Stress Classification Using K-means Clustering and Heart Rate Variability from Electrocardiogram
2021 ◽
Vol 14
◽
pp. 251-254
Keyword(s):
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 𝐑 − 𝐒𝐩𝐞𝐚𝐤 value. This method is expected to be applied to the U-healthcare field to help manage the mental health of people suffering from stress.
Keyword(s):
2005 ◽
Vol 104
(3)
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pp. 307-313
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Keyword(s):
2016 ◽
Vol 14
(2)
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pp. 196-201
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1995 ◽
Vol 18
(7)
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pp. 1401-1410
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Keyword(s):
2005 ◽
Vol 7
(3)
◽
pp. 195-202
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Keyword(s):
2006 ◽
Vol 39
(1)
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pp. 31-37
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