fNIRS Classification of Adults with ADHD Enhanced by Feature Selection DOI Creative Commons
Min Hong,

Suh-Yeon Dong,

Roger S. McIntyre

et al.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2024, Volume and Issue: 33, P. 220 - 231

Published: Dec. 24, 2024

Adult attention deficit hyperactivity disorder (ADHD), a prevalent psychiatric disorder, significantly impacts social, academic, and occupational functioning. However, it has been relatively less prioritized compared to childhood ADHD. This study employed functional near-infrared spectroscopy (fNIRS) during verbal fluency tasks in conjunction with machine learning (ML) techniques differentiate between healthy controls (N=75) ADHD individuals (N=120). Efficient feature selection high-dimensional fNIRS datasets is crucial for improving accuracy. To address this, we propose hybrid method that combines wrapper-based embedded approach, termed Bayesian-Tuned Ridge RFECV (BTR-RFECV). The proposed facilitated streamlined hyperparameter tuning data, thereby reducing the number of features while enhancing HbO from combined frontal temporal regions were key, models achieving precision (89.89%), recall (89.74%), F-1 score (89.66%), accuracy MCC (78.36%), GDR (88.45%). outcomes this highlight promising potential combining ML as diagnostic tools clinical settings, offering pathway reduce manual intervention.

Language: Английский

Accurate identification of anxiety and depression based on the dlPFC in an emotional autobiographical memory task: A machine learning approach DOI
Guixiang Wang, Yusen Huang, Yan Zhang

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 104, P. 107503 - 107503

Published: Jan. 18, 2025

Language: Английский

Citations

0
Advanced autism detection and visualization through XGBoost algorithm for fNIRS hemo-dynamic signals DOI Creative Commons
Sajid Farooq, Hailun He, Deyu Guo

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127013 - 127013

Published: March 1, 2025

Language: Английский

Citations

0
Identification of Subthreshold Depression Based on fNIRS–VFT Functional Connectivity: A Machine Learning Approach DOI Creative Commons
Lin Li,

Jingxuan Liu,

Yifan Zheng

et al.

Depression and Anxiety, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Background: Subthreshold depression (SD) is regarded as a prodromal stage and substantial risk factor for major depressive disorder (MDD). The timely identification of SD critical clinical significance. This study aimed to develop machine learning (ML) classification model the individuals with using functional near‐infrared spectroscopic imaging (fNIRS) verbal fluency task (VFT). Methods: recruited total 70 participants matched 73 healthy controls (HCs) differentiate between two groups based on connectivity (FC) features during fNIRS–VFT, an interpretable random forest (RF) model. Results: RF demonstrated area under curve (AUC) 0.77, accuracy (ACC) 75.86%, sensitivity 75.00%, specificity 76.00% F1 score 0.75 identifying SD. highest‐ranked FC features, in terms importance, were identified Channel (CH) 26 (the right frontal eye fields (FEFs)) CH 30 FEF), 3 left premotor supplementary motor cortex (PMC‐and‐SMA)) 42 PMC‐and‐SMA), well FEF) 32 primary somatosensory (PSC)). Conclusion: has capacity effectively classify efficacy abnormal particularly FEF, bilateral PSC PMC‐and‐SMA. findings this have provided foundation large‐scale screening populations, offering promising opportunities early diagnosis prevention MDD.

Language: Английский

Citations

0
Disclosing the Complexities of Childhood Neurodevelopmental Disorders DOI Creative Commons
Luigi Tarani, Marco Fiore

Children, Journal Year: 2024, Volume and Issue: 12(1), P. 16 - 16

Published: Dec. 25, 2024

Neurodevelopmental disorders represent an important and complex area of pediatric medicine, including a wide range conditions affecting brain nervous system functioning during development [...].

Language: Английский

Citations

1
Functional Near‐Infrared Spectroscopy‐Based Computer‐Aided Diagnosis of Major Depressive Disorder Using Convolutional Neural Network with a New Channel Embedding Layer Considering Inter‐Hemispheric Asymmetry in Prefrontal Hemodynamic Responses DOI Creative Commons
Kyeonggu Lee, Jinuk Kwon, M. Chun

et al.

Depression and Anxiety, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

Functional near-infrared spectroscopy (fNIRS) is being extensively explored as a potential primary screening tool for major depressive disorder (MDD) because of its portability, cost-effectiveness, and low susceptibility to motion artifacts. However, the fNIRS-based computer-aided diagnosis (CAD) MDD using deep learning methods has rarely been studied. In this study, we propose novel framework based on convolutional neural network (CNN) CAD with high accuracy. The fNIRS data participants-48 patients 68 healthy controls (HCs)-were obtained while they performed Stroop task. hemodynamic responses calculated from preprocessed were used inputs proposed CNN model an ensemble architecture, comprising three 1D depth-wise layers specifically designed reflect interhemispheric asymmetry in between HCs, which known be distinct characteristic previous studies. performance was evaluated leave-one-subject-out cross-validation strategy compared those conventional machine models. exhibited accuracy, sensitivity, specificity 84.48%, 83.33%, 85.29%, respectively. accuracies algorithms-shrinkage linear discriminator analysis, regularized support vector machine, EEGNet, ShallowConvNet-were 73.28%, 74.14%, 62.93%, 62.07%, conclusion, can differentiate HCs more accurately than models, demonstrating applicability systems.

Language: Английский

Citations

0
fNIRS Classification of Adults with ADHD Enhanced by Feature Selection DOI Creative Commons
Min Hong,

Suh-Yeon Dong,

Roger S. McIntyre

et al.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2024, Volume and Issue: 33, P. 220 - 231

Published: Dec. 24, 2024

Adult attention deficit hyperactivity disorder (ADHD), a prevalent psychiatric disorder, significantly impacts social, academic, and occupational functioning. However, it has been relatively less prioritized compared to childhood ADHD. This study employed functional near-infrared spectroscopy (fNIRS) during verbal fluency tasks in conjunction with machine learning (ML) techniques differentiate between healthy controls (N=75) ADHD individuals (N=120). Efficient feature selection high-dimensional fNIRS datasets is crucial for improving accuracy. To address this, we propose hybrid method that combines wrapper-based embedded approach, termed Bayesian-Tuned Ridge RFECV (BTR-RFECV). The proposed facilitated streamlined hyperparameter tuning data, thereby reducing the number of features while enhancing HbO from combined frontal temporal regions were key, models achieving precision (89.89%), recall (89.74%), F-1 score (89.66%), accuracy MCC (78.36%), GDR (88.45%). outcomes this highlight promising potential combining ML as diagnostic tools clinical settings, offering pathway reduce manual intervention.

Language: Английский

Citations

0