The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis

Wang, Jing; Ouyang, Hui; Jiao, Runda; Cheng, Suhui; Zhang, Haiyan; Shang, Zhilei; Jia, Yanpu; Yan, Wenjie; Wu, Lili; Liu, Weizhi

doi:10.1038/s41746-024-01117-5

Cited by 5 publications

(1 citation statement)

References 62 publications

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“…This is where Artificial Intelligence (AI) and Machine Learning (ML) come into play. AI and ML have been able to predict stress and detect the brain's normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD) with an accuracy around 90% [1], [7]. Recent advancements in ML techniques have further enhanced their predictive capabilities, making them valuable tools for stress detection [8].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Potential of Wearable Technology in Early Stress Detection: A Multimodal Approach

Darwish,

Salem,

Kareem

et al. 2024

Preprint

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Stress can adversely impact health, leading to issues like high blood pressure, heart diseases, and a compromised immune system. Consequently, using wearable devices to monitor stress is essential for prompt intervention and effective management. This study investigates the efficacy of wearable devices in the early detection of psychological stress, employing both binary and five-class classification models. Significant correlations were observed between stress levels and physiological signals, including Electrocardiogram (ECG), Electrodermal Activity (EDA), and Respiration (RESP), establishing these modalities as reliable biomarkers for stress detection. Utilizing the publicly available Wearable Stress and Affect Detection (WESAD) dataset, we employed two ensemble methods, Majority Voting (MV) and Weighted Averaging (WA), to integrate these signals, achieving maximum accuracies of 99.96% for binary classification and 99.59% for five-class classification. This integration significantly enhances the accuracy and robustness of the stress detection system. Furthermore, ten different classifiers were evaluated, and hyperparameter optimization and K-fold cross-validation ranging from 3-fold to 10-fold were applied. Both time-domain and frequency-domain features were examined separately. A review of commercially available wearable devices supporting these modalities was also conducted, resulting in recommendations for optimal configurations for practical applications. Our findings highlight the potential of multimodal wearable devices in advancing the early detection and continuous monitoring of psychological stress, with significant implications for future research and the development of improved stress detection systems.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Potential of Wearable Technology in Early Stress Detection: A Multimodal Approach

Darwish,

Salem,

Kareem

et al. 2024

Preprint

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Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy

Tao,

Xiong,

Peng

et al. 2025

Gene

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Evaluating the Potential of Wearable Technology in Early Stress Detection: A Multimodal Approach

Darwish,

Salem,

Kareem

et al. 2024

Preprint

View full text Add to dashboard Cite

Stress can adversely impact health, leading to issues like high blood pressure, heart diseases, and a compromised immune system. Monitoring stress with wearable devices is crucial for timely intervention and management. This study examines the efficacy of wearable devices in early stress detection using binary and five-class classification models. Significant correlations between stress levels and physiological signals, including Electrocardiogram (ECG), Electrodermal Activity (EDA), and Respiration (RESP), were found, validating these signals as reliable stress biomarkers. Utilizing the WESAD dataset, we applied ensemble methods, Majority Voting (MV) and Weighted Averaging (WA), achieving maximum accuracies of 99.96% for binary classification and 99.59% for five-class classification. Ten classifiers were evaluated, with hyperparameter optimization and 3 to 10 fold cross-validation applied. Time and frequency domain features were analyzed separately. We reviewed commercially available wearables supporting these modalities and provided recommendations for optimal configurations in practical applications. Our findings demonstrate the potential of multimodal wearable devices for early detection and continuous monitoring of psychological stress, suggesting significant implications for future research and the development of improved stress detection systems.

show abstract

The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis

Cited by 5 publications

References 62 publications

Evaluating the Potential of Wearable Technology in Early Stress Detection: A Multimodal Approach

Evaluating the Potential of Wearable Technology in Early Stress Detection: A Multimodal Approach

Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy

Evaluating the Potential of Wearable Technology in Early Stress Detection: A Multimodal Approach

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