脑电图
头皮
计算机科学
疼痛评估
人工智能
深度学习
模式识别(心理学)
机器学习
医学
物理疗法
心理学
神经科学
疼痛管理
解剖
作者
Zanhao Fu,Huaiyu Zhu,Yi Zhang,Ruohong Huan,Shuohui Chen,Yun Pan
出处
期刊:IEEE Transactions on Biomedical Engineering
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-12
标识
DOI:10.1109/tbme.2024.3355215
摘要
Objective: Common pain assessment approaches such as self-evaluation and observation scales are inappropriate for children as they require patients to have reasonable communication ability. Subjective, inconsistent, and discontinuous pain assessment in children may reduce therapeutic effectiveness and thus affect their later life. Methods: To address the need for suitable assessment measures, this paper proposes a spatiotemporal deep learning framework for scalp electroencephalogram (EEG)-based automated pain assessment in children. The dataset comprises scalp EEG data recorded from 33 pediatric patients with an arterial puncture as a pain stimulus. Two electrode reduction plans in line with clinical findings are proposed. Combining three-dimensional hand-crafted features and preprocessed raw signals, the proposed transformer-based pain assessment network (STPA-Net) integrates both spatial and temporal information. Results: STPA-Net achieves superior performance with a subject-independent accuracy of 87.83% for pain recognition, and outperforms other state-of-the-art approaches. The effectiveness of electrode combinations is explored to analyze pain-related cortical activities and correspondingly reduce cost. The two proposed electrode reduction plans both demonstrate competitive pain assessment performance qualitatively and quantitatively. Conclusion and significance: This study is the first to develop a scalp EEG-based automated pain assessment for children adopting a method that is objective, standardized, and consistent. The findings provide a potential reference for future clinical research.
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