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SCIDIR_on1335398081 |
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OCoLC |
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20231120010654.0 |
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m o d |
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|a YDX
|b eng
|c YDX
|d OPELS
|d OCLCF
|d SFB
|d OCLCQ
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|a 9780128245002
|q (electronic bk.)
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|a 012824500X
|q (electronic bk.)
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|z 9780128244999
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|z 0128244992
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|a (OCoLC)1335398081
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|a RC670
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|a 616.10754
|2 23
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|a New frontiers of cardiovascular screening using unobtrusive sensors, AI, and IoT /
|c Anirban Dutta Choudhury, Rohan Banerjee, Sanjay Kimbahune, Arpan Pal.
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| 260 |
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|a London :
|b Academic Press,
|c 2022.
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| 300 |
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|a 1 online resource
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|a Intro -- New Frontiers of Cardiovascular Screening using Unobtrusive Sensors, AI, and IoT -- Copyright -- Dedication -- Contents -- Foreword -- Preface -- About the authors -- Section 1: Sensors, AI and IoT in cardiovascular diseases -- Chapter 1: Cardiovascular conditions: The silent killer -- 1. Introduction -- 2. Industrial revolution and Healthcare 4.0 -- 2.1. PhysioNet challenge -- 3. Chronic diseases and cardiovascular issues -- 4. Cardiovascular diseases: A silent killer -- 4.1. Factors enhancing cardiac risks -- 4.2. Heart rate, arrhythmia, and atrial fibrillation -- 4.3. Coronary artery disease and ischemic heart diseases -- 4.4. Cardiac fatigue and hypertension -- 4.4.1. Cardiac fatigue -- 4.4.2. Hypertension -- 4.4.3. Regulation of blood pressure -- 5. Sleep apnea and pulmonary conditions -- 6. Need for early screening and diagnosis -- 7. How sensing and AI can help -- 8. Devices: Surgical implants for cardiovascular monitoring and management -- 9. Analytics for screening and diagnosis -- 10. Putting it all together -- 11. Example of a real-world cardiovascular screening system -- References -- Further reading -- Chapter 2: Proliferation of a new generation of sensors: Smartphones and wearables -- 1. Introduction -- 2. Unobtrusive digital sensing -- 3. Electric or electromagnetic sensing -- 3.1. Acoustic and mechanical sensing -- 3.2. Biochemical sensing -- 4. Sensing the heart: An engineers perspective -- 4.1. Mechanical system -- 4.2. Circulatory system -- 4.3. Electrical system -- 4.4. Control system -- 4.5. Systems coming together -- 5. Photoplethysmogram: pulse oximetry -- 6. Phonocardiograms: digital stethoscope -- 6.1. History of heart sounds -- 6.2. Heart sounds -- 6.3. Conventional stethoscope -- 6.4. Digital stethoscope -- 6.5. Future of the stethoscope -- 7. ECG: Electrocardiograph.
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|a 7.1. Origination and pathways of electrical charges in the heart -- 7.2. 12 leads and Einthovens triangle -- 7.3. Basic building blocks of an ECG machine -- 8. Conclusion -- References -- Further reading -- Chapter 3: Sensor signal analytics -- 1. Introduction -- 2. Preprocessing -- 2.1. Signal conditioning -- 2.1.1. Hardware filtering -- 2.1.2. Amplification -- 2.1.3. Attenuation -- 2.2. Noise handling -- 2.2.1. Noise sources -- 2.2.2. Noise removal -- 2.2.3. Signal reconstruction -- 3. Decision making using AI -- 3.1. Supervised machine learning -- 3.2. Unsupervised machine learning -- 3.3. Splitting of training and test data -- 3.4. Feature engineering -- 3.4.1. Dimensionality reduction using principal component analysis -- 3.4.2. Independent component analysis -- 3.5. Popular machine learning algorithms -- 3.5.1. Logistic regression -- 3.5.2. Support vector machine -- 3.5.3. Decision tree -- 3.6. Deep learning in biomedical engineering -- 3.6.1. Neural network activation -- Binary step activation function -- Linear activation function -- Sigmoid activation function -- Hyperbolic tangent activation function (tanh) -- Rectified linear unit activation function -- Softmax function -- 3.6.2. Network hyperparameters -- 3.6.3. Recurrent neural networks -- 3.7. Semisupervised learning in biomedical signal processing -- 4. Further challenges -- 4.1. Handling unbalanced data -- 4.2. Clinical knowledge segmentation -- 5. Conclusion -- References -- Section 2: Disease screening -- Chapter 4: Abnormal heart rhythms -- 1. Introduction -- 2. Heart rate measurement using PPG and ECG -- 2.1. Preprocessing: Smartphone, wearable, and nearable -- 2.2. Frequency and time domain analysis -- 3. Arrhythmia detection using PPG and ECG -- 3.1. Signal conditioning -- 3.2. Feature engineering -- 4. Deep network for rhythm analysis -- 5. Conclusion -- References.
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|a Further reading -- Chapter 5: Heart blockage -- 1. Introduction -- 2. Correlation of heart blockage with ECG, PPG, and PCG -- 3. AI-based detection of chronic ischemic heart disease -- 3.1. Machine learning approaches -- 3.2. Deep learning approaches -- 4. Fusion of multiple sensors for classification -- 5. Patient metadata-based knowledge modeling -- 6. Conclusion -- References -- Further reading -- Chapter 6: Hypertension and cardiac fatigue -- 1. Introduction -- 2. Screening of hypertension from PPG and ECG -- 2.1. Electrical modeling of cardiovascular system -- 2.2. Other lumped models for simulation of arterial blood pressure -- 2.3. Regression modeling from PPG -- 3. Pulse transit time analysis from PPG and ECG -- 4. Cardiac fatigue from PPG and ECG -- 4.1. Why investigating cardiac fatigue is important -- 4.2. Sensing cardiac fatigue -- 4.3. Some interesting early results and path to the future -- 5. Conclusion -- References -- Chapter 7: Correlated diseases -- 1. Introduction -- 1.1. Sleep disorders -- 1.2. Chronic obstructive pulmonary disease -- 2. Sleep analysis -- 2.1. Sleep studies -- 2.1.1. Sleep stage classification -- Preprocessing -- Feature extraction -- Feature selection and classification -- 2.2. Sleep apnea and sleep arousal -- 2.2.1. Detection techniques -- 3. COPD -- 3.1. Conventional machine learning -- 3.2. Deep learning -- 4. Conclusion -- References -- Further reading -- Section 3: Future challenges -- Chapter 8: Looking at the future -- 1. Introduction -- 2. Trends for physiological sensing -- 2.1. Advances in noninvasive physiological sensing -- 2.1.1. Flexible electronics-based wearables -- 2.1.2. Implantables, ingestibles, and injectibles -- 2.1.3. Using the human body as a communication medium -- 2.1.4. Photoacoustic and hyperspectral sensing -- 2.1.5. Radar sensing and computational imaging -- 2.2. Nanobiosensing.
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|a 2.3. Genomic analytics -- 3. Trends for analytics and AI -- 3.1. Technology trends -- 3.1.1. AutoML -- 3.1.2. Edge AI -- 3.1.3. Neuromorphic computing -- 3.1.4. Explainable AI -- 3.2. Challenges -- 3.2.1. Privacy, transparency, and trust -- 3.2.2. Security -- 4. Future vision for cardiovascular health -- 4.1. A day in the life of a patient in 2030 -- 4.2. A day in the life of a cardiologist in 2030 -- References -- Index.
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| 650 |
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|a Cardiovascular system
|x Diseases
|x Diagnosis.
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| 650 |
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0 |
|a Medical screening.
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| 650 |
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0 |
|a Biomedical engineering.
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| 650 |
1 |
2 |
|a Cardiovascular System.
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| 650 |
2 |
2 |
|a Cardiovascular Diseases.
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| 650 |
|
7 |
|a Biomedical engineering.
|2 fast
|0 (OCoLC)fst00832568
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| 650 |
|
7 |
|a Cardiovascular system
|x Diseases
|x Diagnosis.
|2 fast
|0 (OCoLC)fst00847186
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| 650 |
|
7 |
|a Medical screening.
|2 fast
|0 (OCoLC)fst01014632
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| 776 |
0 |
8 |
|c Original
|z 0128244992
|z 9780128244999
|w (OCoLC)1268112195
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| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780128244999
|z Texto completo
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