Automated accurate schizophrenia detection system using Collatz pattern technique with EEG signals
Background Schizophrenia (SZ) is one of the prevalent mental ailments worldwide and is
manually diagnosed by skilled medical professionals. Nowadays electroencephalogram …
manually diagnosed by skilled medical professionals. Nowadays electroencephalogram …
An adaptive optimized schizophrenia electroencephalogram disease prediction framework
Electroencephalogram (EEG) signal analysis has become an interesting and required area
in the medical industry to analyze brain function for different diseases. But, the EEG signal's …
in the medical industry to analyze brain function for different diseases. But, the EEG signal's …
Application of local configuration pattern for automated detection of schizophrenia with electroencephalogram signals
Recently, a mix of traditional and modern approaches have been proposed to detect brain
abnormalities using bio‐signal/bio‐image‐assisted methods. In hospitals, most of the …
abnormalities using bio‐signal/bio‐image‐assisted methods. In hospitals, most of the …
Detection and Classification of ADHD from EEG Signals Using Tunable Q‐Factor Wavelet Transform
The automatic identification of Attention Deficit Hyperactivity Disorder (ADHD) is essential for
developing ADHD diagnosis tools that assist healthcare professionals. Recently, there has …
developing ADHD diagnosis tools that assist healthcare professionals. Recently, there has …
[PDF][PDF] Signal Conducting System with Effective Optimization Using Deep Learning for Schizophrenia Classification.
Signal processing based research was adopted with Electroencephalogram (EEG) for
predicting the abnormality and cerebral activities. The proposed research work is intended …
predicting the abnormality and cerebral activities. The proposed research work is intended …
Clinical Sensitivity of Fractal Neurodynamics
Among the significant advances in the understanding of the organization of the neuronal
networks that coordinate the body and brain, their complex nature is increasingly important …
networks that coordinate the body and brain, their complex nature is increasingly important …
Machine learning systems for detecting schizophrenia
NV Swati, M Indiramma - … Conference on I-SMAC (IoT in Social …, 2020 - ieeexplore.ieee.org
Schizophrenia is a neurological disorder which has drawn a lot of attention around the
world. It includes symptoms such as social withdrawal, hallucinations, delusions, confused …
world. It includes symptoms such as social withdrawal, hallucinations, delusions, confused …
[HTML][HTML] Classifier implementation for spontaneous eeg activity during schizophrenic psychosis
The mental illness or abnormal brain is recorded with EEG, and it records corollary
discharge, which helps to identify the schizophrenia spontaneous situation of a patient. The …
discharge, which helps to identify the schizophrenia spontaneous situation of a patient. The …
Computerised detection of autism spectrum disorder using EEG signals
A Sharma - … Journal of Medical Engineering and Informatics, 2024 - inderscienceonline.com
Autism spectrum disorder (ASD) is one of the most common neurological disorders.
Detection of ASD is based on behavioural analysis made by clinician by conducting …
Detection of ASD is based on behavioural analysis made by clinician by conducting …
Machine Learning Approach to Predict ADHD Types using EEG Signal Data
S Samyuktha, MB Anandaraju - 2023 International Conference …, 2023 - ieeexplore.ieee.org
Early diagnosis and treatment of attention deficit hyperactivity disorder (ADHD) in children is
essential for their overall wellbeing. A recent study introduced a method to classify ADHD as …
essential for their overall wellbeing. A recent study introduced a method to classify ADHD as …