Automated detection of ADHD: Current trends and future perspective
Attention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a
detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit …
detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit …
Evaluation of risk of bias in neuroimaging-based artificial intelligence models for psychiatric diagnosis: a systematic review
Z Chen, X Liu, Q Yang, YJ Wang, K Miao… - JAMA network …, 2023 - jamanetwork.com
Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques
Early detection of Parkinson's disease (PD) is very important in clinical diagnosis for
preventing disease development. In this study, we present efficient discrete wavelet …
preventing disease development. In this study, we present efficient discrete wavelet …
Parkinson's disease detection from resting-state EEG signals using common spatial pattern, entropy, and machine learning techniques
Parkinson's disease (PD) is a very common brain abnormality that affects people all over the
world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent …
world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent …
VHERS: a novel variational mode decomposition and Hilbert transform-based EEG rhythm separation for automatic ADHD detection
Attention deficit hyperactivity disorder (ADHD) is an isogenous pattern of hyperactivity,
impulsivity, and inattention, resulting in disorders like anxiety, disability in learning, and …
impulsivity, and inattention, resulting in disorders like anxiety, disability in learning, and …
Optimal channels and features selection based ADHD detection from EEG signal using statistical and machine learning techniques
Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and
neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based …
neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based …
Application of entropy for automated detection of neurological disorders with electroencephalogram signals: a review of the last decade (2012-2022)
An automated Neurological Disorder detection system can be considered as a cost-effective
and resource efficient tool for medical and healthcare applications. In automated …
and resource efficient tool for medical and healthcare applications. In automated …
Mild cognitive impairment detection with optimally selected EEG channels based on variational mode decomposition and supervised machine learning
Detecting mild cognitive impairment (MCI), which is typically the earliest stage of dementia,
is essential for managing dementia. Recently, researchers have explored the use of …
is essential for managing dementia. Recently, researchers have explored the use of …
A systematic literature review on traditional to artificial intelligence based socio-behavioral disorders diagnosis in India: Challenges and future perspectives
M Mengi, D Malhotra - Applied Soft Computing, 2022 - Elsevier
Background: Socio-behavioral disorders (SBD), a subtype of neurodevelopmental disorders
(NDDs) characterized by social and behavioral abnormalities, is a significant mental health …
(NDDs) characterized by social and behavioral abnormalities, is a significant mental health …
A biomarker discovery framework for childhood anxiety
Introduction Anxiety is the most common manifestation of psychopathology in youth,
negatively affecting academic, social, and adaptive functioning and increasing risk for …
negatively affecting academic, social, and adaptive functioning and increasing risk for …