A Novel Hybrid Grid Search and Tree Parzen Estimator for Deep Learning Hyperparameters Optimization

Hyperparameter optimization plays a crucial role in maximizing the performance of Deep Learning (DL) models, particularly in the medical field. In this study, we propose a novel hybrid approach called GS-TPE, which combines Grid Search (GS) and Tree Parzen Estimator (TPE) for optimizing the hyperparameters of DL architectures in order to enhance the vigilance states classification from the EEG signals. Our experiments demonstrate that the GS-TPE approach competes with the state of the art on multiple performance metrics, leading to significantly improved classification results. The obtained accuracy with combined one-Dimensional Convolutional Neural Network and Long Short-Term Memory (1D-CNN-LSTM) and with combined Auto-Encoder and LSTM (AE-LSTM) architectures reach 93.74 and 93.53%, respectively. The proposed GS-TPE approach shows great promise for advancing the field of medical signal analysis and enhancing the accuracy of EEG-based diagnostic systems.