Development of EMG based Epileptic Seizure Detection using LSTM Approach

Epilepsy is a neurological disorder affecting millions globally, marked by unpredictable seizures that can severely disrupt daily life. Timely and accurate seizure detection is crucial for improving patient outcomes and enabling better management of the condition. To address these issues EMG (Electromyography) based epileptic seizure detection using LSTM (Long Short-Term Memory) approach is proposed in this paper. The proposed model employees data acquisition by collecting emg database and pre processing with band pass filter followed by feature extraction and followed by validating using standard LSTM model. The proposed model accuracy , F1 Score is compared with standard CNN and RNN models. Using the proposed model an accuracy of 99% , precision of 89% and F1 score of 81% is achieved . The proposed seizure detection using LSTM model outperforms when compared with standard CNN and RNN model.