Classification of Epileptic and Psychogenic Nonepileptic Seizures via Time-Frequency Features of EEG Data.

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ozlem Karabiber Cura, Aydin Akan, Hatice Sabiha Ture
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引用次数: 0

Abstract

The majority of psychogenic nonepileptic seizures (PNESs) are brought on by psychogenic causes, but because their symptoms resemble those of epilepsy, they are frequently misdiagnosed. Although EEG signals are normal in PNES cases, electroencephalography (EEG) recordings alone are not sufficient to identify the illness. Hence, accurate diagnosis and effective treatment depend on long-term video EEG data and a complete patient history. Video EEG setup, however, is more expensive than using standard EEG equipment. To distinguish PNES signals from conventional epileptic seizure (ES) signals, it is crucial to develop methods solely based on EEG recordings. The proposed study presents a technique utilizing short-term EEG data for the classification of inter-PNES, PNES, and ES segments using time-frequency methods such as the Continuous Wavelet transform (CWT), Short-Time Fourier transform (STFT), CWT-based synchrosqueezed transform (WSST), and STFT-based SST (FSST), which provide high-resolution time-frequency representations (TFRs). TFRs of EEG segments are utilized to generate 13 joint TF (J-TF)-based features, four gray-level co-occurrence matrix (GLCM)-based features, and 16 higher-order joint TF moment (HOJ-Mom)-based features. These features are then employed in the classification procedure. Both three-class (inter-PNES versus PNES versus ES: ACC: 80.9%, SEN: 81.8%, and PRE: 84.7%) and two-class (Inter-PNES versus PNES: ACC: 88.2%, SEN: 87.2%, and PRE: 86.1%; PNES versus ES: ACC: 98.5%, SEN: 99.3%, and PRE: 98.9%) classification algorithms performed well, according to the experimental results. The STFT and FSST strategies surpass the CWT and WSST strategies in terms of classification accuracy, sensitivity, and precision. Moreover, the J-TF-based feature sets often perform better than the other two.

通过脑电图数据的时频特征对癫痫性和心因性非癫痫性发作进行分类。
大多数心因性非癫痫性发作是由心因性原因引起的,但由于其症状与癫痫相似,常被误诊。虽然脑电图信号在PNES病例中是正常的,但仅凭脑电图(EEG)记录不足以识别疾病。因此,准确的诊断和有效的治疗依赖于长期的视频脑电图数据和完整的患者病史。然而,视频EEG设置比使用标准EEG设备更昂贵。为了区分PNES信号与常规癫痫发作(ES)信号,开发仅基于脑电图记录的方法至关重要。该研究提出了一种利用短期脑电数据对PNES间、PNES和ES段进行分类的技术,使用时频方法,如连续小波变换(CWT)、短时傅里叶变换(STFT)、基于CWT的同步压缩变换(WSST)和基于STFT的SST (FSST),这些方法提供了高分辨率的时频表示(TFRs)。利用脑电片段tfr生成13个基于联合TF (J-TF)的特征、4个基于灰度共生矩阵(GLCM)的特征和16个基于高阶联合TF矩(HOJ-Mom)的特征。然后在分类过程中使用这些特征。三级(inter-PNES vs . PNES vs . ES: ACC: 80.9%, SEN: 81.8%, PRE: 84.7%)和二级(inter-PNES vs . PNES: ACC: 88.2%, SEN: 87.2%, PRE: 86.1%;实验结果表明,PNES与ES (ACC: 98.5%, SEN: 99.3%, PRE: 98.9%)的分类算法表现良好。STFT和FSST策略在分类精度、灵敏度和精度方面都优于CWT和WSST策略。此外,基于j - tf的特性集通常比其他两种性能更好。
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来源期刊
International Journal of Neural Systems
International Journal of Neural Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
28.80%
发文量
116
审稿时长
24 months
期刊介绍: The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.
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