Rukhsar Salim, Tiwari Anil Kumar
Department of Electrical Engineering, Indian Institute of Technology Jodhpur, Rajasthan, 342030, India.
Department of Electrical Engineering, Indian Institute of Technology Jodhpur, Rajasthan, 342030, India.
Comput Methods Programs Biomed. 2023 Dec;242:107856. doi: 10.1016/j.cmpb.2023.107856. Epub 2023 Oct 12.
Epilepsy is a neurological illness affecting the brain that makes people more likely to experience frequent, spontaneous seizures. There has to be an accurate automated method for measuring seizures frequency and severity to assess the efficacy of pharmacological therapy for epilepsy. The drug quantities are often derived from patient reports which may cause significant issues owing to inadequate or inaccurate descriptions of seizures and their frequencies.
This study proposes a novel deep learning architecture-based Lightweight Convolution Transformer (LCT). The Transformer model is able to learn spatial and temporal correlated information simultaneously from the multi-channel electroencephalogram (EEG) signal to detect seizures at smaller segment lengths. In the proposed work, the lack of translation equivariance and localization of ViT is reduced using convolution tokenization, and rich information from the Transformer encoder is extracted by sequence pooling instead of the learnable class token.
Extensive experimental results demonstrate that the proposed model on cross-patient learning can effectively detect seizures from the raw EEG signals. The accuracy and F1-score of seizure detection in the cross-patient case on the CHB-MIT dataset are 96.31% and 96.32%, respectively, at 0.5 sec segment length. In addition, the performance metrics show that the inclusion of inductive biases and attention-based pooling in the model enhances the performance and reduces the number of Transformer encoder layers, which significantly reduces the computational complexity. In this research, we provide a novel approach to enhance efficiency and simplify the architecture for multi-channel automated seizure detection.
癫痫是一种影响大脑的神经系统疾病，会使人们更容易频繁地经历自发性癫痫发作。必须有一种准确的自动化方法来测量癫痫发作的频率和严重程度，以评估癫痫药物治疗的效果。药物用量通常来自患者报告，由于对癫痫发作及其频率的描述不充分或不准确，可能会导致重大问题。
本研究提出了一种基于深度学习架构的新型轻量级卷积变压器（LCT）。变压器模型能够从多通道脑电图（EEG）信号中同时学习空间和时间相关信息，以在较短的片段长度上检测癫痫发作。在这项提议的工作中，使用卷积令牌化减少了视觉变换器（ViT）缺乏平移不变性和定位的问题，并通过序列池化而不是可学习的类别令牌来提取来自变压器编码器的丰富信息。
大量实验结果表明，所提出的跨患者学习模型能够有效地从原始EEG信号中检测癫痫发作。在CHB-MIT数据集上，跨患者情况下癫痫发作检测的准确率和F1分数在0.5秒片段长度时分别为96.31%和96.32%。此外，性能指标表明，模型中包含归纳偏差和基于注意力的池化提高了性能，并减少了变压器编码器层的数量，这显著降低了计算复杂度。在本研究中，我们提供了一种新颖的方法来提高多通道自动癫痫发作检测的效率并简化架构。
