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Advances in non-invasive biomedical signal sensing and processing with machine learning / / edited by Saeed Mian Qaisar, Humaira Nisar, and Abdulhamit Subasi
Advances in non-invasive biomedical signal sensing and processing with machine learning / / edited by Saeed Mian Qaisar, Humaira Nisar, and Abdulhamit Subasi
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (385 pages)
Disciplina 610.28
Soggetto topico Machine learning
Medical innovations
Biosensors
ISBN 3-031-23239-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. 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.
Record Nr. UNINA-9910678252803321
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advances in non-invasive biomedical signal sensing and processing with machine learning / / edited by Saeed Mian Qaisar, Humaira Nisar, and Abdulhamit Subasi
Advances in non-invasive biomedical signal sensing and processing with machine learning / / edited by Saeed Mian Qaisar, Humaira Nisar, and Abdulhamit Subasi
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (385 pages)
Disciplina 610.28
Soggetto topico Machine learning
Medical innovations
Biosensors
ISBN 3-031-23239-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. 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.
Record Nr. UNISA-996547951803316
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Complementary Metal Oxide Semiconductor / / edited by Kim Ho Yeap and Humaira Nisar
Complementary Metal Oxide Semiconductor / / edited by Kim Ho Yeap and Humaira Nisar
Pubbl/distr/stampa Croatia : , : IntechOpen, , 2018
Descrizione fisica 1 online resource (162 pages) : illustrations
Disciplina 621.3815284
Soggetto topico Metal oxide semiconductor field-effect transistors
ISBN 1-83881-512-0
1-78923-497-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910317801903321
Croatia : , : IntechOpen, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Very-large-scale integration / / Kim Ho Yeap, Humaira Nisar, editors
Very-large-scale integration / / Kim Ho Yeap, Humaira Nisar, editors
Pubbl/distr/stampa [Place of publication not identified] : , : IntechOpen, , [2018]
Descrizione fisica 1 online resource (160 pages)
Disciplina 621.395
Soggetto topico Integrated circuits - Very large scale integration
ISBN 953-51-3978-9
953-51-3864-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910265147703321
[Place of publication not identified] : , : IntechOpen, , [2018]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui