Advanced RNN Approach for Classifying Electrophysiological Signals

LSTM with hybrid deep learning architectures, transformer models, 1D, 2D, 3D RNNs models and smart pre-processing methods (noise reduction, filtering, dimension reducing etc.) captures non-linear and non-stationary characteristics of electrophysiological signals for automated classification with high-performance results. The model covers activation functions like Mish and employs the approaches of multimodal fusion for assuring the diagnosis and attains the classification accuracy of more than 90% over benchmark. In addition, novel approaches of feature engineering (order transition patterns extraction) and AI explainability contribution helps to increase the model interpretability with the symbolic representations of the language of neural connectivity. The framework offers a lot of promise for real-time brain computers interfaces, or detecting when someone is having an arrhythmia or classifying a person's mental state or assistive technology for people with motor disabilities.