Nov 29, 2022 · In this paper, we propose a deep learning framework namely SE-1DCNN-LSTM to automatically learn the latent EEG features of the two subtypes.

Accurate diagnosis can provide effective treatment for patients. In this paper, we propose a deep learning framework namely SE-1DCNN-LSTM to automatically learn ...

Accurate diagnosis can provide effective treatment for patients. In this paper, we propose a deep learning framework namely SE-1DCNN-LSTM to automatically learn ...

SE-1DCNN-LSTM: A Deep Learning Framework for EEG-Based Automatic Diagnosis of Major Depressive Disorder and Bipolar Disorder. https://doi.org/10.1007/978-981 ...

In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have ...

SE-1DCNN-LSTM: A Deep Learning Framework for EEG-Based Automatic Diagnosis of Major Depressive Disorder and Bipolar Disorder. 60-72. view. electronic edition ...

Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms ...

Jul 13, 2017 · This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD.

Missing: SE- | Show results with:SE-

SE-1DCNN-LSTM: A Deep Learning Framework for EEG-Based Automatic Diagnosis of Major Depressive Disorder and Bipolar Disorder; 1 Introduction; 2 Materials and ...