This study aims to identify the epileptogenic zone (EZ) during the interictal period in epilepsy patients using electrocorticography data from four individuals. The proposed localization method, which constructs two brain connectivity networks: autoregressive and directed transfer function networks, holds significant potential. Network features are extracted using graph theory techniques employed in machine learning models to classify electrode locations as either part of the EZ. Six node features from the directed graph are selected: indegree, outdegree, cluster coefficient, PageRank, hubs, and community. A balanced support vector machine (SVM) addressed data imbalance. The balanced SVM method achieves the accuracy, precision, and recall of 0.775, 0.475, and 0.554, respectively. The results suggest that the node features of the epileptic network may provide critical information for clinical EZ localization, offering a promising avenue for future research and clinical practice.
The electroencephalogram (EEG) is vital for analyzing brain electrical activity in medical diagnosis and research. Aiming at the limitations of large size, high price, and inability to monitor EEG activity daily, a wireless wearable EEG measuring device was designed for patients with epilepsy. The designed system, with its low-power Bluetooth module, EEG acquisition module, and motion module, is not only efficient but also user-friendly. The system works at a sampling rate of 250 Hz for 8 channels and transmits data to a host computer or cell phone via Bluetooth. In addition, head movements are also recorded for behavior analysis. The results showed that the designed system has low noise and high-resolution performance, meeting the requirements for daily EEG measurement. The key benefit of this new device is its convenience and efficiency, providing a more user-friendly and effective tool for EEG monitoring, which could benefit seizure recording for patients with epilepsy in a home environment.
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