Epilepsy is found to be the fourth most common chronic neurological disorder that tends to abnormal and unpredictable brain activity and seizure states. According to statistics, 70% of the epilepsy patients can be cured if identified and treated with anti-epileptic drugs or shock stimulations. Onlyabout 7% to 8% need to be operated. Electroencephalogram (EEG) is a cheap and effective way to record the prolonged activities of the brain through electrical impulses between neural cells. Seizure is difficult to detect in neonates as the signal involves a lot of disturbances and the existing high accuracy system for adults can’t be used for neonates. In an attempt to build an impregnable system to detect seizure in early stages, EEG signals of neonates procured from Neonatal Intensive Care Unit (NICU) at the Helsinki University Hospital. These signals were processed and fed into three different robust algorithms –Support Vector Machine (SVM), Artificial Neural Network (ANN) and 1-Dimensional Convolutional Neural Network (1D-CNN). The experimental results were compared and the proposed CNN model with 95.99% accuracy outperforms all the state-of-art models for automated Epileptic Seizure prediction in Neonates. Deep CNN has been a powerful tool in extracting robust features from EEG signals. This generalized system can be used by medical experts for detecting Seizure in neonates with better accuracy and reliability.

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基于机器学习的智能自动化新生儿癫痫发作检测

癫痫病是第四种最常见的慢性神经系统疾病，往往会导致异常和不可预测的大脑活动以及癫痫发作状态。据统计，如果识别出并使用抗癫痫药或电击刺激疗法，可以治愈70％的癫痫患者。仅需操作约7％至8％。脑电图（EEG）是一种通过神经细胞之间的电脉冲记录大脑长时间活动的廉价有效方法。新生儿癫痫发作很难检测到，因为该信号涉及很多干扰，并且现有的成人高精度系统无法用于新生儿。为了建立一个牢固的系统来早期检测癫痫发作，从赫尔辛基大学医院新生儿重症监护病房（NICU）获得了新生儿的EEG信号。这些信号经过处理后被馈入三种不同的鲁棒算法-支持向量机（SVM），人工神经网络（ANN）和一维卷积神经网络（1D-CNN）。比较了实验结果，所提出的具有95.99％准确性的CNN模型优于所有用于新生儿癫痫发作自动预测的最新模型。深度CNN是从脑电信号中提取强大功能的强大工具。医学专家可以使用此通用系统以更高的准确性和可靠性检测新生儿的癫痫发作。99％的准确度优于所有最新的新生儿癫痫发作自动预测模型。深度CNN是从脑电信号中提取强大功能的强大工具。医学专家可以使用此通用系统以更高的准确性和可靠性检测新生儿的癫痫发作。99％的准确度优于所有最新的新生儿自动癫痫发作预测模型。深度CNN是从脑电信号中提取强大功能的强大工具。医学专家可以使用此通用系统以更高的准确性和可靠性检测新生儿的癫痫发作。