Stress detection using ECG and EMG signals: A comprehensive study

Pourmohammadi, Sara; Maleki, Ali

doi:10.1016/j.cmpb.2020.105482

Cited by 129 publications

(83 citation statements)

References 37 publications

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“…It can be seen from Table 2 that feature #1 is one of the best performing features in each classifier and feature #1 is reduced in the ILFS state. Consistent with the literature results [7,35], ILFS produces physiological reactions, for example, excitement, a rapid heartbeat, an increased heart rate in the excited state, and a reduced average RR interval. Computational Intelligence and Neuroscience…”

Section: Feature Analysissupporting

confidence: 91%

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Recognition of the Impulse of Love at First Sight Based on Electrocardiograph Signal

Zhang

Yuan

et al. 2021

Computational Intelligence and Neuroscience

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The impulse of love at first sight (ILFS) is a well known but rarely studied phenomenon. Despite the privacy of these emotions, knowing how attractive one finds a partner may be beneficial for building a future relationship in an open society, where partners are accepting each other. Therefore, this study adopted the electrocardiograph (ECG) signal collection method, which has been widely used in wearable devices, to collect signals and conduct corresponding recognition analysis. First, we used photos to induce ILFS and obtained ECG signals from 46 healthy students (24 women and 22 men) in a laboratory. Second, we extracted the time- and frequency-domain features of the ECG signals and performed a nonlinear analysis. We subsequently used a feature selection algorithm and a set of classifiers to classify the features. Combined with the sequence floating forward selection and random forest algorithms, the identification accuracy of the ILFS was 69.07%. The sensitivity, specificity, F1, and area under the curve of the other parameters were all greater than 0.6. The classification of ECG signals according to their characteristics demonstrated that the signals could be recognized. Through the information provided by the ECG signals, it can be determined whether the participant possesses the desire to fall in love, helping to determine the right partner in the fastest time; this is conducive to establishing a romantic relationship.

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Section: Feature Analysissupporting

confidence: 91%

“…By summarizing previous studies on the ILFS [6,7,35], we consider that the ILFS exhibits the characteristics of high arousal, high price, high attractiveness, and high dominance.…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

Recognition of the Impulse of Love at First Sight Based on Electrocardiograph Signal

Zhang

Yuan

et al. 2021

Computational Intelligence and Neuroscience

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“…Moreover, the multivariate pattern analysis method can discover potential biomarkers based on multimodal physiological signals to distinguish patients from normal controls at the individual level and further highlight the physiological mechanism of PD behavioral symptoms. Recently, many researchers combine multimodal physiological data with machine learning methods and have obtained many valuable results [15][16][17][18]. Kyle Ross et al used ECG and galvanic skin response (GSR) signals to classify professional trauma patients and novices at a professional level and achieved an accuracy of 79.84% [15].…”

Section: Introductionmentioning

confidence: 99%

“…Rong L et al used EEG and ECG signals combined with the SVM model to classify cognitive load, which yielded an accuracy of 97.2% [16]. Sara et al used electromyogram (EMG) and ECG data combined with feature selection and machine learning algorithms to detect the psychological stress of healthy people, and the four levels of pressure recognition accuracy reached 96.2% [17]. Wei et al recognized emotions by using four modal physiological signals (EEG, ECG, respiratory (RA), and GSR) combined with a weight fusion strategy, successfully improved the accuracy rate of 74.52% in single-mode to 84.62% in multi-mode [18].…”

Section: Introductionmentioning

confidence: 99%

Diagnosing Parkinson’s Disease Using Multimodal Physiological Signals

Guo

Wang

et al. 2021

Communications in Computer and Information Science

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Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's disease. Due to the complex etiology and diverse clinical symptoms, it's difficult to accurately diagnose PD. In this study, we applied multimodal physiological signals, which include electroencephalography (EEG), electrocardiogram (ECG), photoplethysmography (PPG), and respiratory (RA), to classify PD and healthy control (HC) based on a multimodal support vector machine (SVM). Our experiments achieved an accuracy of 96.03%. Besides, we performed statistical analysis on the four types of physiological data of the PD group and the HC group. Results showed that the EEG of non-dementia PD patients had a significant decrease in high-frequency power, and the high-frequency energy distribution of the normalized PPG signal increased compared with HC. The current study suggests that combining the physiological information of multiple models and machine learning methods could improve the diagnosis accuracy of PD disease and be a potentially effective method of clinical diagnosis.

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“…DataLOG: this is a portable EMG signal collection and monitoring devices designed by Biometrics. It could be placed on the arm, the leg or waist for various fields studies like human performance, sports science, medical research, industrial ergonomics, gait laboratories, and educational settings [93]. [80][81][82][83]; AutoSense is used in [84][85][86]; SleepSense is used in [87,88]; BN-PPGED is used in [89]; Cardiosport TP3 is used in [90]; Q-sensor is used in [70]; Wahoo chest belt is used in [91]; BioHarness 3, Shimmer sensor, and MindWave mobile EEG headset are being used as an integrated system for stress monitoring in [92]; DataLOG is used in [93]; Device 1 is a EEG wearable sensor developed in Online Predictive Tools for Intervention in Mental Illness (PTIMI) project funded by European Union [94]; Device 2 is a noninvasive physiological sensor for stress assessment presented in [95]; Device 3 is used in [96] which they collect the EMG signals of the left trapezius muscle and then remove the contained ECG signal components.…”

mentioning

confidence: 99%

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

Chen

Abbod

Shieh

2021

Sensors

111

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Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin temperature) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue.

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Stress detection using ECG and EMG signals: A comprehensive study

Cited by 129 publications

References 37 publications

Recognition of the Impulse of Love at First Sight Based on Electrocardiograph Signal

Recognition of the Impulse of Love at First Sight Based on Electrocardiograph Signal

Diagnosing Parkinson’s Disease Using Multimodal Physiological Signals

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

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