## 1 Introduction  
Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has initiated a new era of technological breakthroughs in healthcare. These advanced technologies are revolutionizing the way we handle and utilize medical data, enhancing disease classification, assisting in improving diagnostic accuracy, improving the quality of medical services, and enhancing patient treatment outcomes [1]. In recent years, there has been a surge of research combining complex networks and machine learning to diagnose diseases and distinguish sleep states. For instance, in 2017, [PERSON] et al. proposed a method to map single-channel EEG signals into visibility graphs (VGs), including classical visibility graphs (VGs), horizontal visibility graphs (HVGs), and difference visibility graphs (DVGs). Through the use of a support vector machine (SVM) classifier, combining VGS featuresextracted from an EEG channel can improve seizure detection performance (the sensitivity of the benchmark feature set is 24%, with a false detection rate of 1.8 times per hour; the sensitivity after combining VGS features is 38%, with a false detection rate of 1.4 times per hour) [2]. In 2020, [PERSON] et al., based on the quantile graph (QG) method, effectively distinguished healthy individuals with open and closed eyes from Alzheimer's disease patients using five network topology measures, and the authors pointed out the best distinguishing channel, verifying the effectiveness of the QG method in analyzing complex nonlinear EEG signals [3]. [PERSON] et al. proposed an improved quantile graph construction method based on Markov transition probability equidistant interval division strategy, which can effectively distinguish between different EEG signals [4]. In 2021, [PERSON] et al. proposed a new method named GraphTS, which maps EMG time series into complex networks using a visibility graph and achieves high accuracy in distinguishing between healthy and patient samples using deep neural networks [5]. In 2021, [PERSON] et al. proposed a graph-time fusion dual-input CNN method combining complex networks and deep learning. They segmented each single-channel EEG signal into non-overlapping 30 s epochs and mapped each epoch into a limited penetrable visibility graph (LPVG) to obtain degree sequences (DSs). Finally, they combined DSs and the 30 s EEG epochs as inputs for the novel graph-time fusion dual-input CNN to detect sleep stages, achieving a classification accuracy of 87.21% for six states [6]. In 2021, [PERSON] et al. proposed the use of improved Hjorth features and non-parametric classifiers to classify various emotional states in skin conductance activity (EDA) signals. They found that the combination of improved Hjorth features and rotation forests achieved the highest accuracy in classifying emotional states [7]. In 2023, [PERSON] et al. applied six analysis methods, including quantile graphs and visibility graphs, to analyze EEG signals from 160 Alzheimer's patients and 24 healthy controls. They found that methods like wavelet coherence and quantile graphs can robustly distinguish patients from healthy individuals, demonstrating their potential for non-invasive and low-cost Alzheimer's disease detection [8]. Epilepsy is a chronic neurological disorder caused by abnormal discharges of neurons in the brain. The severe convulsions during an epileptic seizure can lead to muscle tears or other internal injuries, and patients may suddenly lose consciousness and fall. Long-term suffering from epilepsy can result in decreased self-esteem and social isolation, potentially leading to psychological issues and cognitive impairments, severely impacting the patient's life. Therefore, the timely and accurate detection of seizures is crucial to mitigating these hazards for patients. Machine learning has great potential in electroencephalography (EEG) test classification, and its accuracy relies heavily on feature extraction [9]. Therefore, feature extraction is crucial.