Key result
A convolutional neural network detected acute cognitive stress from 10-second electrocardiogram windows with an error rate of 17.3%, significantly outperforming conventional heart rate variability methods (p < 0.01).
Why the study?
Stress is linked to mental and physical health issues, but accurate and fast detection of acute cognitive stress using automatic feature learning from heart rate variability remains needed.
Does a CNN-based method improve the real-time detection of acute cognitive stress from ECG signals compared to conventional HRV methods in healthy participants?
Does a CNN-based method improve the real-time detection of acute cognitive stress from ECG signals compared to conventional HRV methods in healthy participants?
A CNN-based method using a 10-second ECG window significantly reduces the error rate in detecting acute cognitive stress compared to conventional HRV methods, enabling real-time monitoring.
Answers are grounded in this paper and cite their sources.
Should not yet guide clinical stress monitoring; leaves open prospective validation of CNN-based ECG detection.
As stress is related to many mental and physical health problems, monitoring stress and its management is getting increasingly important in modern societies. Because of the advantage of convolutional neural network (CNN) in automatic feature learning, this study is proposed to use CNN to achieve accurate and fast detection of acute cognitive stress from heart rate variability (HRV). The traditional mental arithmetic calculation was adopted as the stressor for a total of twenty participants, during which one-lead electrocardiogram (ECG) was acquired. Six conventional HRV methods for inferring cognitive stress were extracted from the ECG signals, and their performance in identifying acute cognitive stress was compared with the proposed CNN-based method. The experimental results showed that with a super-short (10 s) time window, the detection error rate of CNN was 17.3%, which is significantly better than the performance of all six conventional HRV methods (> 7.2%, p <; 0.01). Further analysis showed that the improvement achieved by the proposed CNN methods mainly came from the decrease in false stress sample detection. This study demonstrated the possibility of super-short windows and the advantage of CNN on acute cognitive stress detection. Its outcome would benefit practical applications of real-time stress detection via HRV.
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He et al. (2019) studied Acute cognitive stress (n=20). Convolutional Neural Network (CNN) based stress detection vs. Conventional heart rate variability (HRV) methods was evaluated on Detection error rate (ER) of acute cognitive stress (7.2% absolute reduction in error rate, p=<0.01). A convolutional neural network detected acute cognitive stress from 10-second electrocardiogram windows with an error rate of 17.3%, significantly outperforming conventional heart rate variability methods (p < 0.01).
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