Cognitive Predictive Maintenance Tools for Brain Diseases-Design and Analysis

✍ Scribed by Shweta Gupta

• Publisher: CRC Press
• Year: 2024
• Tongue: English
• Leaves: 197
• Series: Chapman & Hall/CRC Internet of Things
• Edition: 1
• Category: Library

✦ Synopsis

This book involves the design, analysis, and application of various cognitive predictive maintenance
tests with the help of tools like vibration analysis, ultrasonic analysis, infrared analysis, oil analysis,
laser-shaft alignment, and motor circuit analysis in the prediction of various cognitive diseases such
as epilepsy, Parkinson’s disease, Alzheimer’s disease, and depression. These are needed since there
are no proper medical tests available to predict these diseases in remote areas at an early stage.
Various emerging technologies are analyzed for the design of tests.
Key features:
• Incorporates innovative processes for treating cognitive diseases.
• Early and exact identification and treatment strategies are incorporated.
• Future technologies like artificial intelligence, machine learning, the IoT, and data science
are used to find solutions.
• Analysis with existing cognitive disease solutions is incorporated and simulations provided.
• The novelty of the book lies in the accurate prediction of cognitive diseases.
Encompassing future technologies and various communication protocols or devices available for
cognitive diseases for the design of new equipment are an outcome of the book. Various parameters
like power consumption, productivity, and safety should be taken into account during the analysis,
design, and application of a product. The book could well be added to the curriculum of medical
colleges and biomedical engineering students. Possible vendors include biomedical research centers
like Biotechnika and the Indian Council of Medical Research (ICMR). It would be a breakthrough
for biomedical companies to launch their new products.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Note on the Editor
List of Contributors
Chapter 1: The Brain: Its Structure and Functions
1.1 Introduction
1.2 Structure of the Brain
1.2.1 Forebrain
1.2.1.1 The Cerebrum
1.2.1.2 The Cortex
1.2.2 The Midbrain
1.2.2.1 The Pons
1.2.2.2 The Medulla
1.2.3 The Hindbrain
1.3 Brain Diseases
1.3.1 Types of Brain Diseases
1.4 Measures to Maintain Healthy Brain Functions
1.4.1 Diagnosis of Disease
References
Chapter 2: The Current Status of Cognitive Disorders and Their Diagnosis
2.1 Introduction
2.2 Lewy Body Dementia
2.2.1 Epidemiology
2.2.2 Pathogenesis
2.2.3 Clinical Symptoms
2.2.4 Diagnostic Standards
2.2.5 Challenges
2.3 Acute Disseminated Encephalomyelitis (ADEM)
2.3.1 Epidemiology
2.3.2 Aetiology of the Disease
2.3.3 Pathogenesis
2.3.4 Clinical Manifestation
2.3.5 Diagnostic Evaluation
2.4 Attention Deficit Hyperactivity Disorder (ADHD)
2.4.1 Clinical Variants of ADHD
2.4.2 Symptoms
2.4.3 Diagnosis
2.5 Alzheimer’s Disease (AD)
2.5.1 Prevalence
2.5.2 Pathogenesis
2.5.2.1 The Metabolic Pathway of Nerve Growth Factor (NGF)
2.5.2.2 The Signal Transducer and Activator of the Transcription Pathway: Janus Kinase
2.5.2.3 The FGF7/FRFR2/PI3K/AKT Pathway
2.5.3 Clinical Symptoms
2.5.3.1 Mild Alzheimer’s Disease Symptoms
2.5.3.2 Moderate Alzheimer’s Disease Symptoms
2.5.3.3 Warning Signs of Advanced Alzheimer’s
2.6 Frontotemporal Dementia (FTD)
2.6.1 Epidemiology
2.6.2 Symptoms
2.6.2.1 FTD with Behavioural Variants
2.6.2.2 Primarily Progressive Non-fluent Aphasia
2.6.2.3 Primary Progressive Aphasia with Semantic Variation
2.6.3 Diagnosis
2.7 Epilepsy
2.7.1 Epidemiology
2.7.2 Pathogenesis
2.7.3 Epilepsy’s Cognitive Impairment Mechanisms
2.7.4 Cognitive Impairment and Epileptiform Activity
2.7.5 Symptoms
2.7.6 Diagnosis
2.8 Posterior Cortical Atrophy (PCA)
2.8.1 Clinical Features
2.8.2 Diagnosis
2.9 Parkinson’s Disease (PD)
2.9.1 Epidemiology
2.9.2 Pathophysiology
2.9.2.1 Systemic Neurotransmitter Decline and Larger Dopaminergic Deficiencies throughout the Brain
2.9.2.2 Sympathetic and Noradrenergic Nervous Systems
2.9.2.3 Basic Cholinergic Systems in the Forebrain
2.9.3 Genetic Influences
2.9.4 Clinical Features
2.9.5 Diagnosis
2.10 Acute Disseminated Encephalomyelitis (ADEM)
2.10.1 Epidemiology
2.10.2 Etiopathogenesis
2.10.3 Clinical Signs and Symptoms
2.10.4 Diagnostic Criteria
References
Chapter 3: Current Cognitive Medical Tests and Available Therapies
3.1 Introduction
3.2 Comparable Cognitive Evaluations
3.2.1 Mini-Mental State Examination
3.2.1.1 Advantages
3.2.1.2 Disadvantages
3.2.2 Clock-drawing Test
3.2.2.1 Advantages
3.2.2.2 Disadvantages
3.2.3 Addenbrooke’s Cognitive Examinations
3.2.3.1 Advantages
3.2.3.2 Disadvantages
3.2.4 General Practitioner Cognition Assessment
3.2.4.1 Advantages
3.2.4.2 Disadvantages
3.2.5 Montreal Cognitive Assessment
3.2.5.1 Advantages
3.2.5.2 Disadvantages
3.2.6 A Tool for Dementia Screening in the Community
3.2.6.1 Cognitive Testing in Kolkata
3.2.6.2 The Adult Health Survey Conducted by the World Health Organization’s Study on AGEing
3.2.6.3 Indian Variant of Cognistat
3.2.6.4 Multi-domain Cognitive Screening Test (MDCST)
3.2.6.5 Rapid Test for Dementia Assessment
3.2.6.6 Mattis Dementia Rating Scale Translation into Hindi
3.2.6.7 Universal Dementia Assessment Scale (Rowland)
3.2.6.8 A Screen for Impaired Picture Memory
3.2.6.9 Available Domain-Wise Tests with Indian Benchmarks
3.2.6.10 Designed and Standardised Batteries for the Indian Population
3.2.6.10.1 The Postgraduate Institute of Medical Education and Research (PGI) Battery of Brain Malfunction
3.2.6.10.2 A Memory Scale for PGI
3.2.6.10.3 The Neuropsychological Test Battery of the National Institute of Mental Health and Neurosciences (NIMHANS)
3.2.6.10.4 Adult NIMHANS Cognitive Test
3.2.6.10.5 Children’s NIMHANS Neuropsychological Test
3.2.6.10.6 The Senior NIMHANS Neuropsychological Battery
3.2.6.10.7 The Comprehensive Neuropsychological Battery in Hindi (Adult Form) from the All India Institute of Medical Sciences
3.2.6.10.8 10/66 Battery of Cognitive Tests from the Dementia Research Group
3.2.6.10.9 HIV Battery of Cognitive Tests
3.2.6.11 Commercially Available Test Batteries
3.3 Virtual Reality Applications for Diagnosing and Treating Cognitive Disorders
3.3.1 Diagnostics in Virtual Reality
3.3.1.1 Context
3.3.1.2 Egocentric and Allocentric Navigation
3.3.1.3 Navigational Memory
3.3.1.4 Activities of Daily Life (ADL)
3.3.1.5 Advantages
3.3.1.6 Disadvantages
3.3.2 Virtual Reality-based Therapy
3.3.2.1 Context
3.3.2.2 Exercises in Virtual Reality
3.3.2.3 Online Cognitive Training Exercises
3.3.2.4 Dual-task Training
3.3.2.5 Benefits
3.3.2.6 Negative Aspects
3.3.3 Mixed and Augmented Reality
3.4 Recommendations
References
Chapter 4: Characterization of Biomedical Signals in Neurological Disorders
4.1 Introduction
4.2 Different Types of Biomedical Signals Used in the Diagnosis of Neurological Disorders
4.2.1 Electrocardiogram (ECG)
4.2.1.1 Parkinson’s Disease (PD)
4.2.1.2 Amylotrophic Lateral Sclerosis (ALS)
4.2.2 Electroencephalogram (EEG)
4.2.2.1 EEG Signal Analysis and Its Phases
4.2.2.2 Signals and Their Characterizations
4.2.2.3 Role of EEG in Diagnosis of Neurological Diseases
4.2.2.3.1 Parkinson’s Disease
4.2.2.3.2 Alzheimer’s Disease (AD)
4.2.2.3.3 Epilepsy
4.2.2.3.4 Autism Spectrum Disorder (ASD)
4.2.3 Electromyography (EMG)
4.2.3.1 PD
4.2.3.2 Amylotrophic Lateral Sclerosis (ALS)
4.2.3.3 Spinal Cord Injury (SCI)
4.2.4 Heart Rate Variability (HRV)
4.2.4.1 Muscular Dystrophies
4.2.4.2 PD
4.2.4.3 Epilepsy
4.2.5 Magnetoencephalography (MEG)
4.2.5.1 The Applications and Potential of MEG
4.2.5.2 Role of EEG in the Diagnosis of Neurological Diseases
4.2.5.2.1 AD
4.2.5.2.2 Traumatic Brain Injury (TBI)
4.2.5.2.3 Epilepsy
4.3 Limitations of Biomedical Signals
4.3.1 Artefacts of ECG
4.3.2 Artefacts of EEG
4.3.3 Artefacts of EMG
4.3.4 Artefacts of MEG
4.4 Future Approaches
4.5 Summary
References
Chapter 5: An Overview of Distinct Electronic Devices and Circuits
5.1 Introduction
5.2 Electrodes in Deep Brain Stimulators
5.3 An Implantable Pulse Generator (IPG)
5.4 Various Electronic Devices
5.4.1 PN Junction Diode
5.4.2 Bipolar Junction Transistors (BJTs)
5.5 Standard Base Configuration
5.6 The Typical Emitter Design: The Common Emitter (CE)
5.7 A Configuration of Typical Collectors: The Common Collector (CC)
5.8 The Transistor as an Amplifier [11, 15]
5.9 Classes of Amplifiers
5.10 Field Effect Transistor
5.10.1 Junction Field Effect Transistor (JFET) [21, 22]
5.10.2 The Metal Oxide Semiconductor Field Effect Transistor (MOSFET)
5.10.3 N-channel Depletion Type MOSFET
5.11 Conclusion
5.11.1 Fin Field Effect Transistor (FinFET)
References
Chapter 6: Exploration and Application of Cognitive Illness Predictors, such as Parkinson’s and Epilepsy
6.1 Cognitive Disorders
6.2 Epilepsy
6.3 Focal Seizures
6.4 Generalized Seizures
6.5 An Internet of Things Infrastructure for Screening and Managing Epilepsy
6.5.1 Electroencephalograms (EEGs)
6.5.2 Electromyography (EMG)
6.5.3 Electrocardiograms (ECGs)
6.5.4 Triaxial Accelerometer (ACM)
6.5.5 Body Temperature Sensor
6.6 Epilepsy Detection and Prediction Using EEG
6.6.1 Filter
6.6.2 Transformation
6.6.3 Feature Extraction
6.6.4 Prediction
6.7 Seizure Detection and Prediction using Heart Rate Sensors and Temperature Sensors
6.8 Automated Detection of Seizures Using an Electromyography (EMG) Device
6.9 Parkinson’s Disease
6.10 The Internet of Things Infrastructure for Parkinson’s Disease Detection
6.10.1 Bradykinesia
6.10.2 Tremors
6.11 Machine Learning Algorithms for Predicting Parkinson’s Disease
6.12 Conclusion
References
Chapter 7: Artificial Intelligence-based Biosensors
7.1 Introduction
7.2 Characteristics of the Ideal Smart Biosensor
7.3 ML-enabled Biosensors of Various Kinds
7.3.1 Electrochemical (EC) Biosensors
7.3.2 Non-invasive Biosensors
7.3.3 Wearable Biosensors
7.3.4 AI-assisted Wearable Biosensors
7.3.5 Surface Enhanced Raman Spectroscopy (SERS) and Spectra-based Biosensors
7.3.6 Biosensors for Cardiac Health Care
7.4 ML Algorithms for Biosensing Data Analysis
7.5 Point of Care Diagnosis Using Biosensors
7.6 Future of AI-based Biosensors
7.7 Conclusion
References
Chapter 8: Design of Circuits for Various Cognitive Diseases Using Various Cognitive Predictive Maintenance Tools
8.1 Introduction
8.2 ML Approaches for Cognitive Impairment and Disorders
8.3 Classification and Prediction of Brain Disorders
8.4 Intelligent Predictive Maintenance and Remote Monitoring
8.5 Behaviors in Children with Autism and Cognitive Control
8.6 Cognitive Predictive Maintenance Tools
8.7 Conclusion and Future Direction
References
Chapter 9: Advances in the Treatment of Cognitive Diseases Using IOT-based Wearable Devices
9.1 Introduction
9.2 Driving Force Behind the Work
9.3 Classification of Healthcare Wearable Devices (HWDs)
9.4 Categories of Various HWDs
9.5 Cognitive Disease Treatment with the Advancement of the IOT
9.6 Portable/Wearable Technologies Specifically Used by AD Patients
9.7 Dilemmas and Possibilities in the Practical Design of Wearable Microwave Sensors/Antenna for Various Biomedical Applications
9.8 Conclusion
References
Chapter 10: Prediction and Maintenance of Alzheimer’s Disease using Ultrasound and Infrared Spectroscopy Sensors/Augmented Reality Techniques
10.1 Introduction
10.2 Detection of AD using Infrared Analysis Sensors
10.2.1 Infrared Spectrography Sensors
10.2.2 Digit Verbal Span Task
10.2.3 fNIRS Configuration
10.2.4 Data Pre-processing
10.2.5 Data Analysis
10.2.6 Statistical Analysis
10.3 AD Early Diagnosis Methods Using AR/Virtual Reality for Cognitive Assessment
10.3.1 Neuroimaging Techniques
10.3.2 Behaviour Analysis
10.3.3 Emotion Analysis
10.3.4 Evaluation Techniques and Metrics for AD Diagnosis
10.3.5 Machine Learning Techniques for AD Diagnosis
10.3.5.1 Binary/Multi-class Classification
10.3.5.2 One-class Classification
10.4 Alzheimer’s Treatment by Applying Ultrasound Waves
10.4.1 Dependence of Alzheimer’s Physical Symptoms on Brain Shrinking
10.4.2 Influence of Ultrasound Waves on Brain Holes
10.5 Conclusion
References
Chapter 11: Deep Learning in Mental Illnesses: Understanding Networks
11.1 Introduction
11.2 Overview of the Different Categories of Mental Illnesses
11.2.1 Neurocognitive Disorders
11.2.2 Substance-abuse-related Disorders
11.2.3 Psychosis
11.2.4 Mood Disorders
11.2.5 Anxiety Disorders
11.2.6 Eating Disorders
11.3 Prevalence of Mental Health Problems during the COVID-19 Pandemic
11.4 Post-COVID-19 Neuropsychiatric Abnormalities and Their Clinical Manifestations
11.5 Deep Learning
11.6 Applications of Deep Learning in the Classification of Psychiatric Disorders
11.6.1 Schizophrenia
11.6.2 ADHD
11.6.3 Autism Spectrum Disorder (ASD)
11.7 Recent Advances of Deep Learning in Psychiatric Disorders
11.8 Precision Therapeutics by Machine Learning
11.9 Deep Learning in Mental Health Outcome Research
11.10 Limitations and Challenges of Deep Learning
11.11 Conclusion
11.12 Summary
Acknowledgement
References
Index

✦ Subjects

Brain functions; Cognitive diseases; Predictive tests