使用LSTM进行癫痫发作分类

Kishori Shekokar, Shweta Dour, Gufran Ahmad
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引用次数: 1

摘要

癫痫是一种非传染性的神经系统疾病,影响所有年龄段的人。它是通过观察癫痫发作的重复来发出信号的。癫痫的主要原因是头部受伤、分娩时缺氧、脑肿瘤、遗传疾病以及脑膜炎或脑炎等感染。当脑内的电脉冲爆发超出正常限制时,就会出现癫痫发作。传统的诊断技术可能需要时间来诊断,并且由于在每种情况下缺乏专业知识,与自动化系统相比,它可能在完美程度上有所不同。所以这是一个严重的问题紊乱必须在行为症状出现之前被诊断出来。本研究的主要目的是利用深度学习方法建立基于脑电图数据的疾病识别专家模型。为了检测癫痫发作,作者在波恩EEG数据集上建立了长短期记忆(LSTM)模型。用于检查该技术有效性的指标计算为准确性、特异性和敏感性。该模型仅在30个epoch中达到98.5%的准确率、99%的灵敏度和98%的特异性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Epileptic Seizure Classification using LSTM
Epilepsy is a neurological disease which is non transmissible and affects across all ages. It is signalized by observing repetitions of seizures. Main causes of epilepsy are head injuries, low oxygen during birth, brain tumors, genetic conditions and infections like meningitis or encephalitis. A seizure came out whenever explosion of electrical impulses in brain elude their normal restricts. Traditional diagnosis techniques may take time to diagnose and it may differ in perfection as compared to automated system due to lack of expertization in each case. So this is a serious issue that disorder must be diagnose prior to the manifestation of behavioral symptoms. The main motive of this study is bestow expert model for recognizing disorders on basis of Electroencephalogram (EEG) data using Deep learning methodology. In this paper to detect epileptic seizures, author presented long short-term memory (LSTM) model on Bonn’s EEG dataset. Metrics calculated to check the efficacy of the technique are accuracy, specificity and sensitivity. Proposed model has achieved 98.5% accuracy, 99% sensitivity and 98% specificity only in 30 epochs.
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