000			04231nam a22004335i 4500
999			_c383940 _d383940
001			383940
003			ES-MaUEC
005			20230113000152.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			230113s2022 sz | s |||| 0|eng d
020			_a9783030978457
024	7		_a10.1007/978-3-030-97845-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRC348 _b2022 EB
245	0	0	_aBiomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders _cedited by M. Murugappan, Yuvaraj Rajamanickam
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (X, 289 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9169088 _aSistema nervioso _xEnfermedades
650		7	_2embne _9421154 _aInformática médica
700	1		_aMurugappan, M. _eeditor literario _0(orcid)0000-0002-5839-4589 _1https://orcid.org/0000-0002-5839-4589 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aRajamanickam, Yuvaraj _eeditor literario _0(orcid)0000-0003-4526-0749 _1https://orcid.org/0000-0003-4526-0749 _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030978440
776	0	8	_iPrinted edition: _z9783030978464
776	0	8	_iPrinted edition: _z9783030978471
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-97845-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eIG _zSI