Biomedical Sensors and Smart Sensing

Chapter 1: Introduction   1.1 Biomedical Systems  1.2 Biomedical Sensors  1.3 Physical Entity  1.3.1 Sensor noise   1.3.2 Sensor power  1.3.3 Signal to noise ratio  1.4 Sensor Property  1.4.1 Range of sensor  1.4.2 Sensitivity of the sensor  1.4.3 Linearity and non-linearity   1.5 Sensor calibration mechanisms  1.6 Sensing Errors  1.6.1 Quantization error  1.6.2 Dynamic Error  1.6.3 Systematic Error  1.6.4 Random Error  1.7 References  Chapter 2: Data Gathering & Sensing Methodology  2.1: Medical Data Classification  2.2 Sensing& Measurement  2.2.1 ECG Sensing  2.2.2 EEG Sensing  2.2.3 Heart beat monitoring system  2.2.4 Blood flow sensing  2.2.5 Respiratory Gas Flow Sensing  2.2.6 Motion Sensing and measurement  2.2.7 Force measurement  2.3    Data Collection & Networking  2.3.1 Data gathering classification  2.3.2 Converge casting  2.3.3 Sensor Network & Cloud  2.4 References    Chapter 3: Medical signal processing  3.1 Feature Extraction  3.1.1 Temporal data mining  3.1.2 Transforms  3.1.2 Overview of Filters  3.3 Multi-scale signal processing  3.4 Biomedical Imaging  3.5 Wearable and implantable technologies  3.6 Bio-robotics systems  3.7 Micro and Nano-technologies  3.8 References  Chapter 4: Sensor Data Analysis  4.1 Insight into Information learning  4.2 Predication Overview  4.2.1 Regression   4.2.2 Classification  4.2.3 Probability estimation  4.3 Perceptron Learning  4.4 Extreme Learning Machines  4.5 Deep Learning Insight  4.5 Choosing the right learning  4.6 References    Chapter 5: Medical Cyber Physical Systems and applications  5.1 IoT based ubiquitous sensing  5.1.1 MQTT based implementation  5.1.2 MQTT-SN approach  5.1.3 Python Paho application  5.1.4 Node-Js Platform  5.2 Architectural aspect of ubiquitous sensing methodology  5.3 Cloud computing approach  5.4 Security and privacy in Medical cyber physical systems  5.5 Experimental case study  5.6 Drone based Biomedical Service  5.7 References  Chapter 6: Conclusion