Biomedical signal analysis for connected healthcare:
Biomedical Signal Analysis for Connected Healthcare provides rigorous coverage on several generations of techniques, including time domain approaches for event detection, spectral analysis for interpretation of clinical events of interest, time-varying signal processing for understanding dynamical a...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
London, United Kingdom
Academic Press, Elsevier
[2021]
|
| Zusammenfassung: | Biomedical Signal Analysis for Connected Healthcare provides rigorous coverage on several generations of techniques, including time domain approaches for event detection, spectral analysis for interpretation of clinical events of interest, time-varying signal processing for understanding dynamical aspects of complex biomedical systems, the application of machine learning principles in enhanced clinical decision-making, the application of sparse techniques and compressive sensing in providing low-power applications that are essential for wearable designs, the emerging paradigms of the Internet of Things, and connected healthcare. - Provides comprehensive coverage of biomedical engineering, technologies, and healthcare applications of various physiological signals- Covers vital signals, including ECG, EEG, EMG and body sounds- Includes case studies and MATLAB code for selected applications |
| Beschreibung: | 1. Types and characteristics of biomedical signals 2. Time-domain processing of biomedical signals 3. Spectral-domain analysis of biomedical signals 4. Wavelet analysis of biomedical signals 5. Time-frequency analysis of biomedical signals 6. Sparse and compressive sensing techniques for biomedical signals 7. Machine learning for interpreting biomedical signals 8. Wearables and Internet of Things for connected healthcare |
| Umfang: | xiv, 325 Seiten Illustrationen, Diagramme 235 mm |
| ISBN: | 9780128130865 |
Internformat
MARC
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| 520 | |a Biomedical Signal Analysis for Connected Healthcare provides rigorous coverage on several generations of techniques, including time domain approaches for event detection, spectral analysis for interpretation of clinical events of interest, time-varying signal processing for understanding dynamical aspects of complex biomedical systems, the application of machine learning principles in enhanced clinical decision-making, the application of sparse techniques and compressive sensing in providing low-power applications that are essential for wearable designs, the emerging paradigms of the Internet of Things, and connected healthcare. - Provides comprehensive coverage of biomedical engineering, technologies, and healthcare applications of various physiological signals- Covers vital signals, including ECG, EEG, EMG and body sounds- Includes case studies and MATLAB code for selected applications | ||
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| id | DE-604.BV047360335 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T19:17:17Z |
| institution | BVB |
| isbn | 9780128130865 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032762341 |
| oclc_num | 1263279493 |
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| owner_facet | DE-29T |
| physical | xiv, 325 Seiten Illustrationen, Diagramme 235 mm |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Academic Press, Elsevier |
| record_format | marc |
| spelling | Krishnan, Sri Verfasser (DE-588)1213270170 aut Biomedical signal analysis for connected healthcare Sri Krishnan London, United Kingdom Academic Press, Elsevier [2021] xiv, 325 Seiten Illustrationen, Diagramme 235 mm txt rdacontent n rdamedia nc rdacarrier 1. Types and characteristics of biomedical signals 2. Time-domain processing of biomedical signals 3. Spectral-domain analysis of biomedical signals 4. Wavelet analysis of biomedical signals 5. Time-frequency analysis of biomedical signals 6. Sparse and compressive sensing techniques for biomedical signals 7. Machine learning for interpreting biomedical signals 8. Wearables and Internet of Things for connected healthcare Biomedical Signal Analysis for Connected Healthcare provides rigorous coverage on several generations of techniques, including time domain approaches for event detection, spectral analysis for interpretation of clinical events of interest, time-varying signal processing for understanding dynamical aspects of complex biomedical systems, the application of machine learning principles in enhanced clinical decision-making, the application of sparse techniques and compressive sensing in providing low-power applications that are essential for wearable designs, the emerging paradigms of the Internet of Things, and connected healthcare. - Provides comprehensive coverage of biomedical engineering, technologies, and healthcare applications of various physiological signals- Covers vital signals, including ECG, EEG, EMG and body sounds- Includes case studies and MATLAB code for selected applications |
| spellingShingle | Krishnan, Sri Biomedical signal analysis for connected healthcare |
| title | Biomedical signal analysis for connected healthcare |
| title_auth | Biomedical signal analysis for connected healthcare |
| title_exact_search | Biomedical signal analysis for connected healthcare |
| title_full | Biomedical signal analysis for connected healthcare Sri Krishnan |
| title_fullStr | Biomedical signal analysis for connected healthcare Sri Krishnan |
| title_full_unstemmed | Biomedical signal analysis for connected healthcare Sri Krishnan |
| title_short | Biomedical signal analysis for connected healthcare |
| title_sort | biomedical signal analysis for connected healthcare |
| work_keys_str_mv | AT krishnansri biomedicalsignalanalysisforconnectedhealthcare |