基于医疗物联网平台的脑电图信号预测与分析慢性癫痫

IF 0.9 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Noor Hasan Hassoon, M. H. Ali, M. Jaber, Sura Khalil Abd, Ali S. Abosinnee, Z.H. Kareem
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引用次数: 0

摘要

目前难治的癫痫患者可以使用癫痫发作预测脑机接口(BCI)进行监测,该接口使用战略性植入大脑的电极来预测和调节癫痫发作的发作和持续时间。从癫痫发作的瞬时电图活动可以看出,这些技术的实时方法存在挑战。脑电图(EEG)信号本质上是非平稳的,这意味着癫痫患者的常规信号和癫痫发作信号存在显著差异。由于电极上接触的数量有限,在不引起显著处理延迟的情况下,不能在预测函数中采用动态处理和收集的特性。大数据可以保证在这些情况下的安全存储,并且它具有最大的处理能力,可以实时识别、记录和分析时间,以便按照时间表进行扣押事件。大规模头皮脑电图记录的癫痫发作预测和定位一直是本研究的重点,该研究使用可穿戴传感器数据和深度学习来使用云存储来开发系统。提出了一种新的技术来避免癫痫发作,并从所使用的可穿戴传感器中发现癫痫发作的起源。其次,提出了一种基于主成分分析(PCA)、分层搜索算法(HSA)和模糊综合算法(CSA)的扩展优化方法,称为卷积神经网络聚类自动编码器(CAE-CNN),并且已经建立了医疗物联网(MIoT)来定义基于实时、自动计算和存储的可穿戴传感器的大数据存储的建议框架。根据临床试验,对于未解决的慢性癫痫患者,CAE-CNN优于当前基于可穿戴传感器的治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction and analysis of chronic epilepsy using electroencephalographic signals on medical internet of things platform
Epilepsy patients who are presently refractory may be monitored using a seizure prediction Brain-Computer Interface (BCI), which uses electrodes strategically implanted in the brain to anticipate and regulate the onset and duration of a seizure. Real-time approaches to these technologies have challenges, as seen by seizures’ instantaneous electrographic activity. Electroencephalographic (EEG) signals are inherently non-stationary, which means that the regular and seizure signals differ significantly among people with epilepsy. Due to the restricted number of contacts on electrodes, dynamically processed and collected characteristics cannot be employed in a prediction function without causing significant processing delays. Big data can guarantee secure storage in these situations, and it has the maximum processing capability to identify, record, and analyze time in real-time to conduct the seizure event on the timetable. Seizure prediction and location for huge Scalp EEG recordings have been the focus of this study, which used wearable sensor data and deep learning to use cloud storage to develop the systems. A novel technique is suggested to avoid an epileptic seizure and discover the seizure origin from the utilized wearable sensors. Secondly, deep learning architectures called Clustered Autoencoder with Convolutional Neural Network (CAE-CNN), an expanded optimization methodology is presented based on the Principal Component Analysis (PCA), the Hierarchical Searching Algorithm (HSA), and the Medical Internet of Things (MIoT) has been established to define the suggested frameworks based on the collection of big data storage of the wearable sensors in real-time, automatic computation and storage. According to clinical trials, CAE-CNN outperforms the current wearable sensor-based treatment for unresolved chronic epilepsy patients.
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来源期刊
Intelligent Data Analysis
Intelligent Data Analysis 工程技术-计算机:人工智能
CiteScore
2.20
自引率
5.90%
发文量
85
审稿时长
3.3 months
期刊介绍: Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, big data applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing. In particular, papers are preferred that discuss development of new AI related data analysis architectures, methodologies, and techniques and their applications to various domains.
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