Key result
A support vector machine classifier using heart rate variability features achieved an overall recognition accuracy of 74.44% in distinguishing between meditative and stressed emotional states.
Why the study?
Stress monitoring using wearable connected devices enables psycho-physiological assessment to evaluate the relationship between variable stress levels and cardiovascular disease progression.
Observational (n=53)
An SVM classifier using HRV features can distinguish between meditative and stressed states with up to 87% accuracy, potentially enabling continuous psycho-physiological monitoring.
Answers are grounded in this paper and cite their sources.
Supports wearable stress monitoring for daily CVD psycho-physiological assessment; leaves open impact on management pending higher-level evidence.
The research programme reported in this paper is set within the framework of our research under the theme of ICT support for Active Healthy Ageing (AHA). This longitudinal empirical research is focused on the study of the impact on the management of cardiovascular disease if supported by sustained health monitoring using wearable connected devices. One of the key objectives is stress monitoring and its classification during the daily routine of life thus enabling psycho-physiological monitoring to study the correlation between emotional states including variable stress levels and the evolution and prognosis of cardiovascular disease. In this paper, the calibration phase will be studied in order to distinguish between two emotional states: i) meditation and ii) stress condition. For this, the Heart Rate Variability (HRV) features are used as extracted from the RR interval and a support vectors machine (SVM) classifier deployed which resulted in 74% and 87% recognition accuracy based on HRV data for the recognition of the two emotional states, namely meditative, and, stressed, respectively.
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Amira et al. (2019) conducted an observational in Healthy (n=53). Heart Rate Variability (HRV) monitoring vs. Meditation (rest condition) was evaluated on Recognition accuracy of emotional states (meditation vs stress). A support vector machine classifier using heart rate variability features achieved an overall recognition accuracy of 74.44% in distinguishing between meditative and stressed emotional states.
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