Electronic Devices, Circuits, and Systems for Biomedical Applications: Challenges and Intelligent Approac
Copyright
Contributors
Preface
Chapter organization
1 . Carbon-based electrodes as a scaffold for the electrochemical sensing of pharmaceuticals: a special case of immunosuppressa ...
1. Introduction
2. Carbon materials used for electrode modifications
3. The electroactive immunosuppressant drugs
4. Bare carbon electrodes as a platform for the electroanalysis of immunosuppressant drugs
5. Electroanalysis of immunosuppressants on modified carbon electrodes
6. Electrochemical biosensors for immunosuppressants using carbon electrodes
7. Conclusion and outlook
Acknowledgments
References
2 . Selenium-based amorphous semiconductors and their application in biomedicine
1. Introduction
1.1 Crystalline and noncrystalline semiconductors
1.2 Band models in amorphous semiconductors
1.3 CFO model
1.4 Davis and Mott model
1.5 MDS model
2. Defects in selenium
3. Optical analysis
4. Electrical analysis
5. Synthesis of selenium nanoparticles (SeNPs)
5.1 Chemical techniques
5.2 Physical techniques
5.3 Green techniques
5.3.1 Synthesis of SeNPs using bacteria
5.3.2 Synthesis of SeNPs using other microbes
5.3.3 Synthesis of SeNPs using plants
5.3.4 Synthesis of SeNPs using green chemical agents
6. Applications of SeNPs for biomedical purposes
7. Conclusion
References
3 . Nanodevices for biomedical applications
1. Introduction
2. Nanodevices in implantable devices
2.1 Cardiac pacemaker
2.2 Implantable cardioverter defibrillators
2.3 Brain stimulator
2.4 Neuromuscular stimulation
2.5 Retinal prosthesis
2.6 Cochlear implant
3. Nanodevices for IMD memories
4. Nanodevices for wireless power systems
5. Nanodevices for temperature sensors
6. Nanodevices for image sensors
6.1 Image sensors
6.2 Charge-coupled device image sensors
6.2.1 CCD peripheral circuitry
6.3 CMOS image sensors (CIS)
6.3.1 CIS peripheral circuitry
6.3.2 Microlens and illumination
6.3.2.1 Photodiode material
6.4 Scientific CMOS
6.5 Nanophotonic image sensors
7. Applications of image sensors in biomedicine
7.1 CCD for spectrometry
7.2 sCMOS for optical microscopy in biomedicine
7.3 Pill camera for ingestible wireless capsule endoscopy
7.4 Implantable image sensor: retinal prosthetic
8. Conclusion
References
Websites
4 . Analytical model and sensitivity analysis of a gate-engineered dielectric modulated junctionless nanowire transistor-based ...
1. Introduction
2. Device structure
3. Development of analytical model
3.1 Boundary condition 1
3.2 Boundary condition 2
3.3 Boundary condition 3
3.4 Region 1
3.5 Region 2
3.6 Region 3
3.7 To determine V2 and V3
3.8 Analytical modeling of threshold voltage
3.9 Modeling of subthreshold drain current in the biosensor
3.10 Modeling of drain current in the linear and saturation region
4. Simulation setup
5. Results and discussion
6. Conclusion
7. Appendix A
8. Appendix B
9. Appendix C
References
5 . Design and development of AlGaN/GaN HEMT for biosensing applications for detection of cancers, tumors, and kidney malfuncti ...
1. Introduction
1.1 Sensor functionalization
1.2 GaN HEMT history and operation
1.3 Lattice mismatch and strain in III-N semiconductors
1.4 High electron mobility transistors (HEMTs)
1.5 Two-dimensional electron gas (2DEG)
1.6 AlGaN/GaN heterostructure HEMT
1.7 AlGaN/GaN HEMT device fabrication flow
1.7.1 MOCVD growth of epi-stack on Si substrate
1.7.2 Ohmic source (S)/drain (D) deposition
1.7.2.1 # Mask 1: S/D contacts
1.7.3 Device passivation
1.7.3.1 #Mask 3: for etching of the Si3N4 passivation layer
1.7.3.2 #Mask 4: gate oxide deposition, metallization, and padding
1.8 GaN HEMT sensor motivation
1.9 GaN HEMTs for biosensing application for detection
1.9.1 Existing models and their limitations
1.9.2 Analytical model: HEMT breast cancer sensor
1.9.3 C-erbB-2 numeric model
1.9.4 Sensor model
1.9.5 PH detection
1.10 Challenges faced in HEMT sensor commercialization
1.11 Conclusion
References
6 . Preprocessing of the electrocardiogram signal for a patient parameter monitoring system
1. Introduction
2. Biomedical signals
3. Artifacts associated with electrocardiogram signals
3.1 Baseline wander
3.2 Power line interference
3.3 High-frequency noise
3.4 Random artifacts
4. Adaptive filters for noise cancellation
4.1 LMS algorithm
4.2 SDLMS algorithm
4.3 SELMS algorithm
4.4 SSLMS algorithm
4.5 RLS algorithm
4.6 Weiner filter
4.7 Kalman filter
4.8 Tapped delay line adaptive linear network
4.9 ANFIS adaptive filter
4.10 Wavelet transform for denoising
5. Patient parameter monitoring system
5.1 Preprocessing of ECG signal for a portable bedside cardiac monitor
5.2 Current trends in design of patient monitoring systems
6. Summary
References
7 . A study on sleep stage classification based on a single-channel EEG signal
1. Introduction
2. Methodology
2.1 PSG data and qualitative evaluation
2.2 Quantitative evaluation
2.2.1 Preprocessing
2.2.2 Feature extraction
2.2.2.1 Empirical mode decomposition
2.2.2.2 Ensemble empirical mode decomposition
2.2.2.3 Variational mode decomposition
2.2.3 Classification
3. State-of-the-art analysis based on automated sleep scoring
3.1 PSG dataset
3.1.1 Sleep EDF database expanded
3.1.2 Cyclic alternating pattern database
3.2 Comparative studies
4. Results and discussion
5. Conclusion
Acknowledgment
References
8 . Implementation of ultra-low-power electronics for biomedical applications
1. Introduction
2. Related work
3. Sensors
3.1 Components of biomedical system
3.2 Pacemaker
3.2.1 Types of pacemaker
3.2.2 Risks involved in pacemakers
3.2.3 New pacemaker functionality
3.2.4 Battery life of a pacemaker
4. Sensing techniques
4.1 Wearable sensing technology
4.1.1 Illustration of wearable sensing technology
4.1.2 Micromachine system motion sensor
4.1.3 Flexible sensor
4.1.4 Wearable biosensors
4.2 Biochip technology
4.2.1 Genetic factor chips
4.2.2 Protein microarray chips
4.2.3 Cell chips
4.2.4 Tissue chips
4.2.5 Organoid chips
4.3 Biosensors
4.3.1 Biologic molecular sensor
4.3.2 Cell-based sensors
4.4 Implantable sensors technology
4.4.1 Biocompatibility
4.4.2 Biofunctionality: sensitivity and specificity
4.4.3 Miniaturizing nanomaterials
4.4.4 Lifetime
4.5 Neural sensing and interfacing
5. Wireless remote sensing technology
6. Method initiation with improved techniques
7. Conclusion
References
9 . Sensors and their application
1. Introduction
1.1 Active and passive sensors
1.2 Analog and digital sensors
1.3 Inverse sensors
1.4 Based on means of detection
1.5 Based on conversion phenomenon
2. Types of sensors
2.1 Temperature sensor
2.2 Proximity sensor
2.3 FET biosensor
2.4 Accelerometer
2.5 IR sensor
2.6 Pressure sensor
2.7 Light sensor
2.8 Ultrasonic sensor
2.9 Touch sensors
2.10 Color sensors
2.11 MOS sensors
2.12 Temperature and humidity sensors
2.13 Torque sensor
2.14 Magnetic sensor
2.15 Hall effect sensor
2.16 Magneto diode
3. Application of the sensors
3.1 Healthcare
3.2 Wearable body sensors
3.3 Some of the wearable body sensors
3.3.1 Accelerometer
3.3.2 Gyroscope
3.4 Cardiopulmonary and vascular monitoring (CVD)
3.5 Neurologic function monitoring
3.6 Physical therapy and rehabilitation
3.7 Biosensors
3.8 Early detection of COVID-19 with the help of FET and MOSFET biosensor
3.9 Agriculture
3.10 Soil water measurement sensor
3.10.1 Mathematic calculation
3.11 Water content sensor
3.12 Sensor for soil moisture content
3.13 Sensor for soil electrical conductivity measurement
3.14 pH sensor
3.15 Deployment of pH sensor
3.16 Weed seeker sensor
3.17 Temperature sensor
3.18 Sensors application in daily life
3.19 Industry
3.20 Pressure sensor
3.21 Air Pollution
3.22 PM-2.5 particle concentration sensor
3.23 Carbon monoxide sensor
3.24 Hazard gas sensor
3.25 Safety and security
3.26 Education
4. Enabling sensors with IoT and machine learning
Abbreviations
References
10 . ADC and DAC for biomedical application
1. Introduction [1,2]
1.1 Transducers (sensors)
1.2 ADC
1.3 Processing unit
1.4 DAC
2. Analog-to-digital conversion [1โ4]
2.1 Sampling
2.2 Filters
2.3 Quantizer and quantization error
2.4 Encoder
2.4.1 Unipolar code
2.4.2 Bipolar codes
3. Data converter parameters
3.1 Signal-to-noise ratio
3.2 Harmonic distortion
3.3 Signal-to-noise and distortion ratio
3.4 Analog bandwidth
3.5 Noise factor
3.6 Aperture time, aperture delay time, and aperture jitter
3.7 DAC settling time
3.8 Glitch impulse area
4. Data converter architectures [1,5โ7]
4.1 DAC architecture
4.1.1 The thermometer DAC (voltage mode)
4.1.2 Thermometer (fully decoded) DACs
4.1.3 R-2R DACs
4.1.4 Oversampling interpolating DACs
4.2 ADC architecture
4.2.1 The comparator (1-bit ADC)
4.2.2 Successive approximation ADCs
4.2.3 Dual-slope ADCs
4.2.4 Pipelined ADCs
4.2.5 Sigma-delta (ฮฃโฮ) ADCs
5. ADC application in biomedical electronics
6. Conclusion
References
11 . A low-power reconfigurable ADC for bioimpedance monitoring system
Pipe line analog to digital converter
1 Introduction
1.1 Static performance
1.1.1 Least significant bit
1.1.2 Static error
1.1.3 Offset error
1.1.4 Gain error
1.1.5 Integral nonlinearity
1.1.6 Differential nonlinearity
1.2 Dynamic performance
1.2.1 S/N ratio
1.2.2 SNDR
1.2.3 Effective number of bits
1.2.4 Spurious-free dynamic range
1.2.5 Dynamic range
2. Pipelined ADC architecture
2.1 S/H circuit
2.1.1 Sampling time
2.1.2 Holding time
2.2 Comparator
2.3 Transmission gate
2.3.1 Structure of transmission gate
2.3.2 Working
2.4 Subtractor
2.4.1 Working
2.5 Residue amplifier
3. Automatic adaptation unit
3.1 Sampling speed configuration
3.2 Reconfigurable amplifiers
3.3 Resolution configuration
3.4 Power in pipelined stages
3.5 Automatic adaptation
3.5.1 Preamplifier
3.5.2 Schmitt trigger
3.5.3 Upper threshold voltage
3.5.4 Lower threshold voltage
3.5.5 Hysteresis voltage
3.5.6 Transfer characteristics
3.6 Frequency-to-voltage converter (FVC)
3.6.1 Logic block
4. DTMOS logic
5. Simulation results of the designed circuitry
6. Performance
7. Conclusion
References
Further reading
12 . Design of a 16-bit 500-MS/s SAR-ADC for low-power application
1. Introduction
1.1 Paper organization
2. Overview of analog-to-digital converters
2.1 Analog-to-digital converter
2.2 Working principle of ADC
2.3 Performance factors of an ADC
2.4 Applications of ADCs
2.5 Classification of ADCs
2.6 Comparison of different ADCs
3. Successive approximation register
3.1 An N-Bit SAR-ADC architecture
3.2 SAR algorithm
4. Proposed SAR-ADC design
4.1 Digital-to-analog converter
4.2 Comparator
5. Conclusion
References
13 . Design and applications of rail-to-rail FC-OTA and second-generation CCII+ cell
1. Introduction
2. Circuit schematic and description of low-voltage, low-power FC-OTA
3. Simulation results of OTA
3.1 AC analysis of OTA
3.2 DC sweep analysis of OTA
3.3 Transient analysis of OTA in unity gain configuration
4. Second-generation current conveyor (CCII)
4.1 AC analysis of CCII+ cell
4.2 DC sweep analysis of CCII+cell
4.3 Transient analysis of CCII+ cell
5. Applications of operational transconductance amplifiers
5.1 Five-OTA-based MISO biquadratic filters
5.2 Two-OTA-based Gm-C MISO type biquadratic filter
5.3 SIMO voltage mode biquadratic filter
5.4 Three-phase oscillators using FC-OTAs
5.5 Full-wave rectifiers using MO-CM-OTA
5.6 FC-OTA-based signal adder
6. Applications of second-generation positive CCII cell
6.1 MISO type biquadratic filter
6.2 Quadrature oscillator
6.3 Current mode instrumentation amplifier
6.4 Voltage and current adders
7. Conclusions
References
14 . The role of electronic filters in biomedical applications: a brief survey
1. Introduction
2. Literature review
2.1 ECG/EMG
2.2 MRI
2.3 Mammography
2.4 Electronic prosthetics
3. Conclusions and future scope of work
References
15 . Fingerprint-based smart medical emergency first aid kit using IoT
1. Introduction
2. IoT in healthcare
2.1 Redefining healthcare
3. Literature review
4. Proposed methodology
5. Hardware description
5.1 Arduino UNO
5.1.1 Specifications
5.2 NodeMCU
5.2.1 Specification
5.3 Heartbeat sensor
5.3.1 Working principle of heartbeat sensor
5.3.2 Circuit diagram of heartbeat sensor
5.3.3 Heartbeat sensor board
5.3.4 Specifications
5.4 Fingerprint sensor
5.4.1 Types of fingerprint sensors
5.4.2 Optical fingerprint sensor
5.4.3 Working principle of optical fingerprint sensor
5.4.4 Specifications
5.5 GSM module
5.6 GPS unit
5.7 LCD unit
6. Results and discussion
7. Conclusion
8. Future enhancement
References
16 . An overview of the dynamics of telemedicine and robotics for the benefit of mankind
1. Introduction
2. Telemedicine and robotics as the key for a smarter future
2.1 Telemedicine technology
2.2 Robotics in healthcare
2.2.1 Robots in surgery
2.2.2 Robots for intervention
2.2.3 Robots for rehabilitation or other assistance
3. Global scenario of demand and cost
4. Future aspects
5. Summary
References
17 . A guidance system to read and analyze the traffic rules for the visually impaired human
1. Introduction
2. The magnitude of the problem
3. Literature survey
4. Materials and methods
5. Implementation
6. Results and discussion
7. Conclusion and future work
References
18 . An overview of the various medical devices for diagnosis, monitoring, and treatment of diseases
1. Introduction
2. Medical devices for diagnosis
2.1 Diagnosis of blood disorders
2.2 General-purpose improvement of medical care
2.3 Detection of brain pressure
2.4 Diagnosis of arterial diseases
2.5 Diagnosis of cancer
2.6 Diagnosis of infectious diseases
2.7 Diagnosis of liver fibrosis
2.8 Diagnosis in psychiatry
2.9 Diagnosis using artificial intelligence-based techniques
2.10 Smartphone-based diagnosis
2.11 Diagnosis of inflammatory diseases
2.12 Applications of internet of things for diagnosis
2.13 Application of smart polymers in medical diagnosis
3. Monitoring of different diseases with medical devices
3.1 Tele-home healthcare
3.2 Interface devices
3.3 Monitoring the physical activity of patients
3.4 Glucose monitoring
3.5 Detection of diseases and measurement of breath flow
3.6 Diabetic foot disorder
3.7 Implantable devices for monitoring
3.8 Health monitoring projects
3.9 Smartphone Android applications for health monitoring
3.10 Monitoring of Parkinson disease
3.11 Other chronic disease monitoring
3.12 Application of IoT in heart diseases
3.13 Blood pressure and heart disease monitoring
4. Treatment of different diseases with medical devices
4.1 Multiple signal modes for medical services
4.2 Treatment of eye diseases
4.3 Treatment of liver diseases
4.4 Treatment of neurologic disorders
4.5 Treatment with magnetic medical devices
4.6 Treatment for obesity
4.7 Treatment with mobile devices and their sensors
4.8 Treatment of cardiovascular diseases
4.9 Some other issues with the medical devices
5. Conclusions
References
19 . Efficient wireless power transfer system for biomedical applications
1. Introduction
1.1 Implantable devices and their power management
1.2 Classification of wireless power technology for implantable system
2. Design of WPT system
2.1 Common coil structures
2.1.1 Circular coil
2.1.2 Rectangular coils
2.1.3 Double coils
3. Power conditioning units
3.1 Transmitter side power conversion and management
3.1.1 SiT8008B oscillator
3.1.2 The operating frequency
3.1.3 Class-E power amplifier
3.1.4 Class-E power amplifier transistor DMG230UK-7
3.1.5 Driver LM5134
3.2 Receiver side power conversion and management
3.2.1 Backward data communication unit
4. Challenges and solutions
4.1 Variation of output load
4.2 Misalignment
4.3 Resonance detuning
4.4 Electrical safety
4.5 Biocompatibility
5. Conclusion
References
20 . Impact of IoT in biomedical applications: Part I
1. Introduction
2. Architectural levels of IoT
2.1 Layer 1: sensors and physical devices
2.2 Layer 2: data acquisition system for sensor
2.3 Layer 3: edge information technology systems
2.4 Layer 4: analysis and data storage
3. IoT sensors used in healthcare and biomedical sciences
3.1 Glucometer
3.2 Temperature sensor
3.3 Blood pressure sensor
3.4 Airflow sensor
3.5 Electrocardiogram sensor
3.6 Electromyography sensor
4. IoT-based medical devices
5. Impact of IoT in healthcare
5.1 Real-time remote monitoring
5.2 Smart pills
5.3 Diabetes management
5.4 Blood pressure monitoring
5.5 Connected contact lenses
6. Security and privacy concerns in IoT-based medical devices for biomedical applications
6.1 IoT security issues
6.2 Threats at perception layer
6.3 Threats at network layer
6.4 Threats at middleware layer
6.5 Threats at gateway
6.6 Threats at application layer
6.7 Privacy solutions of IoT for biomedical applications
7. Conclusion and future scope
References
21 . Impact of IoT in biomedical applications: Part II
1. Introduction to IoT in biomedical applications
1.1 History of IoT in biomedical applications
1.2 Architecture and working
1.2.1 Perception layer
1.2.2 Network layer
1.2.3 Storage and Processing layer
1.2.4 Application layer
1.3 Design considerations of IoT
2. Hospital management system and mobile applications using IoT
2.1 Medical applications using IoT
3. Integrated devices for a single parameter
4. Integrated devices for multiple parameters
5. Challenges of IoT
5.1 Mechanisms to prevent IoT threats
5.2 IoT laws and policies in various countries
6. Conclusion and future work
Further reading
22 . Health monitoring system
1. Introduction
2. Nanotechnology for disease diagnosis
2.1 Biomedical applications
2.2 Introduction to nanosensors
2.3 Nanotechnology for Alzheimer disease
2.4 Nanotechnology for Parkinson disease
2.5 Nanotechnology for cancer diagnostics
3. Analysis of exhaled breath
4. Types of sensors
4.1 MOS sensor
4.2 Carbon polymer array
4.3 Carbon nanotube
4.4 Optical sensors
4.4.1 Colorimetric sensor array
4.4.2 Optical fiber
4.5 Gas chromatography
4.6 Calorimetric methods
4.7 Gold nanoparticle-based sensor
4.7.1 Monolayer-capped gold nanoparticle
4.7.2 Chronic disease detection through gold nanoparticle sensors
5. Smart health monitoring systems
5.1 Sensor systems
5.2 Communication systems
5.2.1 Edge-based architecture for the healthcare industry 4.0
5.2.2 Some case studies
5.3 Sensor data analysis or processing system
6. Conclusion
Abbreviations
References
23 . Real-time remote health monitoring using IoT sensors
1. Introduction
1.1 Overview of the internet of things
1.2 Barriers and challenges of IoT
1.2.1 Data security and privacy
1.2.2 Tremendous data handling and analytics
1.2.3 Interoperability
1.2.4 Network connectivity
1.2.5 Excessive cost
1.3 Benefits of IoT in health monitoring
1.3.1 Simultaneous reporting and monitoring
1.3.2 Cross-functional connectivity and accessibility
1.3.3 Data categorization and analysis
1.3.4 Surveillance and notifications
1.3.5 Remote medical assistance
2. IoT medical devices for health monitoring
2.1 Overview of wireless body area network
2.1.1 Remote activation of medical devices
2.1.2 Wearable IoT-enabled sensing devices
2.1.3 Implantable IoT-enabled sensing devices
2.2 Architecture of wireless body area network
3. Technologies integrated with IoT and blockchain for healthcare
3.1 Role of artificial intelligence in healthcare
3.2 Significance of machine learning for healthcare informatics
3.3 Deep learning for healthcare data analysis
4. Applications of IoT sensors in health monitoring
4.1 Remote temperature monitoring for vaccines
4.2 Drug effectiveness tracking
4.3 Medication refill reminder technology
5. IoT in biomedical applications
6. Open research challenges of IoT in health monitoring
7. Conclusion
References
24 . E-health monitoring system
1. Introduction
2. Technology used
3. Proposed model
3.1 Objective of the work
3.2 System approach
3.2.1 Data acquirement
3.2.2 Data processing
3.2.3 Data storage
3.2.4 Data transmission
3.3 Medicine monitoring
3.4 Emergency button
3.5 MyMonitor
3.6 Smartwatch
4. Results and discussion
4.1 Pulse rate
4.2 Body temperature and activity
4.3 Blood pressure
4.4 Blood oxygen
5. Conclusion
References
25 . Comparative analysis of various supervised machine learning techniques for diagnosis of COVID-19
1. Introduction
2. Problem formulation
2.1 Data sets description
2.2 Data analysis
2.3 Data preprocessing
2.4 Evaluation metrics
3. ML
3.1 kNN (k-nearest neighbor)
3.2 Random forest (RF)
3.3 Bagging algorithm
4. Result analysis
4.1 kNN model
4.2 Random forest
4.3 Bagging algorithm
5. Conclusion and future work
References
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
Z