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Biomedical Signal Analysis for Connected Healthcare

Langue : Anglais

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Couverture de l’ouvrage 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 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.

1. Types and characteristics of biomedical signals2. Time-domain processing of biomedical signals3. Spectral-domain analysis of biomedical signals4. Wavelet analysis of biomedical signals5. Time-frequency analysis of biomedical signals6. Sparse and compressive sensing techniques for biomedical signals7. Machine learning for interpreting biomedical signals8. Wearables and Internet of Things for connected healthcare

Biomedical, electrical, and computer engineers, researchers in connected healthcare and in biomedical signal analysis.

Dr. Sridhar (Sri) Krishnan took degrees in electrical and computer engineering at the University of Calgary before embarking on a long and distinguished career at various research institutions, including Clinical Research Institute of Montreal, University of Western Ontario, University of Toronto, and Ryerson University. Dr. Krishnan, has been the Department Chair of the Department of Electrical and Computer Engineering at Ryerson University, and was the Founding Program Director for Biomedical Engineering at Ryerson University. Dr. Krishnan is currently Affiliate Scientist at the Keenan Research Centre for Biomedical Science at St. Michael’s Hospital and Associate Dean of Research, Development and Graduate Programs in Engineering and Architectural Science at Ryerson University. Dr. Krishnan has developed numerous patents and technical inventions through the course of his career, and has coordinated the establishment of 18 research laboratories in a variety of research areas. In addition, Dr. Krishnan has also played an anchor role in establishing a large research institute (iBEST) to support biomedical sciences and engineering research at Ryerson University. He has been involved in developing institutional partnerships with St. Michael’s Hospital and University Health Network. These partnerships provide strategic access to clinical expertise, health information and experimental test facilities for the biomedical engineering and sciences students and researchers at Ryerson University.
  • 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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Ouvrage de 334 p.

19x23.3 cm

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Thèmes de Biomedical Signal Analysis for Connected Healthcare :

Mots-clés :

Accelerometer sensors; Adaptive segmentation; Affordable health technology; Auscultation; Autoregressive modeling; Bioacoustics; Bluetooth; Causality; Clustering; Compressive sensing; Connected healthcare; COVID-19 pandemic tracking; Cross validation; Data compression; Decision support systems; Decision trees; Deep learning; Delta modulation; Digital health; Digital modulation; ECG wearables; ECG; Edge ML; EEG wearables; EEG; EHR; Electronic circuits; EMG wearables; EMG; Empirical mode decomposition; EMR; Ensemble learning; Entropy; Ethics and fairness in AI; F1score; Filter realization; FIR filters; Gait; Healthcare; IoT; and AI; Heart rate; HIPAA; HL7Low-cost digital technology; Human factors; IIR filters; Internet of things (IoT)Internet of medical things (IoMT)PPG; K-means; k-NN; Least mean squares algorithm; Linear discriminant analysis; Linear systems; Logistic regression; Mathematical modeling; Moving average; Naïve Bayes; Neural networks; Nonstationarity; Normal equation; Overfitting; Pan-Tompkins; Parametric representation; Pattern recognition; Physiological signals; PPG sensor; Pulse code modulation; Quantization; Receiver operating characteristics; Region of Convergence; Reinforcement learning; Remote monitoring; Sampling; Savitzky-Golay filters; Semisupervised learning; Sensitivity; Sleep and activity monitoring; Sparsity; Specificity; Spectral estimation; Spectral features; Spectrogram; Stability; Supervised learning; Support vector machines; Telemedicine; Time-domain features; Tiny ML; Unsupervised learning; User experience; Vital signs monitoring; Wavelet transform; Wearables; WiFi; Wigner-Ville distribution; Zigbee