Key result
Support-vector-machine and deep-learning techniques feature extensively in ~200 papers analyzing mental health via multi-modal biometrics.
Why the study?
With the rise of biosensors and healthcare data collection devices, a comprehensive review of trends in machine-learning models for mental health analysis using multi-channel multi-modal biometric signals was needed.
Systematic Review (n=200)
This systematic review highlights the predominant use of SVM and deep learning techniques for analyzing multi-modal biometric signals in mental health applications.
Supports prioritizing SVM and deep learning for biometric mental health tools; extends method-mapping for targeted validation studies.
With the increase in biosensors and data collection devices in the healthcare industry, artificial intelligence and machine learning have attracted much attention in recent years. In this study, we offered a comprehensive review of the current trends and the state-of-the-art in mental health analysis as well as the application of machine-learning techniques for analyzing multi-variate/multi-channel multi-modal biometric signals.This study reviewed the predominant mental-health-related biosensors, including polysomnography (PSG), electroencephalogram (EEG), electro-oculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG). We also described the processes used for data acquisition, data-cleaning, feature extraction, machine-learning modeling, and performance evaluation. This review showed that support-vector-machine and deep-learning techniques have been well studied, to date.After reviewing over 200 papers, we also discussed the current challenges and opportunities in this field.
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Ehiabhi et al. (2023) conducted a systematic review in Mental health (n=200). Machine learning models was evaluated. A systematic review of over 200 papers showed that support-vector-machine and deep-learning techniques are well studied for mental health analysis using multi-modal biometric signals.
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