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research-article

ECG Stress Detection Model Based on Heart Rate Variability Feature Extraction

Authors:

Fangjie SongAuthors Info & Claims

HP3C '23: Proceedings of the 2023 7th International Conference on High Performance Compilation, Computing and Communications

Pages 184 - 188

https://doi.org/10.1145/3606043.3606069

Published: 16 November 2023 Publication History

Abstract

Stress usually causes changes in the status of the primary and secondary sympathetic nerves of the autonomic nervous system. This is also reflected in changes in indicators related to heart rate variability. Therefore, stress identification through the characteristics of heart rate variability is very effective. In this paper, Electrocardiographic Stress Detection (ESD) model based on heart rate variability is proposed to accurately identify stress from an objective perspective using physiological signals. Among them, the heart rate variability (HRV) in the ECG signal is the main feature involved in the ESD model, which is related to the response pattern of the human autonomic nervous system, and the changes of the relevant features represent the active state of the main sympathetic and parasympathetic nerves. Based on these patterns, we extracted the relevant HRV features and used a support vector machine (SVM) classifier for classification calculations with an accuracy of 94.8%.

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Digital Library

Cited By

Wang LHao JZhou TBi T(2024)Heart Disease Prediction Model based on HRV Emotional Features FusionProceedings of the 2024 8th International Conference on High Performance Compilation, Computing and Communications10.1145/3675018.3675773(71-75)Online publication date: 7-Jun-2024
https://dl.acm.org/doi/10.1145/3675018.3675773

Index Terms

ECG Stress Detection Model Based on Heart Rate Variability Feature Extraction
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection

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HP3C '23: Proceedings of the 2023 7th International Conference on High Performance Compilation, Computing and Communications

June 2023

354 pages

ISBN:9781450399883

DOI:10.1145/3606043

Copyright © 2023 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2023

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HP3C 2023

HP3C 2023: 2023 7th International Conference on High Performance Compilation, Computing and Communications

June 17 - 19, 2023

Jinan, China

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Cited By

Wang LHao JZhou TBi T(2024)Heart Disease Prediction Model based on HRV Emotional Features FusionProceedings of the 2024 8th International Conference on High Performance Compilation, Computing and Communications10.1145/3675018.3675773(71-75)Online publication date: 7-Jun-2024
https://dl.acm.org/doi/10.1145/3675018.3675773

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