ArticleHealth information science and systems2021
Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain.
Article in Health information science and systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 83 citations in OpenAlex.
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- Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Multidimensional EEG features integration with feature selection strategy for precision diagnosis of depressive disorders.Frontiers in psychiatry · 2025Article
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- Risk of data leakage in estimating the diagnostic performance of a deep-learning-based computer-aided system for psychiatric disorders.Scientific reports · 2023Article
- Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset.Computational intelligence and neuroscience · 2023Article
- Current development of biosensing technologies towards diagnosis of mental diseases.Frontiers in bioengineering and biotechnology · 2023Review
- Microstate feature fusion for distinguishing AD from MCI.Health information science and systems · 2022Article
- Identification of normal and depression EEG signals in variational mode decomposition domain.Health information science and systems · 2022Article
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- Intelligent Analysis of Exercise Health Big Data Based on Deep Convolutional Neural Network.Computational intelligence and neuroscience · 2022Article
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- Detection of the quality of vital signals by the Monte Carlo Markov Chain (MCMC) method and noise deleting.Health information science and systems · 2021Article
- SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot recognition.Wireless communications & mobile computing · 2021Article
- A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction.Entropy (Basel, Switzerland) · 2021Article
- Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A widespread brain disorder of present days is depression which influences 264 million of the world's population. Depression may cause diverse undesirable consequences, including poor physical health, suicide, and self-harm if left untreated. Depression may have adverse effects on the personal, social, and professional lives of individuals. Both neurologists and researchers are trying to detect depression by challenging brain signals of Electroencephalogram (EEG) with chaotic and non-stationary characteristics. It is essential to detect early-stage depression to help patients obtain the best treatment promptly to prevent harmful consequences. In this paper, we proposed a new method based on centered correntropy (CC) and empirical wavelet transform (EWT) for the classification of normal and depressed EEG signals. The EEG signals are decomposed to rhythms by EWT and then CC of rhythms is computed as the discrimination feature and fed to K-nearest neighbor and support vector machine (SVM) classifiers. The proposed method was evaluated using EEG signals recorded from 22 depression and 22 normal subjects. We achieved 98.76%, 98.47%, and 99.05% average classification accuracy (ACC), sensitivity, and specificity in a 10-fold cross-validation strategy by using an SVM classifier. Such efficient results conclude that the method proposed can be used as a fast and accurate computer-aided detection system for the diagnosis of patients with depression in clinics and hospitals.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.