0% Complete
Home
/
13th International Conference on Computer and Knowledge Engineering
Diagnosis of Depression Based on New Features Extractive from the Frequency Space of the EEG
Authors :
Melika Changizi
1
Saeid Rashidi
2
1- Faculty of Medical Sciences and Technologies, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- Faculty of Medical Sciences and Technologies, Science and Research Branch, Islamic Azad University, Tehran, Iran
Keywords :
Classification،Depression،EEG،Spectral Features،Window Duration
Abstract :
Major depressive disorder (MDD) is a prevalent psychotic disorder. Understanding the neurophysiological characteristics of MDD is very important in diagnostic and therapeutic applications. The use of electroencephalography (EEG) is being developed to identify brain activity mechanisms in MDD patients. The present study aimed to examine the efficacy of spectral features extracted from EEG signals based on reductionism and relativism methods in the diagnosis of MDD. Using the Welch analysis method, the best results were calculated with 6 optimal frequency features corresponding to 5 channels on 12-second segments. Using the K-Nearest Neighbor classifier, criteria such as accuracy, F1 score and Kappa were obtained as 86.79%, 85.71% and 0.73 respectively.
Papers List
List of archived papers
Hate Sentiment Recognition System For Persian Language
Pegah Shams jey - Arash Hemmati - Ramin Toosi - Mohammad ali Akhaee
Novel Insights in Deep Learning for Predicting Climate Phenomena
Mohammad Naisipour - Saghar Ganji - Iraj Saeedpanah - Behnam Mehrakizadeh - Ahmad Reza Labibzadeh
Classification of COVID-19 and Nodule in CT Images using Deep Convolutional Neural Network
Amirhossein Ghaemi - Seyyed Amir Mousavi mobarakeh - Habibollah Danyali - Kamran Kazemi
Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition
Hamid Ahmadabadi - Omid Nejati Manzari - Ahmad Ayatollahi
New Design of Efficient Reversible Quantum Saturation Adder
Negin Mashayekhi - Mohammad Reza Reshadinezhad - Shekoofeh Moghimi
Real-Time Forecasting Using Mixed Frequency Time-Series Data
Armin Khayati - Mohammad Taheri - Koorush Ziarati
A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series
Ahmad Taheri - Shahriar Ghashghaei - Amin Beheshti - Keyvan RahimiZadeh
Word-level Persian Lipreading Dataset
Javad Peymanfard - Ali Lashini - Samin Heydarian - Hossein Zeinali - Nasser Mozayani
GroupRec: Group Recommendation by Numerical Characteristics of Groups in Telegram
Davod Karimpour - Mohammad Ali Zare Chahooki - Ali Hashemi
Leveraging Self-Supervised Models for Automatic Whispered Speech Recognition
Aref Farhadipour - Homa Asadi - Volker Dellwo
more
Samin Hamayesh - Version 41.3.1