Key result
A stress classification algorithm using k-means clustering and heart rate variability from ECG signals achieved an accuracy of 85.07%.
Why the study?
The study aimed to develop a method to classify stress states using electrocardiogram signals to aid mental health management in U-healthcare.
A novel algorithm using k-means clustering and ECG-derived R-S peak values achieved 85.07% accuracy in classifying physiological stress states.
Hypothesis-generating ECG stress classifier via k-means; leaves open clinical validation and utility.
In this study, we propose a method to classify individuals under stress and those without stress using k-means clustering. After extracting the R and S peak values from the ECG signal, the heart rate variability is extracted using a fast Fourier transform. Then, a criterion for classifying the ECG signal for the stress state is set, and the stress state is classified through k-means clustering. In addition, the stress level is indicated using the π β ππ©πππ€ value. This method is expected to be applied to the U-healthcare field to help manage the mental health of people suffering from stress.
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Kang et al. (2021) studied Stress (n=30). K-means clustering using R-S peak values and HRV from ECG vs. No stress state was evaluated on Accuracy of stress classification. A stress classification algorithm using k-means clustering and heart rate variability from ECG signals achieved an accuracy of 85.07%.
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