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基于注意力的深卷积神经网络用于对泛性和焦点性发作进行分类

Taimur Shahzad Gill¹, Syed Sajjad Haider Zaidi¹, Muhammad Ayaz Shirazi¹

¹Department of Electronics and Power Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan.

Epilepsy & behavior : E&B

|April 18, 2024

概括

这项研究开发了一种自动化的深度学习模型,用于使用电脑电图 (EEG) 信号对发作进行分类. 这种先进的模型显著提高了诊断七种发作类型的准确性,帮助更快的临床干预.

科学领域:

神经学神经学
人工智能的人工智能
生物医学信号处理

背景情况:

影响全球超过5000万个人,需要准确的诊断工具.
用于诊断的脑电图 (EEG) 手动分类是劳动密集型的,需要专门的专业知识.
自动发作分类具有提高诊断效率和患者治疗结果的潜力.

研究的目的:

开发和评估使用EEG信号的自动化多类发作分类模型.
为了提高诊断各种发作亚型的准确性和效率.
探索深度学习的有效性,特别是将多头自我注意力与CNN结合起来,用于EEG分析.

主要方法:

从10秒的EEG信号窗口中提取了11个不同的特征.
设计了一种混合深度学习模型,将深度卷积神经网络 (CNN) 与多头自我注意力机制集成在一起.
使用了寺大学医院的扣押体 (ver. 1.5.2) 进行培训和验证.

主要成果:

拟议的模型在分类七种发作亚型时,实现了0.921的加权精度和0.902的加权F1得分.
与没有多头注意力机制的标准CNN模型 (0.767加权精度) 相比,表现出更高的性能.
除研究证实了变压器编码器和注意力机制对模型性能的关键贡献.

结论:

开发的深度学习模型显示出对中复杂的EEG信号的准确和自动分类的重大前景.
这种方法有可能简化的诊断,使得临床干预更及时,更有效.
进一步融入临床实践可能会彻底改变管理和患者护理.

关键词:

卷积神经网络是一个卷积神经网络.电脑脑电图 (EEG) 是一种电脑电图.是一种病.发作发作发作多头注意力机制多头注意力机制

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Seizures: Classification

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:

Arteries of the Lower Limbs

Arteries of the Lower Limbs

Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...