利用小波散射变换和最近分量分析对新生儿癫痫发作进行自动分类的框架

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2024-06-18 DOI:10.1016/j.irbm.2024.100842
Vipin Prakash Yadav , Kamlesh Kumar Sharma
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引用次数: 0

摘要

导言:新生儿惊厥是新生儿常见的神经系统疾病。新生儿癫痫发作可通过临床或脑电图诊断。然而,由于癫痫和非癫痫的临床表现多种多样,新生儿癫痫发作的临床诊断非常困难,尤其是重症婴儿。另一方面,新生儿癫痫发作可以通过脑电图记录有效地检测出来。本研究提出了一种基于小波散射变换(WST)和直方图最近分量分析(HBNCA)的新生儿癫痫发作和非癫痫发作脑电信号分类框架。波散射变换(WST)将脑电信号转换为平移不变和变形稳定的表示形式。利用 HBNCA 方法找到有效的小波散射系数(WSC),对癫痫发作和非癫痫发作脑电信号进行分类。结果在重复随机子采样验证(RRSV)和留空交叉验证(LOOCV)中,对于持续时间为 1 秒的脑电信号,所提出的框架分别获得了 98.59% 和 97.83% 的平均准确率。通过对持续时间为 1 秒的脑电信号进行准确分类,显示了所提出的框架在可靠的新生儿癫痫发作分类方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Automatic Classification Framework for Neonatal Seizure Using Wavelet Scattering Transform and Nearest Component Analysis

Automatic Classification Framework for Neonatal Seizure Using Wavelet Scattering Transform and Nearest Component Analysis

Introduction

Neonatal seizure is a common neurologic disorder in neonates. The diagnosis of a neonatal seizure can be made clinically or with an EEG. However, the clinical diagnosis of neonatal seizures is difficult, particularly in critically ill infants, because of the multitude of epileptic and nonepileptic clinical manifestations. On the other hand neonatal seizure can be effectively detected using EEG recordings. Hence, there is a need for an electroencephalograph (EEG) based automatic diagnosis framework for neonatal seizure.

Methods

This work proposed a wavelet scattering transform (WST) and histogram-based nearest component analysis (HBNCA) based framework for classifying seizures and non-seizure neonate's EEG signals. The WST converts EEG signals into its translation invariant and deformation stable representation. The HBNCA method is deployed to find the effective wavelet scattering coefficients (WSC) for classifying seizures and non-seizures EEG signals. Then, various classifiers are used to identify the effectiveness of the features.

Results

The proposed framework is managed to get an average accuracy of 98.59% and 97.83% for a 1-second duration of EEG signal for repeated random subsampling validation (RRSV) and leave one out cross-validation (LOOCV), respectively.

Conclusions

The results are compared with the other state of art methods. The accurate classification from the 1-second duration of the EEG signal shows the potential of the proposed framework for reliable neonatal seizure classification.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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