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A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection
Alessio Staffini1,2,3, Thomas Svensson1,4,5, Ung-Il Chung1,4,6
1Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Insights
This study introduces a new AI model using wearable device data to detect heart rate anomalies during sleep, improving cardiovascular disease risk assessment for users.
Area of Science:
- Artificial Intelligence
- Cardiovascular Health
- Wearable Technology
Background:
- Cardiovascular diseases (CVDs) are a leading global cause of death, with risk factors including lifestyle choices and conditions like diabetes.
- Wearable devices collect physiological data, such as heart rate, offering potential for CVD monitoring and prevention.
- Current wearable data often lacks immediate user comprehension of health risks, necessitating advanced analytics.
Purpose of the Study:
- To develop an unsupervised anomaly detection model for heart rate data from wearable devices.
- To enhance the interpretation of wearable-collected physiological data for early CVD risk identification.
- To provide users with more accessible and actionable health insights from their wearable devices.
Main Methods:
- Utilized a disentangled variational autoencoder (β-VAE) with a bidirectional long short-term memory (BiLSTM) network.
- Applied the model to unsupervised anomaly detection in heart rate data collected during sleep from eight participants.
- Tested the model using mean heart rate data sampled at 30-second and 1-minute intervals.
Main Results:
- The proposed β-VAE with BiLSTM backend demonstrated superior performance in detecting heart rate anomalies compared to other algorithms.
- The model's effectiveness was consistent across different participants and data sampling intervals.
- Outperformed established anomaly detection methods in most tested scenarios.
Conclusions:
- The developed AI model effectively detects anomalies in sleep heart rate data from wearables.
- Integration of such anomaly detection algorithms can significantly improve the utility of wearable devices for health monitoring.
- Wearable technology, enhanced with AI, can offer users clearer insights into potential cardiovascular risks.
Abstract:
Cardiovascular diseases (CVDs) remain a leading cause of death globally. According to the American Heart Association, approximately 19.1 million deaths were attributed to CVDs in 2020, in particular, ischemic heart disease and stroke. Several known risk factors for CVDs include smoking, alcohol consumption, lack of regular physical activity, and diabetes. The last decade has been characterized by widespread diffusion in the use of wristband-style wearable devices which can monitor and collect heart rate data, among other information. Wearable devices allow the analysis and interpretation of physiological and activity data obtained from the wearer and can therefore be used to monitor and prevent potential CVDs. However, these data are often provided in a manner that does not allow the general user to immediately comprehend possible health risks, and often require further analytics to draw meaningful conclusions. In this paper, we propose a disentangled variational autoencoder (β-VAE) with a bidirectional long short-term memory network (BiLSTM) backend to detect in an unsupervised manner anomalies in heart rate data collected during sleep time with a wearable device from eight heterogeneous participants. Testing was performed on the mean heart rate sampled both at 30 s and 1 min intervals. We compared the performance of our model with other well-known anomaly detection algorithms, and we found that our model outperformed them in almost all considered scenarios and for all considered participants. We also suggest that wearable devices may benefit from the integration of anomaly detection algorithms, in an effort to provide users more processed and straightforward information.
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