Paul Thuras, Sheena Dufresne, Serguei Vs Pakhomov
Institution: University of Minnesota
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G235943272, G9000076473, G9000345003
OpenAlex ID: W4399317437