U-Healthcare monitoring systems. Volume 1, Design and applications.
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| Imprint: | London : Academic Press, [2019] |
|---|---|
| Description: | 1 online resource (430 pages). |
| Language: | English |
| Series: | Advances in Ubiquitous Sensing Applications for Healthcare Ser. Advances in Ubiquitous Sensing Applications for Healthcare Ser. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11706335 |
Table of Contents:
- Front Cover; U-Healthcare Monitoring Systems: Volume 1: Design and Applications; Copyright; Contents; Contributors; Preface; Chapter 1: Wearable U-HRM device for rural applications; 1. Introduction; 2. U-Healthcare System in India; 3. Application; 4. Open Issues and Problems; 5. Requirements of a Healthcare System; 6. Requirement of Wearable Devices; 7. Implementation; 8. Measurement of Heart Rate and Body Temperature; 9. Discussion; 10. Conclusion and Future Trends; Glossary; References; Chapter 2: A robust framework for optimum feature extraction and recognition of P300 from raw EEG
- 1. Introduction2. Literature Survey; 3. The Framework; 3.1. Initialization; 3.2. Model Setup; 3.2.1. Preprocessors; 3.2.2. Custom epoch extractor (Cepex); 3.3. Postprocessor; 3.4. Classification; 4. Results and Discussion; 4.1. The Dataset; 4.2. Framework Results; 4.2.1. Preprocessing; 4.2.2. Postprocessing; 4.2.3. Classification; 4.2.4. Performance comparison; 4.2.5. Open source implementation; 5. Conclusion and Future Work; References; Chapter 3: Medical image diagnosis for disease detection: A deep learning approach; 1. Introduction; 1.1. Related Work
- 2. Requirement of Deep Learning Over Machine Learning2.1. Fundamental Deep Learning Architectures; 2.1.1. Multilayer Perceptron; 2.1.2. Deep Belief Networks; 2.1.3. Stacked Auto-Encoder; 2.1.4. Convolution Neural Networks; Convolution architecture; Convolution layers; Stride and pooling layers; Fully connected; 2.1.5. Recurrent Neural Network; How does LSTM improve the RNN?; 3. Implementation Environment; 3.1. Toolkit Selection/Evaluation Criteria [13]; 3.2. Tools and Technology Available for Deep Learning [13]; 3.3. Deep Learning Framework Popularity Levels [14]
- 4. Applicability of Deep Learning in Field of Medical Image Processing [15]4.1. Current Research Applications in the Field of Medical Image Processing; 5. Hybrid Architectures of Deep Learning in the Field of Medical Image Processing [17]; 6. Challenges of Deep Learning in the Fields of Medical Imagining [17]; 7. Conclusion; References; Further Reading; Chapter 4: Reasoning methodologies in clinical decision support systems: A literature review; 1. Introduction; 2. Methods; 2.1. Research Questions; 2.2. Selection Criteria; 2.3. Search Strategy; 3. Literature Review and Results
- 3.1. Paper Screening3.2. Selecting the Most Relevant Papers; 3.3. Extracting and Analyzing Concepts; 3.3.1. Rule-based reasoning; 3.3.2. Ontology reasoning; 3.3.3. Ontology-based fuzzy decision support system; 3.3.4. Case-based reasoning; 3.4. Current Challenges and Future Trends; 4. Conclusion; References; Chapter 5: Embedded healthcare system for day-to-day fitness, chronic kidney disease, and congestive heart failure; 1. Ubiquitous Healthcare and Present Chapter; 2. Introduction; 3. Frequency-Dependent Behavior of Body Composition