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Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform
Epilepsy is a widely spread neurological disorder caused due to the abnormal excessive
neural activity which can be diagnosed by inspecting the electroencephalography (EEG) …
neural activity which can be diagnosed by inspecting the electroencephalography (EEG) …
Emerging energy-efficient biosignal-dedicated circuit techniques: A tutorial brief
High spatiotemporal resolution biosignal that is vital for biomedical applications results in an
information bottleneck that poses challenges for their transferring and processing. The …
information bottleneck that poses challenges for their transferring and processing. The …
Detection of ADHD from EEG signals using different entropy measures and ANN
R Catherine Joy, S Thomas George… - Clinical EEG and …, 2022 - journals.sagepub.com
Attention deficit hyperactivity disorder (ADHD) is a prevalent behavioral, cognitive,
neurodevelopmental pediatric disorder. Clinical evaluations, symptom surveys, and …
neurodevelopmental pediatric disorder. Clinical evaluations, symptom surveys, and …
Detection of lung cancers from ct images using a deep CNN architecture in layers through ML
Lung inflammation is caused by the development of cancer cells. As the frequency of cancer
rises, men and women are dying at a higher rate. With malignancy, cancerous cells multiply …
rises, men and women are dying at a higher rate. With malignancy, cancerous cells multiply …
Extracting epileptic features in EEGs using a dual-tree complex wavelet transform coupled with a classification algorithm
The detection of epileptic seizures from electroencephalogram (EEG) signals is traditionally
performed by clinical experts through visual inspection. It is a long process, is error prone …
performed by clinical experts through visual inspection. It is a long process, is error prone …
A new approach for automatic detection of focal EEG signals using wavelet packet decomposition and quad binary pattern method
A comprehensive feature representation for electroencephalogram (EEG) signal to achieve
effective epileptic focus localization using a one-dimensional quad binary pattern (QBP) is …
effective epileptic focus localization using a one-dimensional quad binary pattern (QBP) is …
A machine learning-based method to identify bipolar disorder patients
J Mateo-Sotos, AM Torres, JL Santos… - Circuits, Systems, and …, 2022 - Springer
Bipolar disorder is a serious psychiatric disorder characterized by periodic episodes of
manic and depressive symptomatology. Due to the high percentage of people suffering from …
manic and depressive symptomatology. Due to the high percentage of people suffering from …
Multibit local neighborhood difference pattern optimization for seizure detection of west syndrome EEG signals
West syndrome is a common age-related epileptic encephalopathy, which seriously
threatens children's intellectual development. The accurate detection of seizures in the …
threatens children's intellectual development. The accurate detection of seizures in the …
Local Pattern Transformation-Based convolutional neural network for sleep stage scoring
Sleep stage scoring is essential for the diagnosis and treatment of sleep disorders.
However, manual sleep scoring is a tedious, time-consuming, and subjective task …
However, manual sleep scoring is a tedious, time-consuming, and subjective task …
Automatic localization of seizure onset zone from high-frequency SEEG signals: A preliminary study
Objective: Stereoelectroencephalogram (SEEG) has been widely adapted to detect the
electrical activity of patients with epilepsy. Due to the low-quality, large-amount, high …
electrical activity of patients with epilepsy. Due to the low-quality, large-amount, high …