Peng P. Wen - Publications

Muti-band Morlet mutual information functional connectivity for classifying Alzheimer's disease and frontotemporal dementia with a deep learning technique.

Journal: Computers in biology and medicine

Published: June 18, 2025

Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features.

Journal: Brain informatics

Published: June 17, 2024

An automatic method using MFCC features for sleep stage classification.

Journal: Brain informatics

Published: October 23, 2023

MixSleepNet: A Multi-Type Convolution Combined Sleep Stage Classification Model.

Journal: Computer methods and programs in biomedicine

Published: September 05, 2023

3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning.

Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

Published: August 28, 2023

A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network.

Journal: Heliyon

Published: June 15, 2023

Automatic identification of schizophrenia based on EEG signals using dynamic functional connectivity analysis and 3D convolutional neural network.

Journal: Computers in biology and medicine

Published: March 20, 2023

Automatic sleep spindles identification and classification with multitapers and convolution.

Journal: Sleep

Published: January 17, 2023

Exploring deep residual network based features for automatic schizophrenia detection from EEG.

Journal: Physical and engineering sciences in medicine

Published: November 02, 2022

Real-Time Depth of Anaesthesia Assessment Based on Hybrid Statistical Features of EEG.

Journal: Sensors (Basel, Switzerland)

Published: July 07, 2022

SchizoGoogLeNet: The GoogLeNet-Based Deep Feature Extraction Design for Automatic Detection of Schizophrenia.

Journal: Computational intelligence and neuroscience

Published: June 22, 2022

Jumping Knowledge Based Spatial-Temporal Graph Convolutional Networks for Automatic Sleep Stage Classification.

Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

Published: May 18, 2022