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008			200624s2020 si a o |||| 0|eng d
020			_a9789811538247
024	7		_a10.1007/978-981-15-3824-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aR856.15 _b2020 EB
245	0	0	_aFeature Engineering and Computational Intelligence in ECG Monitoring _cedited by Chengyu Liu, Jianqing Li
250			_aFirst edition
264		1	_aSingapore _bSpringer _c2020.
300			_a1 recurso en línea (X, 268 páginas) _b101 ilustraciones, 77 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aBiomedical and Life Sciences (SpringerNature-11642)
490	0		_aBiomedical and Life Sciences (R0) (SpringerNature-43708)
505	0		_aChapter 1. Feature engineering and computational intelligence in ECG monitoring - an introduction -- Chapter 2. Representative Databases for Feature Engineering and Computational Intelligence in ECG Processing -- Chapter 3. An Overview of signal quality indices on dynamic ECG signal quality assessment -- Chapter 4. Signal quality features in dynamic ECGs -- Chapter 5. Motion Artifact Suppression Method in Wearable ECG -- Chapter 6. Data Augmentation for Deep Learning based ECG analysis -- Chapter 7. Study on Automatic Classification of Arrhythmias -- Chapter 8. ECG Interpretation with deep learning -- Chapter 9. Visualizing ECG contribution into Convolutional Neural Network classification -- Chapter 10. Atrial fibrillation detection in dynamic signals -- Chapter 11. Applications of Heart rate variability in Sleep Apnea -- Chapter 12. False Alarm Rejection for ICU ECG Monitoring -- Chapter 13. Respiratory Signal Extraction from ECG Signal -- Chapter 14. Noninvasive Recording of Cardiac Autonomic Nervous Activity--What's behind ECG? -- Chapter 15. A questionnaire study on artificial intelligence and its effects on individual health and wearable device.
520			_aThis book discusses feature engineering and computational intelligence solutions for ECG monitoring, with a particular focus on how these methods can be efficiently used to address the emerging challenges of dynamic, continuous & long-term individual ECG monitoring and real-time feedback. By doing so, it provides a "snapshot" of the current research at the interface between physiological signal analysis and machine learning. It also helps clarify a number of dilemmas and encourages further investigations in this field, to explore rational applications of feature engineering and computational intelligence in ECG monitoring. The book is intended for researchers and graduate students in the field of biomedical engineering, ECG signal processing, and intelligent healthcare.
988			_aSpringer_Biomedlife_03082020
650		7	_2embne _9143820 _aIngeniería biomédica
700	1		_aLiu, Chengyu _eeditor _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/nr2003002592 _1http://viaf.org/viaf/77708390 _9675318
700	1		_aLi, Jianqing _eeditor _4http://id.loc.gov/vocabulary/relators/edt _1http://viaf.org/viaf/200243251 _9675319
776	0	8	_iPrinted edition: _z9789811538230
776	0	8	_iPrinted edition: _z9789811538254
776	0	8	_iPrinted edition: _z9789811538261
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-15-3824-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b08/2020 _dz _ek _zSI