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Biomedical signals based computer-ai...

Murugappan, M.

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Biomedical signals based computer-aided diagnosis for neurological disorders

Record Type:	Electronic resources : Monograph/item
Title/Author:	Biomedical signals based computer-aided diagnosis for neurological disorders/ edited by M. Murugappan, Yuvaraj Rajamanickam.
other author:	Murugappan, M.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	x, 289 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Abnormal EEG detection using time-frequency images and convolutional neural network. -- 2. Physical action categorization pertaining to certain neurological disorders using machine learning based signal analysis -- 3. A comparative study on EEG features for neonatal seizure detection. -- 4. Hilbert huang transform (HHT) analysis of heart rate variability (HRV) in recognition of emotion in children with autism spectrum disorder (ASD) -- 5. Detection of tonic-clonic seizures using scalp EEG of spectral moments. -- 6. Investigation of the brain activation pattern of stroke patients and healthy individuals during happiness and sadness -- 7. A novel parametric non-stationary signal model for EEG signals and its application in epileptic seizure detection -- 8. Biomedical signal analysis using entropy measures: A case study of motor imaginary BCI in end-users with disability -- 9. Automatic detection of epilepsy using CNN-GRU hybrid model -- 10. Catalogic systematic literature review of hardware-accelerated neurodiagnostic -- 11. Wearable Real-time Epileptic Seizure Detection and Warning System -- 12. Analysis of Intramuscular Coherence of Lower Limb Muscles Activities using Magnitude Squared Coherence.
Contained By:	Springer Nature eBook
Subject:	Nervous system - Diseases -
Online resource:	https://doi.org/10.1007/978-3-030-97845-7
ISBN:	9783030978457

Biomedical signals based computer-aided diagnosis for neurological disorders
Biomedical signals based computer-aided diagnosis for neurological disorders[electronic resource] /edited by M. Murugappan, Yuvaraj Rajamanickam. - Cham :Springer International Publishing :2022. - x, 289 p. :ill., digital ;24 cm.

1. Abnormal EEG detection using time-frequency images and convolutional neural network. -- 2. Physical action categorization pertaining to certain neurological disorders using machine learning based signal analysis -- 3. A comparative study on EEG features for neonatal seizure detection. -- 4. Hilbert huang transform (HHT) analysis of heart rate variability (HRV) in recognition of emotion in children with autism spectrum disorder (ASD) -- 5. Detection of tonic-clonic seizures using scalp EEG of spectral moments. -- 6. Investigation of the brain activation pattern of stroke patients and healthy individuals during happiness and sadness -- 7. A novel parametric non-stationary signal model for EEG signals and its application in epileptic seizure detection -- 8. Biomedical signal analysis using entropy measures: A case study of motor imaginary BCI in end-users with disability -- 9. Automatic detection of epilepsy using CNN-GRU hybrid model -- 10. Catalogic systematic literature review of hardware-accelerated neurodiagnostic -- 11. Wearable Real-time Epileptic Seizure Detection and Warning System -- 12. Analysis of Intramuscular Coherence of Lower Limb Muscles Activities using Magnitude Squared Coherence.

Biomedical signals provide unprecedented insight into abnormal or anomalous neurological conditions. The computer-aided diagnosis (CAD) system plays a key role in detecting neurological abnormalities and improving diagnosis and treatment consistency in medicine. This book covers different aspects of biomedical signals-based systems used in the automatic detection/identification of neurological disorders. Several biomedical signals are introduced and analyzed, including electroencephalogram (EEG), electrocardiogram (ECG), heart rate (HR), magnetoencephalogram (MEG), and electromyogram (EMG) It explains the role of the CAD system in processing biomedical signals and the application to neurological disorder diagnosis. The book provides the basics of biomedical signal processing, optimization methods, and machine learning/deep learning techniques used in designing CAD systems for neurological disorders. Presents the concepts of CAD for various neurological disorders; Covers biomedical signal processing and machine learning/deep learning techniques; Includes case studies, real-time examples, and research directions.

ISBN: 9783030978457

Standard No.: 10.1007/978-3-030-97845-7doiSubjects--Topical Terms:

702980
Nervous system
--Diseases

LC Class. No.: RC348 / .B56 2022

Dewey Class. No.: 616.80475

Biomedical signals based computer-aided diagnosis for neurological disorders
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505 0 $a 1. Abnormal EEG detection using time-frequency images and convolutional neural network. -- 2. Physical action categorization pertaining to certain neurological disorders using machine learning based signal analysis -- 3. A comparative study on EEG features for neonatal seizure detection. -- 4. Hilbert huang transform (HHT) analysis of heart rate variability (HRV) in recognition of emotion in children with autism spectrum disorder (ASD) -- 5. Detection of tonic-clonic seizures using scalp EEG of spectral moments. -- 6. Investigation of the brain activation pattern of stroke patients and healthy individuals during happiness and sadness -- 7. A novel parametric non-stationary signal model for EEG signals and its application in epileptic seizure detection -- 8. Biomedical signal analysis using entropy measures: A case study of motor imaginary BCI in end-users with disability -- 9. Automatic detection of epilepsy using CNN-GRU hybrid model -- 10. Catalogic systematic literature review of hardware-accelerated neurodiagnostic -- 11. Wearable Real-time Epileptic Seizure Detection and Warning System -- 12. Analysis of Intramuscular Coherence of Lower Limb Muscles Activities using Magnitude Squared Coherence.
520 $a Biomedical signals provide unprecedented insight into abnormal or anomalous neurological conditions. The computer-aided diagnosis (CAD) system plays a key role in detecting neurological abnormalities and improving diagnosis and treatment consistency in medicine. This book covers different aspects of biomedical signals-based systems used in the automatic detection/identification of neurological disorders. Several biomedical signals are introduced and analyzed, including electroencephalogram (EEG), electrocardiogram (ECG), heart rate (HR), magnetoencephalogram (MEG), and electromyogram (EMG) It explains the role of the CAD system in processing biomedical signals and the application to neurological disorder diagnosis. The book provides the basics of biomedical signal processing, optimization methods, and machine learning/deep learning techniques used in designing CAD systems for neurological disorders. Presents the concepts of CAD for various neurological disorders; Covers biomedical signal processing and machine learning/deep learning techniques; Includes case studies, real-time examples, and research directions.
650 0 $a Nervous system $x Diseases $x Diagnosis. $3 702980
650 0 $a Medical informatics. $3 661258
650 1 4 $a Biomedical Engineering and Bioengineering. $3 3381533
650 2 4 $a Signal, Speech and Image Processing. $3 3592727
650 2 4 $a Neurological Disorders. $3 3594206
650 2 4 $a Radiology. $3 894545
650 2 4 $a Health Informatics. $3 892928
650 2 4 $a Computer Vision. $3 3538524
700 1 $a Murugappan, M. $3 3490730
700 1 $a Rajamanickam, Yuvaraj. $3 3601889
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773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-97845-7
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