| dc.description.abstract | Epilepsy is a common brain disease that is caused by abnormal discharging from overactive nerve cells in the brain. Electroencephalography (EEG) is one of the main diagnostic measurements for epilepsy detection. EEG based signal analysis in epilepsy seizures have been studied to find out brain wave patterns of patients being diagnosed with/without epilepsy diseases. However, doctors usually do not recommend using epileptiform patterns as epilepsy detection features for stroke patients, whose abnormal brain signals originating from certain brain injuries. Therefore, we design a series of brain signal processing to extract discriminative features for a machine-learning-driven epilepsy detection on stroke patients.
The experimental data came from Taipei Veterans General Hospital (TVGH), Taiwan. A total of 831 stroke patients from 2012 to 2017 had been measured their EEGs using the 10-20 system. Different light hertz had been used as external stimulations. A total of 1,323 EEGs were collected in a state sequence of “resting, light stimulation, resting”. We firstly removed signal noises and strengthened features through brainwave preprocessing such as notch filtering, bandpass filtering, epochs, and CSP. We then used a wavelet transform method to separate brainwaves in terms of different frequency bands. Several statistical methods were extracted to obtain informative and non-redundant features. Finally, we exploited well-known machine learning models to detect epilepsy seizures on stroke patients. Based on a series of experiments and their result analysis, we find that the Logistic Regression model with four features (coherence, entropy, kurtosis, and skewness) significantly outperforms machine-learning-based epilepsy detection methods and EEG-based deep learning models, achieving the best F1 score of 0.7192, sensitivity of 0.4479 and specificity of 0.8313 from EEG data (each duration is 1 minutes and 54 seconds). Besides, we implemented a windows based programming interface, with which doctors can select epilepsy detection models and observe EEG distribution with the time change for clinical decision supports. This is our pilot study of this research issue. Hope our explorations can benefit the studies for EEG based epilepsy detection on stroke patients. | en_US |