Hilbert将基于差分进化的卷积神经网络优化用于脑电图信号的滤波和分类

IF 4.9 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Raja Sekhar Banovoth, Kadambari K V
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引用次数: 0

摘要

脑电图(EEG)被广泛认为是测量人脑神经元活动的一种有效的非侵入性技术,具有高时间分辨率。然而,脑电图信号经常受到噪声和伪影的污染,严重影响其分析。为了应对这一挑战,基于深度学习的去噪技术已经取得了各种进展。尽管取得了这些进步,但在广泛的初始参数范围内确定最佳网络架构仍然很复杂。手动调优这些参数以实现最佳性能需要大量的时间和专业知识。同样,我们引入了一种新的基于残差块的神经网络来自动去除伪影,并引入了一种卷积神经网络来进行分类。该架构分为两个不同的阶段:首先,噪声消除,其中原始EEG信号经过希尔伯特变换,将其转换为复值信号。这些信号作为使用差分进化(HT-DEResNet)创新技术构建的基于残差块的卷积神经网络的输入,该网络优化了结构和初始参数。这使得无需人工干预即可有效地移除工件。该模型应用于三个复杂的数据集:HaLT、眼伪影和重度抑郁症。第二阶段包括用于分类的多卷积神经网络(MCNN)的发展。为了验证有效性,将BCI竞争数据集2a和2b置于HT-DEResNet框架中。随后,对信号进行分类。结果不仅在准确性上超过了最先进的方法,而且由于去噪,性能提高了17.73%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hilbert transformed differential evolution based optimization of convolution neural network for Electroencephalogram signal filtering and classification
Electroencephalography (EEG) is widely regarded as an effective non-invasive technique for measuring neuronal activity in the human brain, offering high temporal resolution. However, EEG signals are often contaminated by noise and artifacts, which significantly impact their analysis. To tackle this challenge, various advancements in deep learning-based denoising techniques have been developed. Despite these advancements, identifying the optimal network architecture across a broad range of initial parameters remain complex. Manually tuning these parameters to achieve optimal performance is time-intensive and demands substantial expertise. Along the same lines, we introduce a novel residual block-based neural network for automatic artifact removal and a convolution neural network for classification. The architecture unfolds in two distinct stages: Firstly, the noise elimination, where the raw EEG signals undergo the Hilbert transformation, converting them into complex-valued signals. These signals serve as the input for a Residual block-based Convolution Neural Network constructed using the innovative technique of Differential Evolution (HT-DEResNet), which optimizes both the architecture and initial parameters. This enables the effective removal of artifacts without manual intervention. The model is applied across three complex datasets: HaLT, eye artifact, and major depressive disorder. The secondary stage comprises the development of a Multi-Convolution Neural Network (MCNN) for classification. To validate the effectiveness, the BCI competition datasets 2a & 2b are subjected to the HT-DEResNet framework. Subsequently, the signals are classified. The results not only surpass state-of-the-art methods in accuracy but also achieve a 17.73% improvement in performance attributed to noise removal.
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来源期刊
Computers & Electrical Engineering
Computers & Electrical Engineering 工程技术-工程:电子与电气
CiteScore
9.20
自引率
7.00%
发文量
661
审稿时长
47 days
期刊介绍: The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.
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