Sharma Aarti, Rai Jaynendra Kumar, Tewari Ravi Prakash
Department of ECE, Inderprastha Engineering College, Site-IV, Sahibabad, Ghaziabad, Uttar Pradesh, India.
Department of Electronics and Communication Engineering, ASET, Amity University Uttar Pradesh, Sector-125, Noida, India.
Biomed Tech (Berl). 2020 Jul 5. doi: 10.1515/bmt-2020-0044.
Objectives Epilepsy is characterized by uncontrollable seizure during which consciousness of patient is disturbed. Prediction of the seizure in advance will increase the remedial possibilities for the patients suffering from epilepsy. An automated system for seizure prediction is important for seizure enactment, prevention of sudden unexpected deaths and to avoid seizure related injuries. Methods This paper proposes the prediction of an upcoming seizure by analyzing the 23 channel non-stationary EEG signal. EEG signal is divided into smaller segments to change it into quasi-stationary data using an overlapping moving window. Brain region is marked into four regions namely left hemisphere, right hemisphere, central region and temporal region to identify the epileptogenic region. The epileptogenic region shows significant variations during pre-ictal state in comparison to the other regions. So, seizure prediction is carried out by analyzing EEG signals from this region. Seizure prediction is proposed using features extracted from both time and frequency domain. Relative entropy and relative energy are extracted from wavelet transform and Pearson correlation coefficient is obtained from time domain EEG signal. Extracted features have been smoothened using moving average filter. First order derivative of relative features have been used to normalize the intervariability before deciding the threshold for marking the prediction of seizure. Results Isolated seizures where pre-ictal duration of more than 1 h is reported has been detected with an accuracy of 92.18% with precursory warning 18 min in advance and seizure confirmation 12 min in advance. An overall accuracy of 83.33% with false positive alarm rate of 0.01/h has been obtained for all seizure cases with average prediction time of 9.9 min.
目标 癫痫的特点是发作时无法控制，患者的意识会受到干扰。提前预测癫痫发作将增加癫痫患者的补救可能性。一个自动的癫痫发作预测系统对于癫痫发作的判定、预防意外猝死以及避免与癫痫发作相关的伤害非常重要。方法 本文提出通过分析23通道非平稳脑电图信号来预测即将到来的癫痫发作。利用重叠移动窗口将脑电图信号分成较小的段，将其转换为准平稳数据。将脑区分为四个区域，即左半球、右半球、中央区域和颞叶区域，以识别癫痫源区。与其他区域相比，癫痫源区在发作前状态下表现出显著差异。因此，通过分析该区域的脑电图信号来进行癫痫发作预测。提出利用从时域和频域提取的特征进行癫痫发作预测。从小波变换中提取相对熵和相对能量，并从时域脑电图信号中获得皮尔逊相关系数。提取的特征已使用移动平均滤波器进行平滑处理。在确定癫痫发作预测的阈值之前，已使用相对特征的一阶导数对变异性进行归一化。结果 对于发作前持续时间超过1小时的孤立性癫痫发作，检测准确率为92.18%，提前18分钟发出先兆预警，提前12分钟确认癫痫发作。对于所有癫痫发作病例，总体准确率为83.33%，误报率为0.01/小时，平均预测时间为9.9分钟。
