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020			_a9783030978457
024	7		_a10.1007/978-3-030-97845-7 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
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245	1	0	_aBiomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders _h[electronic resource] / _cedited by M. Murugappan, Yuvaraj Rajamanickam.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_a1. 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			_aBiomedical 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	_aBiomedical engineering.
650		0	_aSignal processing.
650		0	_aNervous system—Diseases.
650		0	_aRadiology.
650		0	_aMedical informatics.
650		0	_aComputer vision.
650	1	4	_aBiomedical Engineering and Bioengineering.
650	2	4	_aSignal, Speech and Image Processing .
650	2	4	_aNeurological Disorders.
650	2	4	_aRadiology.
650	2	4	_aHealth Informatics.
650	2	4	_aComputer Vision.
653		0	_aDiagnostic Techniques, Neurological -- instrumentation
653		0	_aDiagnosis, Computer-Assisted -- instrumentation
653		0	_aSignal Processing, Computer-Assisted -- instrumentation
653		0	_aNervous System Diseases -- diagnostic imaging
653		0	_aMachine Learning
700	1		_aMurugappan, M. _eeditor. _0(orcid)0000-0002-5839-4589 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aRajamanickam, Yuvaraj. _eeditor. _0(orcid)0000-0003-4526-0749 _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-030-97845-7 _3Springer eBooks _zOnline access link to the resource
942			_2NLM _cEBK