Efficient methods for acute stress detection using heart rate variability data from Ambient Assisted Living sensors
| Autor: | István Vassányi, Edit Schumacher, Benedek Szakonyi, István Kósa |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Ambient Intelligence
Computer science State–Trait Anxiety Inventory Biomedical Engineering Ambient Assisted Living Stroop colour word test Biomaterials 03 medical and health sciences Electrocardiography 0302 clinical medicine Heart Rate Trier social stress test Medical technology Heart rate variability Humans Radiology Nuclear Medicine and imaging Acute stress R855-855.5 Wearable sensor 030304 developmental biology Assisted living Stress detection 0303 health sciences Radiological and Ultrasound Technology business.industry Research Pattern recognition General Medicine 03.05. Egyéb orvostudományok Statistical classification Feature (computer vision) Artificial intelligence F1 score business 030217 neurology & neurosurgery State-Trait Anxiety Inventory Algorithms |
| Zdroj: | BioMedical Engineering OnLine, Vol 20, Iss 1, Pp 1-19 (2021) BioMedical Engineering |
| Popis: | Background Using Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations. This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices. Methods Features extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions. The accuracy and F1 score of the trained models were compared and analysed. Results The best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. Shorter overlapping windows had slightly lower performance, with 91.62–94.55% accuracy and 91.77–94.55% F1 score ranges. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively. Conclusion The tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements. |
| Databáze: | OpenAIRE |
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