Stress detection using ECG and EMG signals: A comprehensive study. (September 2020)

• Record Type: Journal Article
• Authors: Pourmohammadi, Sara; Maleki, Ali
• Is Part Of: Computer methods and programs in biomedicine. Volume 193(2020)
• Publication Date: 2020-09
• Subjects: Electrocardiogram -- Electromyogram -- Erector spinae muscle -- Heart rate variability -- Multi-level stress detection -- Trapezius muscle -- Stress-inducing protocol; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105482
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An efficient stress detection method has been developed based on EMG and ECG signals. A comparison between the performance of EMG and ECG for multi-level stress detection has been represented. The accuracies of stress recognition for two, three and four levels obtained as 100%, 97.3%, and 95.4 %, respectively. Abstract: Background and Objective: In recent years, stress and mental health have been considered as important worldwide concerns. Stress detection using physiological signals such as electrocardiogram (ECG), skin conductance (SC), electromyogram (EMG) and electroencephalogram (EEG) is a traditional approach. However, the effect of stress on the EMG signal of different muscles and the efficacy of combination of the EMG and other biological signals for stress detection have not been taken into account yet. This paper presents a comprehensive review of the EMG signal of the right and left trapezius and right and left erector spinae muscles for multi-level stress recognition. Also, the ECG signal was employed to evaluate the efficacy of EMG signals for stress detection. Methods: Both EMG and ECG signals were acquired simultaneously from 34 healthy students (23 females and 11 males, aged 20-37 years). Mental arithmetic, Stroop color-word test, time pressure, and stressful environment were employed to induce stress in the laboratory. Results: The accuracies of stress recognition in two, three and four levels were 100%, 97.6%, and 96.2%, respectively, obtained from the distinct combination of feature selection and machine learning algorithms. Conclusions: The comparison of stress detection accuracies resulted from EMG and ECG indicators demonstrated the strong ability and the effectiveness of EMG signal for multi-level stress detection.