Efficient Epileptic Seizure Prediction Based on Deep Learning

计算机科学 人工智能 判别式 深度学习 卷积神经网络 预处理器 学习迁移 特征提取 癫痫发作 人工神经网络 稳健性(进化) 恒虚警率 假警报 模式识别(心理学) 脑电图 循环神经网络 机器学习 发作性 癫痫 心理学 化学 神经科学 精神科 生物化学 基因 生物
作者
Hisham Daoud,Magdy Bayoumi
出处
期刊:IEEE Transactions on Biomedical Circuits and Systems [Institute of Electrical and Electronics Engineers]
卷期号:13 (5): 804-813 被引量:359
标识
DOI:10.1109/tbcas.2019.2929053
摘要

Epilepsy is one of the world's most common neurological diseases. Early prediction of the incoming seizures has a great influence on epileptic patients' life. In this paper, a novel patient-specific seizure prediction technique based on deep learning and applied to long-term scalp electroencephalogram (EEG) recordings is proposed. The goal is to accurately detect the preictal brain state and differentiate it from the prevailing interictal state as early as possible and make it suitable for real time. The features extraction and classification processes are combined into a single automated system. Raw EEG signal without any preprocessing is considered as the input to the system which further reduces the computations. Four deep learning models are proposed to extract the most discriminative features which enhance the classification accuracy and prediction time. The proposed approach takes advantage of the convolutional neural network in extracting the significant spatial features from different scalp positions and the recurrent neural network in expecting the incidence of seizures earlier than the current methods. A semi-supervised approach based on transfer learning technique is introduced to improve the optimization problem. A channel selection algorithm is proposed to select the most relevant EEG channels which makes the proposed system good candidate for real-time usage. An effective test method is utilized to ensure robustness. The achieved highest accuracy of 99.6% and lowest false alarm rate of 0.004 h - 1 along with very early seizure prediction time of 1 h make the proposed method the most efficient among the state of the art.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
梨卜橙完成签到,获得积分10
1秒前
小武哥完成签到 ,获得积分10
1秒前
梅竹完成签到,获得积分10
2秒前
3秒前
M二以发布了新的文献求助10
3秒前
3秒前
4秒前
梨卜橙发布了新的文献求助30
4秒前
田様应助嚭嚭采纳,获得10
5秒前
mao305发布了新的文献求助10
5秒前
5秒前
5秒前
Dallas发布了新的文献求助10
5秒前
充电宝应助满意可乐采纳,获得10
5秒前
ppg123应助木木木采纳,获得10
6秒前
6秒前
miles发布了新的文献求助10
6秒前
欣慰冬亦完成签到 ,获得积分10
7秒前
chunb发布了新的文献求助10
7秒前
天天快乐应助高贵花瓣采纳,获得30
7秒前
Ls完成签到 ,获得积分10
8秒前
heart发布了新的文献求助30
8秒前
zzx完成签到,获得积分10
8秒前
.。。。。发布了新的文献求助10
8秒前
PUTIDAXIAN发布了新的文献求助10
10秒前
令狐擎宇发布了新的文献求助10
10秒前
chapai发布了新的文献求助10
11秒前
特特雷珀萨努完成签到 ,获得积分10
11秒前
11秒前
大个应助马明旋采纳,获得10
11秒前
11秒前
13秒前
zzx发布了新的文献求助10
15秒前
Singularity发布了新的文献求助10
15秒前
xiaofei666发布了新的文献求助30
16秒前
大模型应助nykal采纳,获得10
17秒前
BreadCheems发布了新的文献求助10
17秒前
17秒前
高分求助中
The late Devonian Standard Conodont Zonation 2000
Nickel superalloy market size, share, growth, trends, and forecast 2023-2030 2000
The Lali Section: An Excellent Reference Section for Upper - Devonian in South China 1500
Smart but Scattered: The Revolutionary Executive Skills Approach to Helping Kids Reach Their Potential (第二版) 1000
Very-high-order BVD Schemes Using β-variable THINC Method 830
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 800
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 800
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3248330
求助须知:如何正确求助?哪些是违规求助? 2891731
关于积分的说明 8268453
捐赠科研通 2559668
什么是DOI,文献DOI怎么找? 1388584
科研通“疑难数据库(出版商)”最低求助积分说明 650772
邀请新用户注册赠送积分活动 627744