LEADER 01193nam--2200397---450- 001 990003057810203316 005 20080129121011.0 010 $a3-518-22120-5 035 $a000305781 035 $aUSA01000305781 035 $a(ALEPH)000305781USA01 035 $a000305781 100 $a20080129h1985----km-y0itay50------ba 101 $ager 102 $aDE 105 $a||||||||001yy 200 1 $aAlte Meister$eKomödie$fThomas Bernhard 210 $aFrankfurt am Main$cSuhrkamp$dcopyr. 1985 215 $a310 p.$d18 cm 225 2 $a<> Bibliothek Suhrkamp$v1120 410 0$12001$a<> Bibliothek Suhrkamp 454 0$12001 676 $a833.914 700 1$aBERNHARD,$bThomas$0132758 801 0$aIT$bsalbc$gISBD 912 $a990003057810203316 951 $aVII.2.A. 1052$b204707 L.M.$cVII.2.$d00065064 951 $aVII.2.A. 1052a$b204709 L.M.$cVII.2. 1052a$d00065058 951 $aVII.2.A. 1052b$b204708 L.M.$cVII.2.$d00065063 959 $aBK 969 $aUMA 979 $aPAOLA$b90$c20080129$lUSA01$h1148 979 $aPAOLA$b90$c20080129$lUSA01$h1157 979 $aPAOLA$b90$c20080129$lUSA01$h1210 996 $aAlte Meister$91019102 997 $aUNISA LEADER 04907nam 22007695 450 001 9910578697903321 005 20250210003722.0 010 $a9783030978457 010 $a3030978451 024 7 $a10.1007/978-3-030-97845-7 035 $a(MiAaPQ)EBC7019577 035 $a(Au-PeEL)EBL7019577 035 $a(CKB)23931927100041 035 $aEBL7019577 035 $a(AU-PeEL)EBL7019577 035 $a(DE-He213)978-3-030-97845-7 035 $a(EXLCZ)9923931927100041 100 $a20220617d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBiomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders /$fedited by M. Murugappan, Yuvaraj Rajamanickam 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (295 pages) 300 $aDescription based upon print version of record. 311 08$aPrint version: Murugappan, M. Biomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders Cham : Springer International Publishing AG,c2022 9783030978440 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aBiomedical engineering 606 $aSignal processing 606 $aNervous system$xDiseases 606 $aRadiology 606 $aMedical informatics 606 $aComputer vision 606 $aBiomedical Engineering and Bioengineering 606 $aSignal, Speech and Image Processing 606 $aNeurological Disorders 606 $aRadiology 606 $aHealth Informatics 606 $aComputer Vision 615 0$aBiomedical engineering. 615 0$aSignal processing. 615 0$aNervous system$xDiseases. 615 0$aRadiology. 615 0$aMedical informatics. 615 0$aComputer vision. 615 14$aBiomedical Engineering and Bioengineering. 615 24$aSignal, Speech and Image Processing. 615 24$aNeurological Disorders. 615 24$aRadiology. 615 24$aHealth Informatics. 615 24$aComputer Vision. 676 $a610.285 676 $a616.80475 702 $aMurugappan$b M. 702 $aRajamanickam$b Yuvaraj 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910578697903321 996 $aBiomedical signals based computer-aided diagnosis for neurological disorders$92997190 997 $aUNINA