Efficient multiscale weighted features based residual ConvLSTM method to detect Parkinson's disease using electroencephalogram data

Parkinson's Disease (PD) is a neurological disease that affects the psychological and neural systems. Various factors, including age, medications, and disease state, can affect the Electroencephalogram (EEG) signal. It becomes difficult to establish common features to identify PD. So, this research developed a powerful PD detection model using deep learning to overcome such challenges. Initially, data is taken from different sources. Here, the wave features, temporal features, spatial features, spectral features, and deep features are extracted from the collected data, where the deep features is extracted using the Autoencoder (AE). Then, the extracted features are fed into the Multiscale Weighted Features-based Residual Convolutional Long Short Term Memory (MWF-RconvLSTM). The multiscale weighted features incorporated in the developed model can effectively solve the complexity issue while detecting the disease. Further, convolutional LSTM in the proposed model significantly enhances the model's ability to understand complex features in the detection of PD. Here, the weights are optimized using the developed Enhanced Peafowl Optimization Algorithm (EPOA). Weight optimization using the developed EPOA can enhance the effectiveness of PD detection. Moreover, the developed model is evaluated with various models to display the effective performance in detecting PD. Finally, the developed EPOA-MWF-RconvLSTM model offers the best result in terms of accuracy is 94.97. Moreover, the conventional model like DMO-MWF-RconvLSTM, BFGO-MWF-RconvLSTM, RHA-MWF-RconvLSTM, and POA-MWF-RconvLSTM achieved the accuracy to be 80.02, 88.49, 82.01, and 90.87. This confirmed that the recommended model is more successful in the detection of PD than other existing models.