A multi-domain feature fusion epilepsy seizure detection method based on spike matching and PLV functional networks.

Qikai Fan, Lurong Jiang, Amira El Gohary, Fang Dong, Duanpo Wu, Tiejia Jiang, Chen Wang, Junbiao Liu
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Abstract

Objective.The identification of spikes, as a typical characteristic wave of epilepsy, is crucial for diagnosing and locating the epileptogenic region. The traditional seizure detection methods lack spike features and have low sample richness. This paper proposes a seizure detection method with spike-based phase locking value (PLV) functional brain networks and multi-domain fused features.Approach.In the spiking detection part, brain functional networks based on PLV are constructed to explore the changes in brain functional states during spiking discharge, from the perspective of microscopic neuronal activity to macroscopic brain region interactions. Then, in the epilepsy seizure detection task, multi-domain fused feature sequences are constructed using time-domain, frequency-domain, inter-channel correlation, and the spike detection features. Finally, Bi-LSTM and Transformer encoders and their optimized models are used to verify the effectiveness of the proposed method.Main results.Experimental results achieve the best seizure detection metrics on Bi-LSTM-Attention, with accuracy, sensitivity, and specificity reaching 98.40%, 98.94%, and 97.86%, respectively.Significance.The method is significant as it innovatively applies multi channel spike network features to seizure detection. It can potentially improve the diagnosis and location of the epileptogenic region by accurately detecting seizures through the identification of spikes, which is a crucial characteristic wave of epilepsy.

目的:尖波是癫痫的典型特征波,识别尖波对于诊断和定位致痫区至关重要。传统的癫痫发作检测方法缺乏尖峰特征,样本丰富度较低。本文提出了一种基于尖峰锁相值(PLV)功能脑网络和多域融合特征的癫痫发作检测方法。在尖峰检测部分,构建基于锁相值(PLV)的脑功能网络,从微观神经元活动到宏观脑区相互作用的角度,探索尖峰放电时脑功能状态的变化。然后,在癫痫发作检测任务中,利用时域、频域、信道间相关性和尖峰检测特征构建多域融合特征序列。最后,使用 Bi-LSTM 和 Transformer 编码器及其优化模型验证了所提方法的有效性。实验结果表明,Bi-LSTM Attention 的癫痫发作检测指标最好,准确率、灵敏度和特异性分别达到 98.40%、98.94% 和 97.86%。该方法创新性地将多通道尖峰网络特征应用于癫痫发作检测,意义重大。它通过识别癫痫发作的关键特征波--尖峰,准确检测癫痫发作,从而有可能改进对致痫区的诊断和定位。
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