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一种基于深度神经网络的方法来识别患者的活动

Danial Khurshid¹, Fazli Wahid^2,3, Sikandar Ali¹

¹Department of Information Technology, The University of Haripur, Haripur, Khyber Pakhtunkhwa, Pakistan.

Frontiers in medicine

|August 16, 2024

概括

这项研究引入了一个深度神经网络 (DNN) 模型,用于从EEG信号中改进的识别,达到97%的准确性. 这种先进的机器学习方法为诊断提供了一种更有效,更可靠的方法.

科学领域:

神经学神经学
机器学习机器学习
生物医学信号处理

背景情况:

是一种常见的神经疾病,影响全球数百万人,严重影响日常生活.
电脑电图 (EEG) 信号对于的诊断至关重要,但传统方法是劳动密集型的,容易出错.
现有的机器学习技术用于的识别在效率和准确性方面存在局限性.

研究的目的:

为了解决以前的识别方法的局限性.
提出和评估一个深度神经网络 (DNN) 模型,以提高的检测.
提高使用EEG数据识别活动的准确性和效率.

主要方法:

利用公共数据集进行训练和测试DNN模型.
在各种训练测试数据比率 (80:20,70:30,60:40,50:50) 上进行了实验.
使用多个指标评估模型性能,包括验证和比较分析.

主要成果:

拟议的DNN模型实现了97%的高整体准确率.
与以前的发作检测方法相比,证明了更高的效率和准确性.
实验结果证实了该模型在不同数据集分割比率的有效性.

结论:

该DNN模型显示了使用数值EEG数据进行准确可靠的发作检测的巨大潜力.
这种数据驱动的方法为患者护理和管理提供了有前途的进步.
这项研究为现有的发作检测系统提供了更准确,更有效的替代方案.

关键词:

深度学习是一种深度学习.深度神经网络是一个神经网络.一个电脑电图 (electroencephalogram) 是一个电脑电图.的检测的检测发作残疾残疾的疾病.抓获活动识别抓获活动识别

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