Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal
Abstract
:1. Introduction
- (a)
- Introducing flexible dry electrodes based on wearable smart T-shirts to monitor researchers’ health.
- (b)
- An automatic system based on extracted features from ECG signals and demographic features for the detection of researchers’ mental stress.
- (c)
- Relationship between demographic and extracted features based on clustering technique.
- (d)
- Comparison between different machine learning classifiers to find a suitable classification method for the automatic mental stress detection system.
- (e)
- To achieve the average accuracy for the inter-subject (subject-wise) classification using the best performer classifier of the intra-subject (mental stress and normal) classification.
2. Wearable Smart T-Shirt
3. Materials and Methods
3.1. Experimental Design and Data Collection
3.2. Preprocessing and Features Calculation
3.3. Classification Techniques
3.4. Performance Evaluation of the Proposed System
3.5. Normalization Method
3.6. Statistical Analysis
4. Results and Discussion
4.1. Analysis of the Signal
4.2. Intra-Subject (Mental Stress and Normal) Classification Results of the Proposed System
4.3. Performance of the Inter-Subject Classification Using DT Classifier
4.4. Comparison between Proposed and Previously Selected Methods
4.5. Applications and Limitations of the Proposed Study
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Total No. of the Subjects (N) | 20 |
---|---|
Occupation of the Subjects | Research (Ph.D. and Research Associate) |
Conditions of the Subjects | Mental Stress and Normal |
Gender of the Subjects | All are Male |
The total duration of the recordings (T) | 1800 min |
The total duration of the mental stress recordings (TMF) | 1200 min |
The total duration of the normal recordings (TN) | 600 min |
Model | Classifier | Recall | Specificity | Precision | Accuracy | F1 | AUC |
---|---|---|---|---|---|---|---|
Leave one out | DT | 0.933 | 0.967 | 0.944 | 0.933 | 0.935 | 0.990 |
NB | 0.733 | 0.817 | 0.807 | 0.733 | 0.741 | 0.840 | |
RF | 0.900 | 0.800 | 0.913 | 0.900 | 0.895 | 1.000 | |
LR | 0.800 | 0.700 | 0.795 | 0.800 | 0.794 | 0.835 | |
10-fold | DT | 0.567 | 0.483 | 0.577 | 0.567 | 0.571 | 0.540 |
NB | 0.533 | 0.467 | 0.556 | 0.533 | 0.542 | 0.570 | |
RF | 0.633 | 0.467 | 0.607 | 0.633 | 0.614 | 0.530 | |
LR | 0.700 | 0.550 | 0.683 | 0.700 | 0.684 | 0.630 | |
3-fold | DT | 0.467 | 0.483 | 0.533 | 0.467 | 0.481 | 0.530 |
NB | 0.467 | 0.333 | 0.467 | 0.467 | 0.467 | 0.410 | |
RF | 0.633 | 0.467 | 0.607 | 0.633 | 0.614 | 0.435 | |
LR | 0.767 | 0.683 | 0.762 | 0.767 | 0.763 | 0.815 | |
2-fold | DT | 0.667 | 0.433 | 0.628 | 0.667 | 0.617 | 0.680 |
NB | 0.500 | 0.550 | 0.579 | 0.500 | 0.512 | 0.633 | |
RF | 0.633 | 0.667 | 0.689 | 0.633 | 0.644 | 0.692 | |
LR | 0.700 | 0.700 | 0.729 | 0.700 | 0.707 | 0.700 | |
Average | 0.664 | 0.597 | 0.679 | 0.664 | 0.661 | 0.676 | |
±Standard Deviation | 0.136 | 0.165 | 0.132 | 0.136 | 0.134 | 0.173 |
Model | Classifier | Recall | Specificity | Precision | Accuracy | F1 | AUC |
---|---|---|---|---|---|---|---|
Leave one out | DT | 0.933 | 0.967 | 0.944 | 0.933 | 0.935 | 0.990 |
NB | 0.733 | 0.817 | 0.807 | 0.733 | 0.741 | 0.840 | |
RF | 0.867 | 0.733 | 0.889 | 0.867 | 0.856 | 1.000 | |
LR | 0.867 | 0.833 | 0.867 | 0.867 | 0.867 | 0.880 | |
10-fold | DT | 0.567 | 0.483 | 0.577 | 0.567 | 0.571 | 0.550 |
NB | 0.467 | 0.433 | 0.512 | 0.467 | 0.481 | 0.495 | |
RF | 0.633 | 0.467 | 0.607 | 0.633 | 0.614 | 0.573 | |
LR | 0.733 | 0.617 | 0.723 | 0.733 | 0.725 | 0.685 | |
3-fold | DT | 0.500 | 0.400 | 0.512 | 0.500 | 0.505 | 0.438 |
NB | 0.400 | 0.450 | 0.481 | 0.400 | 0.411 | 0.440 | |
RF | 0.567 | 0.433 | 0.556 | 0.567 | 0.561 | 0.418 | |
LR | 0.733 | 0.667 | 0.733 | 0.733 | 0.733 | 0.790 | |
2-fold | DT | 0.667 | 0.433 | 0.628 | 0.667 | 0.617 | 0.680 |
NB | 0.500 | 0.600 | 0.608 | 0.500 | 0.505 | 0.537 | |
RF | 0.633 | 0.617 | 0.664 | 0.633 | 0.642 | 0.737 | |
LR | 0.700 | 0.700 | 0.729 | 0.700 | 0.707 | 0.730 | |
Average | 0.656 | 0.603 | 0.677 | 0.656 | 0.654 | 0.673 | |
±Standard Deviation | 0.148 | 0.166 | 0.138 | 0.148 | 0.145 | 0.185 |
Subject | Recall | Specificity | Precision | Accuracy | F1 | AUC |
---|---|---|---|---|---|---|
Normal 1 | 0.966 | 0.984 | 0.969 | 0.966 | 0.966 | 0.997 |
Normal 2 | 0.966 | 0.984 | 0.969 | 0.966 | 0.966 | 0.994 |
Normal 3 | 0.931 | 0.969 | 0.944 | 0.931 | 0.933 | 0.989 |
Normal 4 | 0.931 | 0.969 | 0.944 | 0.931 | 0.933 | 0.989 |
Normal 5 | 0.931 | 0.969 | 0.944 | 0.931 | 0.933 | 0.989 |
Normal 6 | 0.931 | 0.969 | 0.944 | 0.931 | 0.933 | 0.989 |
Normal 7 | 0.931 | 0.969 | 0.944 | 0.931 | 0.933 | 0.989 |
Normal 8 | 0.966 | 0.984 | 0.969 | 0.966 | 0.966 | 0.994 |
Normal 9 | 0.931 | 0.969 | 0.944 | 0.931 | 0.933 | 0.989 |
Normal 10 | 0.931 | 0.969 | 0.944 | 0.931 | 0.933 | 0.989 |
Mental Stress 1 | 0.964 | 0.980 | 0.968 | 0.964 | 0.965 | 0.997 |
Mental Stress 2 | 0.929 | 0.960 | 0.940 | 0.989 | 0.930 | 0.989 |
Mental Stress 3 | 0.929 | 0.916 | 0.929 | 0.929 | 0.929 | 0.986 |
Mental Stress 4 | 0.929 | 0.960 | 0.940 | 0.929 | 0.930 | 0.989 |
Mental Stress 5 | 0.929 | 0.916 | 0.929 | 0.929 | 0.929 | 0.986 |
Mental Stress 6 | 0.929 | 0.960 | 0.940 | 0.929 | 0.930 | 0.989 |
Mental Stress 7 | 0.929 | 0.960 | 0.940 | 0.929 | 0.930 | 0.989 |
Mental Stress 8 | 0.929 | 0.960 | 0.940 | 0.929 | 0.930 | 0.986 |
Mental Stress 9 | 0.929 | 0.960 | 0.940 | 0.929 | 0.930 | 0.989 |
Mental Stress 10 | 0.964 | 0.980 | 0.968 | 0.964 | 0.965 | 0.997 |
Average | 0.938 | 0.964 | 0.947 | 0.941 | 0.939 | 0.990 |
±Standard Deviation | 0.015 | 0.018 | 0.012 | 0.018 | 0.014 | 0.003 |
Reference | Year | Subject | Recording Device | Signal | Accuracy |
---|---|---|---|---|---|
Huang et al. [105] | 2018 | 35 | ADS1292R | ECG | 75.50 |
Xiao et al. [31] | 2018 | 5 | Emotiv EPOC | EEG | 77.12 |
Son et al. [106] | 2018 | 32 | AgCl Electrodes | EEG, EOG, EMG | 72.00 |
Hasan et al. [107] | 2019 | EEG | 73.00 | ||
Proposed (Intra-subject) | 20 | Smart T-Shirt | ECG | 93.30 | |
Proposed (Inter-subject) | 94.10 |
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Bin Heyat, M.B.; Akhtar, F.; Abbas, S.J.; Al-Sarem, M.; Alqarafi, A.; Stalin, A.; Abbasi, R.; Muaad, A.Y.; Lai, D.; Wu, K. Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal. Biosensors 2022, 12, 427. https://doi.org/10.3390/bios12060427
Bin Heyat MB, Akhtar F, Abbas SJ, Al-Sarem M, Alqarafi A, Stalin A, Abbasi R, Muaad AY, Lai D, Wu K. Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal. Biosensors. 2022; 12(6):427. https://doi.org/10.3390/bios12060427
Chicago/Turabian StyleBin Heyat, Md Belal, Faijan Akhtar, Syed Jafar Abbas, Mohammed Al-Sarem, Abdulrahman Alqarafi, Antony Stalin, Rashid Abbasi, Abdullah Y. Muaad, Dakun Lai, and Kaishun Wu. 2022. "Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal" Biosensors 12, no. 6: 427. https://doi.org/10.3390/bios12060427