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基于神经网络的方法来测量性脑活动对心脏周期的影响

Cândida H L Alves¹, Gilberto S Alves^2,3, Rômulo Kunrath²

¹Department of Psychology, Edufor Faculty, Idomed Faculty, São Luís, Maranhão, Brazil.

Frontiers in neurology

|August 18, 2025

概括

电心电图 (ECG) 可以通过分析心脏周期来监测患者的脑活动. 这种非侵入性,负担得起的方法为可访问的监测提供了电脑电图 (EEG) 的有希望的替代方案.

科学领域:

生物医学工程生物医学工程
神经科学是一个神经科学.
医疗技术医疗技术医学技术

背景情况:

电脑电图 (EEG) 是监测的标准,但面临着可访问性和可重复性挑战.
可穿戴式心电图 (ECG) 提供了一种更容易获得的方法,可以通过心脏周期间接监测大脑活动.

研究的目的:

开发一种使用心电图形态学检测患者大脑活动的计算模型.
评估基于心电图的监测作为EEG的替代方案的有效性.

主要方法:

开发了一个使用统计周期和神经网络来分析心电图波形态的计算模型.
作为关键分析特征,利用以中位数为中心的方差,斜率和曲率.
将心电图分析与传统的心率变化方法进行比较.

主要成果:

在使用心电图检测性脑活动时,获得了100%的灵敏度,特异性和准确性.
证明了心电图分析可以检测心率变化之前的大脑活动变化.
选择的统计特征使得使用不那么复杂的算法实现了高精度.

结论:

电脑心电图提供了一种可行的,负担得起的,有效的非侵入性方法来估计性脑活动.
应用到心电图的机器学习为监测提供了具有成本效益和可访问性的解决方案,特别是在弱势群体中.
这种方法克服了EEG的局限性,提高了广泛的管理.

关键词:

这是一个ECGECGECGECGECG.数据分类数据的分类.是一种.心率变化的心率变化.机器学习是机器学习.神经网络的神经网络的神经网络发作发作这些

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