A Neural Network Approach for Anxiety Detection Based on ECG

Vulpe-Grigorasi, Adrian; Grigore, Ovidiu

doi:10.1109/ehb52898.2021.9657544

Cited by 7 publications

(9 citation statements)

References 17 publications

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“…For neural networks, the best accuracy was obtained with the study [ 33 ] that combined EDA, PPG, and ST. Good performance corresponding to accuracy above 85% was also achieved with EEG or EEG combined with ECG, EDA, and EMG. In contrast, a study [ 16 ] using ECG, ST, and RSP had lower accuracy results of 77%.…”

Section: Modelsmentioning

confidence: 86%

“…The comparison between anxiety and anxiety disorder is one of the limitations of this review. Indeed, some studies [ 23 , 24 , 26 , 27 , 31 , 35 , 37 ], focus on detecting anxiety as an induced experience in danger, whereas others [ 15 , 16 , 19 , 25 , 26 , 28 , 32 ] aim to detect ADs subjects which need a timeline to be categorized, from health control.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…Neural networks are widely used, particularly CNNs, which are applied to both EEG and a combination of physiological signals. When working on EEG, a two-dimensional (2D) CNN was used [ 21 , 32 ]; for other signals, a one-dimensional (1D) CNN was used [ 16 , 33 ]. CNN is a multilayer perceptron with several convolution-pooling layers and fully connected layers at the output.…”

Section: Modelsmentioning

confidence: 99%

“…EDA = electrodermal activity; ECG = electrocardiogram; EEG = electroencephalography; RSP = respiration; ST = skin temperature; PPG = photoplethysmogram; N/R = not reported. The included studies are [ 15 , 16 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ].…”

Section: Figurementioning

confidence: 99%

“…Nevertheless, short-term HRV analysis is thought to be more practical because of its simplicity of use and reduced analysis of time, although it is highly dependent on the window length of the processed ECG signal [ 11 , 12 , 13 , 14 ]. Two of the studies [ 15 , 16 ] that are part of this review used ultra-short HRV as a feature (<5 min) to detect anxiety. The key benefit of ultra-short HRV is that it is well suited for mobile applications; consequently, it generates data quickly with a significantly shorter recording time than standard methods [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Anxiety Detection Using Biosignals: A Review

2022

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Anxiety disorder (AD) is a major mental health illness. However, due to the many symptoms and confounding factors associated with AD, it is difficult to diagnose, and patients remain untreated for a long time. Therefore, researchers have become increasingly interested in non-invasive biosignals, such as electroencephalography (EEG), electrocardiogram (ECG), electrodermal response (EDA), and respiration (RSP). Applying machine learning to these signals enables clinicians to recognize patterns of anxiety and differentiate a sick patient from a healthy one. Further, models with multiple and diverse biosignals have been developed to improve accuracy and convenience. This paper reviews and summarizes studies published from 2012 to 2022 that applied different machine learning algorithms with various biosignals. In doing so, it offers perspectives on the strengths and weaknesses of current developments to guide future advancements in anxiety detection. Specifically, this literature review reveals promising measurement accuracies ranging from 55% to 98% for studies with sample sizes of 10 to 102 participants. On average, studies using only EEG seemed to obtain the best performance, but the most accurate results were obtained with EDA, RSP, and heart rate. Random forest and support vector machines were found to be widely used machine learning methods, and they lead to good results as long as feature selection has been performed. Neural networks are also extensively used and provide good accuracy, with the benefit that no feature selection is needed. This review also comments on the effective combinations of modalities and the success of different models for detecting anxiety.

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Section: Modelsmentioning

confidence: 86%