LEADER 02486nam 2200613Ia 450 001 9910456005303321 005 20200520144314.0 010 $a1-280-18451-5 010 $a9786610184514 010 $a0-309-50839-8 035 $a(CKB)111069351126442 035 $a(EBL)3375197 035 $a(SSID)ssj0000096445 035 $a(PQKBManifestationID)11108972 035 $a(PQKBTitleCode)TC0000096445 035 $a(PQKBWorkID)10082535 035 $a(PQKB)10904687 035 $a(MiAaPQ)EBC3375197 035 $a(Au-PeEL)EBL3375197 035 $a(CaPaEBR)ebr10032387 035 $a(OCoLC)923253388 035 $a(EXLCZ)99111069351126442 100 $a20020522d2002 ua 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAccess to research data in the 21st century$b[electronic resource] $ean on going dialogue among interested parties : report of a workshop /$fScience, Technology, and Law Panel, Policy and Global Affairs, National Research Council 210 $aWashington, D.C. $cNational Academy Press$dc2002 215 $a1 online resource (66 p.) 225 1 $aCompass series 300 $aDescription based upon print version of record. 311 $a0-309-08329-X 320 $aIncludes bibliographical references. 327 $a""Front Matter""; ""Preface""; ""Acknowledgments""; ""Contents""; ""1 Historical Perspective""; ""2 The Scientific Process and the Universe of Data""; ""3 Public Access to Research Data Used in Rule Making""; ""4 Privacy vs. Openness: A View from the Bench""; ""5 Alternative Approaches to Data Access""; ""6 Closing Remarks""; ""Appendix A Science, Technology, and Law Panel""; ""Appendix B Agenda""; ""Appendix C List of Registrants"" 410 0$aCompass series. 606 $aCommunication in science$vCongresses 606 $aResearch$xInformation services$vCongresses 606 $aInformation technology$zUnited States$vCongresses 606 $aPublic records$xAccess control$zUnited States$vCongresses 608 $aElectronic books. 615 0$aCommunication in science 615 0$aResearch$xInformation services 615 0$aInformation technology 615 0$aPublic records$xAccess control 676 $a501 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910456005303321 996 $aAccess to research data in the 21st century$92000357 997 $aUNINA LEADER 05762nam 2200529 450 001 996547951803316 005 20230526112746.0 010 $a3-031-23239-9 024 7 $a10.1007/978-3-031-23239-8 035 $a(MiAaPQ)EBC7208102 035 $a(Au-PeEL)EBL7208102 035 $a(CKB)26189281400041 035 $a(DE-He213)978-3-031-23239-8 035 $a(PPN)269099522 035 $a(EXLCZ)9926189281400041 100 $a20230526d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvances in non-invasive biomedical signal sensing and processing with machine learning /$fedited by Saeed Mian Qaisar, Humaira Nisar, and Abdulhamit Subasi 205 $a1st ed. 2023. 210 1$aCham, Switzerland :$cSpringer,$d[2023] 210 4$dİ2023 215 $a1 online resource (385 pages) 311 08$aPrint version: Qaisar, Saeed Mian Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning Cham : Springer International Publishing AG,c2023 9783031232381 320 $aIncludes bibliographical references and index. 327 $a1. Introduction to non-invasive biomedical signals for healthcare -- 2. Signal Acquisition Preprocessing and Feature Extraction Techniques for Biomedical Signals -- 3. The Role of EEG as Neuro-Markers for Patients with Depression: A systematic Review -- 4. Brain-Computer Interface (BCI) Based on the EEG Signal Decomposition Butterfly Optimization and Machine Learning -- 5. Advances in the analysis of electrocardiogram in context of mass screening: technological trends and application of artificial intelligence anomaly detection -- 6. Application of Wavelet Decomposition and Machine Learning for the sEMG Signal based Gesture Recognition -- 7. Review of EEG Signals Classification using Machine Learning and Deep-learning Techniques -- 8. "Biomedical signal processing and artificial intelligence in EOG signals" -- 9. Peak Spectrogram and Convolutional Neural Network-based Segmentation and Classification for Phonocardiogram Signals -- 10. Eczema skin lesions segmentation using deep neural network (U-net) -- 11. Biomedical signal processing for automated detection of sleep arousals Based on Multi-Physiological Signals with Ensemble learning methods -- 12. Deep Learning Assisted Biofeedback -- 13. Estimations of Emotional Synchronization Indices for Brain regions using Electroencephalogram Signal Analysis -- 14. Recognition Enhancement of Dementia Patients? Working Memory using Entropy-based Features and Local Tangent Space Alignment Algorithm. 330 $aThis book presents the modern technological advancements and revolutions in the biomedical sector. Progress in the contemporary sensing, Internet of Things (IoT) and machine learning algorithms and architectures have introduced new approaches in the mobile healthcare. A continuous observation of patients with critical health situation is required. It allows monitoring of their health status during daily life activities such as during sports, walking and sleeping. It is realizable by intelligently hybridizing the modern IoT framework, wireless biomedical implants and cloud computing. Such solutions are currently under development and in testing phases by healthcare and governmental institutions, research laboratories and biomedical companies. The biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG), Electromyography (EMG), phonocardiogram (PCG), Chronic Obstructive Pulmonary (COP), Electrooculography (EoG), photoplethysmography (PPG), and image modalities such as positron emission tomography (PET), magnetic resonance imaging (MRI) and computerized tomography (CT) are non-invasively acquired, measured, and processed via the biomedical sensors and gadgets. These signals and images represent the activities and conditions of human cardiovascular, neural, vision and cerebral systems. Multi-channel sensing of these signals and images with an appropriate granularity is required for an effective monitoring and diagnosis. It renders a big volume of data and its analysis is not feasible manually. Therefore, automated healthcare systems are in the process of evolution. These systems are mainly based on biomedical signal and image acquisition and sensing, preconditioning, features extraction and classification stages. The contemporary biomedical signal sensing, preconditioning, features extraction and intelligent machine and deep learning-based classification algorithms are described. Each chapter starts with the importance, problem statement and motivation. A self-sufficient description is provided. Therefore, each chapter can be read independently. To the best of the editors? knowledge, this book is a comprehensive compilation on advances in non-invasive biomedical signal sensing and processing with machine and deep learning. We believe that theories, algorithms, realizations, applications, approaches, and challenges, which are presented in this book will have their impact and contribution in the design and development of modern and effective healthcare systems. 606 $aMachine learning 606 $aMedical innovations 606 $aBiosensors 615 0$aMachine learning. 615 0$aMedical innovations. 615 0$aBiosensors. 676 $a610.28 702 $aNisar$b Humaira 702 $aSubasi$b Abdulhamit 702 $aQaisar$b Saeed Mian 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996547951803316 996 $aAdvances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning$93071688 997 $aUNISA