X-ECGNet: An Interpretable DL model for Stress Detection using ECG in COVID-19 Healthcare Workers

Gupta, Anubha; Kansal, Deepankar; Gupta, V. B.; Shetty, Manu Kumar; Mp, Girish; Gupta, Mohit

doi:10.1109/biosmart54244.2021.9677750

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Rodrigues et al incorporated other ECG features from firefighters such as the average normal to normal intervals (AVNN), their standard deviation (SDNN), and the root mean square of differences between successive intervals (RMSSD) [95]. For Covid-19 healthcare workers' stress levels, X-ECGNet was developed, and it takes raw ECG and HRV features as inputs and achieved a 91.62% accuracy [62]. As for police officers, the challenge of assessing their stress levels using ECG is labeling which events were stressful since these officers are expected to have high endurance for stressful situations [60].…”

Section: ) Ecgmentioning

confidence: 99%

Mental Stress Assessment in the Workplace: A Review

Masri,

Al-Shargie,

Tariq

et al. 2024

IEEE Trans. Affective Comput.

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Workers with demanding jobs are at risk of experiencing mental stress, leading to decreased performance, mental illness, and disrupted sleep. To detect elevated stress levels in the workplace, studies have explored stress measurement from physiological, psychological, and behavioral perspectives. This paper reviews the assessment methods and strategies for mitigating mental stress in the workplace and provides recommendations for early detection and mitigation of mental stress. Among the modalities, Electroencephalography (EEG), Electrocardiography (ECG) and Galvanic Skin Response (GSR) were found to be the most used in assessing mental stress in the workplace. Nevertheless, these modalities are sensitive to motion artifacts and are difficult to be integrated into real work environments. To further improve stress level assessment in the workplace, multimodality integration with a reduced number of sensors such as EEG, GSR and Functional near infrared spectroscopy (fNIRS) can be utilized. This would lead to developing strategies for stress management in real-time. Furthermore, combining EEG with fNIRS would improve source localization of mental stress. To mitigate stress, we recommend developing a closed loop system that incorporates brain data acquisition systems and machine learning with physical stimulations such as audio Binaural Beats Stimulation and/or Transcranial Electric Stimulation.

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Section: ) Ecgmentioning

confidence: 99%

Mental Stress Assessment in the Workplace: A Review

Masri,

Al-Shargie,

Tariq

et al. 2024

IEEE Trans. Affective Comput.

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“…In contrast to that, Singh & Sharma [22] conducted a more systematic comparison of four xAI methods based on Shapley additive explanations (SHAP), local interpretable model-agnostic explanations (LIME), and GradCAM. SHAP was also used in [23][24][25][26] and exemplary compared to LIME and permutation feature relevance in [12]. Besides these post-hoc methods, applied to a model after classification, multiple works visualized ante-hoc generated attention layer values to explain ECG classifications, showing the samples' relevance for classification [27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

xECGArch: A trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features

Goettling,

Hammer,

Malberg

et al. 2024

Preprint

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Deep learning-based methods have demonstrated high classification performance in the detection of cardiovascular diseases from electrocardiograms (ECGs). However, their blackbox character and the associated lack of interpretability limit their clinical applicability. To overcome existing limitations, we present a novel deep learning architecture for interpretable ECG analysis (xECGArch). For the first time, short- and long-term features are analyzed by two independent convolutional neural networks (CNNs) and combined into an ensemble, which is extended by methods of explainable artificial intelligence (xAI) to whiten the blackbox. To demonstrate the trustworthiness of xECGArch, perturbation analysis was used to compare 13 different xAI methods. We parameterized xECGArch for atrial fibrillation (AF) detection using four public ECG databases (n = 9,854 ECGs) and achieved an F1 score of 95.43 % in AF versus non-AF classification on an unseen ECG test dataset. A systematic comparison of xAI methods showed that deep Taylor decomposition provided the most trustworthy explanations (+24 % compared to the second-best approach). xECGArch can account for short- and long-term features corresponding to clinical features of morphology and rhythm, respectively. Further research will focus on the relationship between xECGArch features and clinical features, which may help in medical applications for diagnosis and therapy.

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“…Another recent study has built a CNN based interpretable AI model for cardiac disorders using ECG wave analysis on PTBXL dataset [ 25 ]. Similarly, interesting machine learning and deep learning studies have been conducted recently to detect stress in COVID healthcare workers using ECG signal analysis [ 26 , 27 ]. Thus, we observe that CNN based DL models are increasingly being used for ECG analysis.…”

Section: Introductionmentioning

confidence: 99%