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| Individual-Level Assessment in Patients with Disorders of Consciousness under Passive Auditory ERP Paradigm |
| Wang Xiaoyu1&, Yang Yi2&, Li Fan1, Chen Xueling2, Gao Hanbing1, Ma Zhaonan1, He Jianghong2*, Cong Fengyu1* |
1(School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, Liaoning, China)
2(Department of Neurosurgery, Beijing Tiantan Hospital Affiliated to Capital Medical University, Beijing 100700,China) |
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Abstract In recent years, there have been increased efforts to assess the levels of consciousness in patients with disorders of consciousness (DoC) using neuroimaging techniques, aiming to improve diagnosis and identification beyond current subjective behavioral assessments that suffer from high misdiagnosis rates. Previous evidence suggests that N1 and mismatch negativity (MMN) components elicited by passive auditory event-related potentials (ERPs) paradigms are critical neurophysiological markers of DoC. However, as such evidence is limited to group-level analysis, the extent to which they enable residual consciousness detection at the individual-level is unclear. Considering the characteristics of N1 and MMN components, we proposed a deep learning algorithm for the individual assessment of patients with DoC under a passive auditory ERPs paradigm. The algorithm proposed a data augmentation strategy, which randomly fused single-trials elicited by different types of stimuli in the spatial domain to form fusion samples, and a deep learning classifier, known as EEGNET, to achieve automatic feature extraction and classification. The proposed method was evaluated in a three-class classification task (38 healthy controls, 40 minimally conscious state, and 54 vegetative state patients) using a single-trial dataset including 132 subjects. Statistical results showed that the proposed data augmentation method significantly improved the classification performance in the current task, and it achieved the highest 75.14% mean classification accuracies in sample level as well as 83.00% mean classification accuracies, 83.79% precision rate, and 84.02% recall rate in subject level when the number of single-subject samples was augmented to 1000. In conclusion, the proposed method could overcome the drawbacks of poor assessment performance in the conventional individual-level assessment methods, providing a new strategy for individual-level assessment in patients with DoC.
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Received: 18 May 2021
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Corresponding Authors:
*E-mail: he_jianghong@sina.cn; cong@dlut.edu.cn
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| About author:: &Co-first author |
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