Schizophrenia detection using Entropy Difference-based Electroencephalogram Channel Selection Approach DOI

T. Sunil Kumar,

Shishir Maheshwari, Kandala N. V. P. S. Rajesh

et al.

Published: July 15, 2024

In this work, we propose a novel approach for identifying schizophrenia using an entropy difference (ED)- based electroencephalogram (EEG) channel selection algorithm. At the core of our is ED-based algorithm, which selects most significant EEG channels that contain discriminative information detection values. This process not only but also reduces computational complexity detection. After selecting channels, decompose selected signals into subbands discrete wavelet transform (DWT). Furthermore, extract symmetrically-weighted local binary patterns to capture subband variations. The features are then subjected support vector machine (SVM) differentiate individuals with on their signals. proposed achieves classification accuracy 100% when from one used, outperforming existing approaches in Also, outperforms entropy-based

Language: Английский

Automated detection and forecasting of COVID-19 using deep learning techniques: A review DOI
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

Citations

58
Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses DOI Creative Commons
Ismail BAYDİLİ, Burak Taşçı, Gülay TAŞCI

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(4), P. 434 - 434

Published: Feb. 11, 2025

Artificial intelligence (AI) has emerged as a transformative force in psychiatry, improving diagnostic precision, treatment personalization, and early intervention through advanced data analysis techniques. This review explores recent advancements AI applications within focusing on EEG ECG analysis, speech natural language processing (NLP), blood biomarker integration, social media utilization. EEG-based models have significantly enhanced the detection of disorders such depression schizophrenia spectral connectivity analyses. ECG-based approaches provided insights into emotional regulation stress-related conditions using heart rate variability. Speech frameworks, leveraging large (LLMs), improved cognitive impairments psychiatric symptoms nuanced linguistic feature extraction. Meanwhile, analyses deepened our understanding molecular underpinnings mental health disorders, analytics demonstrated potential for real-time surveillance. Despite these advancements, challenges heterogeneity, interpretability, ethical considerations remain barriers to widespread clinical adoption. Future research must prioritize development explainable models, regulatory compliance, integration diverse datasets maximize impact care.

Language: Английский

Citations

4
Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis DOI
Shraddha Jain, Ruchi Srivastava

Brain Topography, Journal Year: 2025, Volume and Issue: 38(3)

Published: Feb. 24, 2025

Language: Английский

Citations

2
Staphylococcus Aureus-Related antibiotic resistance detection using synergy of Surface-Enhanced Raman spectroscopy and deep learning DOI
Zakarya Al‐Shaebi,

Fatma Uysal Ciloglu,

Mohammed Nasser

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 91, P. 105933 - 105933

Published: Jan. 10, 2024

Language: Английский

Citations

10
Diagnosis of Schizophrenia in EEG Signals Using dDTF Effective Connectivity and New PreTrained CNN and Transformer Models DOI
Afshin Shoeibi, Marjane Khodatars,

Hamid Alinejad-Rorky

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 150 - 160

Published: Jan. 1, 2024

Language: Английский

Citations

6
Application of Transfer Learning for Biomedical Signals: A Comprehensive Review of the Last Decade (2014-2024) DOI Creative Commons
Mahboobeh Jafari, Xiaohui Tao, Prabal Datta Barua

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: 118, P. 102982 - 102982

Published: Jan. 30, 2025

Language: Английский

Citations

0
Enhanced classification of motor imagery EEG signals using spatio-temporal representations DOI

Renjie Lv,

Wenwen Chang, Guanghui Yan

et al.

Information Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 122221 - 122221

Published: April 1, 2025

Language: Английский

Citations

0
Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records DOI Creative Commons
Mamadou Dia,

Ghazaleh Khodabandelou,

Syed Muhammad Anwar

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 2, 2025

Mental disorders represent a critical global health challenge that affects millions around the world and significantly disrupts daily life. Early accurate detection is paramount for timely intervention, which can lead to improved treatment outcomes. Electroencephalography (EEG) provides non-invasive means observing brain activity, making it useful tool detecting potential mental disorders. Recently, deep learning techniques have gained prominence their ability analyze complex datasets, such as electroencephalography recordings. In this study, we introduce novel deep-learning architecture classification of post-traumatic stress disorder, depression, or anxiety, using data. Our proposed model, multichannel convolutional transformer, integrates strengths both neural networks transformers. Before feeding model low-level features, input pre-processed common spatial pattern filter, signal space projection wavelet denoising filter. Then EEG signals are transformed continuous transform obtain time-frequency representation. The layers tokenize by our pre-processing pipeline, while Transformer encoder effectively captures long-range temporal dependencies across sequences. This specifically tailored process data has been preprocessed transform, technique representation, thereby enhancing extraction relevant features classification. We evaluated performance on three datasets: Psychiatric Dataset, MODMA dataset, Psychological Assessment dataset. achieved accuracies 87.40% 89.84% 92.28% approach outperforms every concurrent approaches datasets used, without showing any sign over-fitting. These results underscore in delivering reliable disorder through analysis, paving way advancements early diagnosis strategies.

Language: Английский

Citations

0
Automated seizure detection in epilepsy using a novel dynamic temporal-spatial graph attention network DOI Creative Commons

Kunxian Yan,

Xiangyu Luo, Lei Ye

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 12, 2025

Epilepsy is a neurological disorder characterized by recurrent seizures caused excessive electrical discharges in brain cells, posing significant diagnostic and therapeutic challenges. Dynamic network analysis via electroencephalography (EEG) has emerged as powerful tool for capturing transient functional connectivity changes, offering advantages over static networks. In this study, we propose Temporal-Spatial Graph Attention Network (DTS-GAN) to address the limitations of fixed-topology graph models analysing time-varying By integrating signal processing with hybrid deep learning framework, DTS-GAN collaboratively extracts spatiotemporal features through two key modules: an LSTM-based temporal encoder model long-term dependencies EEG sequences, dynamic attention probabilistic Gaussian connectivity, enabling adaptive interactions across electrode nodes. Experiments on TUSZ dataset demonstrate that achieves 89-91% accuracy weighted F1-score 87-91% classifying seven seizure types, significantly outperforming baseline models. The multi-head mechanism generation strategy effectively resolve variability connectivity. These results highlight potential providing precise automated detection, serving robust clinical analysis.

Language: Английский

Citations

0
Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics DOI Creative Commons
Thawirasm Jungrungrueang, Sawrawit Chairat,

Kasidach Rasitanon

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 19, 2025

Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis are essential for timely accurate treatment, improving patient outcomes quality of life. This study investigated dynamic features resting-state electroencephalography (EEG) functional connectivity to identify characteristic patterns dementia subtypes, such as Alzheimer's disease (AD) frontotemporal (FD), evaluate their potential biomarkers. We extracted distinctive statistical features, including mean, variance, skewness, Shannon entropy, from brain measures, revealing common alterations in dementia, specifically generalized disruption Alpha-band connectivity. Distinctive characteristics were found, Delta-band hyperconnectivity with increased complexity AD disrupted phase-based Theta, Beta, Gamma bands FD. also employed convolutional neural network model, enhanced these differentiate between subtypes. Our classification models achieved multiclass accuracy 93.6% across AD, FD, healthy control groups. Furthermore, the model demonstrated 97.8% 96.7% differentiating FD controls, respectively, 97.4% classifying pairwise classification. These establish high-performance deep learning framework utilizing EEG biomarkers, offering promising approach early disorders using EEG. findings suggest that analyzing dynamics during tasks could offer more valuable information diagnosis, assessing severity, potentially identifying personalized neurological deficits.

Language: Английский

Citations

0