Awais Muhammad, Belhaouari Samir Brahim, Kassoul Khelil
Department of Creative Technologies, Air University, Islamabad 44000, Pakistan.
Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha 5825, Qatar.
Biomedicines. 2024 Jun 10;12(6):1283. doi: 10.3390/biomedicines12061283.
Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes. This classification is crucial because it distinguishes between the states of seizure and seizure-free periods in patients with epilepsy. Our study presents an innovative approach for detecting seizures and neurological diseases using EEG signals by leveraging graph neural networks. This method effectively addresses EEG data processing challenges. We construct a graph representation of EEG signals by extracting features such as frequency-based, statistical-based, and Daubechies wavelet transform features. This graph representation allows for potential differentiation between seizure and non-seizure signals through visual inspection of the extracted features. To enhance seizure detection accuracy, we employ two models: one combining a graph convolutional network (GCN) with long short-term memory (LSTM) and the other combining a GCN with balanced random forest (BRF). Our experimental results reveal that both models significantly improve seizure detection accuracy, surpassing previous methods. Despite simplifying our approach by reducing channels, our research reveals a consistent performance, showing a significant advancement in neurodegenerative disease detection. Our models accurately identify seizures in EEG signals, underscoring the potential of graph neural networks. The streamlined method not only maintains effectiveness with fewer channels but also offers a visually distinguishable approach for discerning seizure classes. This research opens avenues for EEG analysis, emphasizing the impact of graph representations in advancing our understanding of neurodegenerative diseases.
癫痫的特征是由大脑中异常电活动引起的反复发作的癫痫发作。这些癫痫发作表现为各种症状，包括肌肉收缩和意识丧失。检测癫痫发作的一项具有挑战性的任务是将脑电图（EEG）信号分类为发作期（癫痫发作）和发作间期（无癫痫发作）类别。这种分类至关重要，因为它可以区分癫痫患者的癫痫发作状态和无癫痫发作期。我们的研究提出了一种利用图神经网络通过脑电图信号检测癫痫发作和神经疾病的创新方法。该方法有效地解决了脑电图数据处理的挑战。我们通过提取基于频率、基于统计和Daubechies小波变换特征等特征来构建脑电图信号的图表示。这种图表示通过对提取的特征进行目视检查，有可能区分癫痫发作和非癫痫发作信号。为了提高癫痫发作检测的准确性，我们采用了两种模型：一种是将图卷积网络（GCN）与长短期记忆（LSTM）相结合，另一种是将GCN与平衡随机森林（BRF）相结合。我们的实验结果表明，这两种模型都显著提高了癫痫发作检测的准确性，超过了以前的方法。尽管通过减少通道简化了我们的方法，但我们的研究显示了一致的性能，表明在神经退行性疾病检测方面有了显著进展。我们的模型能够准确识别脑电图信号中的癫痫发作，突出了图神经网络的潜力。这种简化方法不仅在通道较少的情况下保持有效性，而且还提供了一种视觉上可区分的方法来辨别癫痫发作类别。这项研究为脑电图分析开辟了道路，强调了图表示在推进我们对神经退行性疾病理解方面的影响。
