Real-Time Data Acquisition in Human Physiology: Real-Time Acquisition, Processing, and Interpretation—A MATLAB-Based Approach

Description

Real-Time Data Acquisition in Human Physiology: Real-Time Acquisition, Processing, and Interpretation—A MATLAB-Based Approach focuses on the design and development of a computer-based system to detect and digitally process human ECG, EMG, and carotid pulse waveforms in real time. The indigenous system developed and described in this book allows for an easy-to-interface, simple hardware arrangement for bio-signal detection. The computational functionality of MATLAB is verified for viewing, digital filtration, and feature extraction of acquired bio-signals. This book demonstrates a method of providing a relatively cost-effective solution to human physiology real-time monitoring, processing, and interpretation that is more realizable and would directly benefit a larger population of patients. [list] [*]Presents an application-driven, interdisciplinary, and experimental approach to bio-signal processing with a focus on acquiring, processing, and understanding human ECG, EMG, carotid pulse data and HRV. [*]Covers instrumentation and digital signal processing techniques useful for detecting and interpreting human physiology in real time, including experimental layout and methodology in an easy-to-understand manner. [*]Discusses development of a computer-based system that is capable of direct interface through the sound port of a PC and does not require proprietary DAQ units and ADC units. [*]Covers a MATLAB-based algorithm for online noise reduction, features extraction techniques, and infers diagnostic features in real time. [*]Provides proof of concept of a PC-based twin channel acquisition system for the recognition of multiple physiological parameters. [*]Establishes the use of Digital Signal Controller to enhance features of acquired human physiology. [*]Presents the use of carotid pulse waveforms for HRV analysis in critical situations using a very simple hardware/software arrangement. [/list] Front Cover Real-Time Data Acquisition in Human Physiology Copyright Page Contents Preface Acknowledgment 1 Introduction 1.1 Rationale 1.2 Technical overview 1.2.1 Signals 1.2.2 Bio-signals 1.2.3 Understanding biomedical data acquisition and processing system 1.2.3.1 Measurand (bio-signals) 1.2.3.2 Transducer 1.2.3.3 Signal conditioning 1.2.3.4 Analog-to-digital converters 1.2.3.5 Data acquisition software (signal processing and analysis) 1.2.3.6 Database 1.2.3.7 Simulation and modeling 1.2.3.8 Bio-signal data transmission 1.2.4 Recent developments 1.2.5 Challenges in bio-signal acquisition and processing 1.3 Objectives References 2 PC-based data acquisition 2.1 Introduction 2.2 Sensors and transducers 2.2.1 Physical sensors 2.2.1.1 Radiation sensors 2.2.1.2 Mechanical sensors 2.2.1.2.1 Doppler Sonography used measurement of blood flow 2.2.1.2.2 Hemodynamic invasive blood pressure sensors 2.2.1.3 Sensors in spirometry 2.2.1.4 Ultrasonic pressure transducer module 2.2.1.4.1 Piezo sensors for pressure pulses 2.2.1.4.2 Measurement of internal ocular pressure 2.2.1.4.3 Acoustic sensors-based hearing aids 2.2.1.5 Thermal sensors 2.2.1.6 Magnetic sensors 2.2.2 Chemical sensors 2.2.2.1 Sensors for pH and blood gases 2.2.3 Biosensors 2.2.3.1 Enzymatic biosensors 2.2.3.2 Aptasensors or nucleic acid bioreceptors 2.2.3.3 Living biosensors or microbial sensors 2.2.3.4 Optical transducers 2.2.3.4.1 Electrochemical 2.2.3.4.2 Optical transducers 2.2.3.4.3 Mass-based detection methods 2.2.4 Electropotential sensors 2.2.4.1 ECG electrodes 2.2.4.2 EMG electrodes 2.2.4.3 EEG electrodes 2.3 Market research and latest developments in sensor technologies 2.3.1 IoT sensors 2.3.2 RFID sensors 2.3.3 Wearable sensors 2.3.4 Pollution sensors 2.3.5 Optical image sensors 2.3.6 MEMS and NEMS sensors 2.3.6.1 MEMS 2.3.6.2 NEMS 2.3.7 Sensor materials 2.4 Data acquisition system hardware 2.4.1 Front end of data acquisition system hardware 2.4.1.1 Gain 2.4.1.2 Frequency response 2.4.1.3 Common-mode rejection ratio 2.4.1.4 Noise and drift 2.4.1.5 Recovery time 2.4.1.6 Input impedance 2.4.1.7 Electrode polarization 2.4.2 Filtering subsystem 2.4.3 Interface units 2.5 Data acquisition system software 2.6 Data acquisition systems market References 3 Detection and processing of real-time carotid pulse waves 3.1 Introduction 3.1.1 Clinical significance of carotid pulse waveform 3.1.2 Investigation of carotid pulse measurement 3.2 Experimental arrangement for detection of carotid pulse in real time 3.2.1 The piezoelectric sensor 3.2.2 The acquisition protocol and results 3.3 Digital filter designs 3.3.1 Finite impulse response filter design 3.3.2 Infinite impulse response filter design 3.4 Simulated model for real-time carotid pulse detection and processing 3.4.1 Filter design and analysis tool 3.4.2 Simulink model developed 3.5 Algorithm for real-time carotid pulse analysis 3.6 Conclusion References 4 Real-time detection and processing of electromyography signal 4.1 Introduction 4.1.1 Application areas of electromyography signal analysis 4.1.2 Investigation of electromyography signal measurement 4.2 Signal processing the electromyography data 4.3 Real-time detection and processing of electromyography signal in single channel mode 4.3.1 Silver–silver chloride surface electrodes 4.3.2 Front-end amplifier and interface unit 4.3.3 Digital filter for online processing 4.3.4 The acquisition protocol and results 4.4 Real-time detection and processing of electromyography signal in dual channel mode 4.5 Standalone MATLAB code for physiological data detection and processing 4.6 Conclusion References 5 Real-time detection and processing of electrocardiogram signal 5.1 Introduction 5.1.1 Interpretation of electrocardiogram waveform 5.1.2 Lead placement in an electrocardiogram system 5.1.3 Investigation of electrocardiogram signal measurement 5.2 Recent trends in cardiology 5.3 Notch filter designs for reducing power line interference in electrocardiogram signals 5.3.1 Computer simulation of various notch filter design topologies 5.4 Real-time detection and processing of electrocardiogram signal 5.4.1 Online algorithm for feature extraction 5.5 Digital signal controller-based electrocardiogram acquisition system 5.5.1 The acquisition protocol and results 5.6 Conclusion References 6 Measurement and analysis of heart rate variability 6.1 Introduction 6.1.1 Basic block diagram for heart rate variability evaluation process 6.2 Heart rate variability metrics and norms 6.2.1 Time-domain analysis 6.2.2 Frequency-domain analysis 6.2.2.1 Ultralow-frequency band 6.2.2.2 Very-low-frequency band 6.2.2.3 Low-frequency band 6.2.2.4 High-frequency band 6.2.3 Nonlinear measurement analysis 6.2.3.1 Heart rate variability measurement and analysis platforms 6.3 QRS detection methods 6.3.1 Differentiation-based methods for QRS peak detection 6.3.2 Template matching methods for QRS peak detection 6.3.3 Wavelets for QRS peak detection 6.4 Real-time detection and analysis of heart rate variability 6.4.1 Dual channel system for real-time, simultaneous acquisition of ECG, and carotid pulse wave 6.5 Conclusion References 7 Conclusion 7.1 Major contributions 7.1.1 Detection and analysis of carotid pulse wave 7.1.2 Detection and analysis of electromyogram signal 7.1.3 Detection and analysis of electrocardiogram signal and heart rate variability 7.2 Conclusion 7.3 Future directions References Index Back Cover