Open AccessStress Classification Using K-means Clustering and Heart Rate Variability from Electrocardiogram
Author(s) -
Mingu Kang,
Siho Shin,
Jaehyo Jung,
Youn Tae Kim
Publication year - 2021
Publication title -
international journal of biology and biomedical engineering
Language(s) - English
Resource type - Journals
ISSN - 1998-4510
DOI - 10.46300/91011.2020.14.32
Subject(s) - cluster analysis , stress (linguistics) , heart rate variability , signal (programming language) , pattern recognition (psychology) , set (abstract data type) , artificial intelligence , mental stress , heart rate , computer science , statistics , mathematics , medicine , blood pressure , philosophy , linguistics , programming language
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 − value. This method is expected to be applied to the U-healthcare field to help manage the mental health of people suffering from stress.