Early Detection of Neurological Disorders Using Machine Learning Systems

Early Detection of Neurological Disorders Using Machine Learning Systems

Sudip Paul, Pallab Bhattacharya, Arindam Bit

Copyright: © 2019 |Pages: 376

ISBN13: 9781522585671|ISBN10: 1522585672|EISBN13: 9781522585688

DOI: 10.4018/978-1-5225-8567-1

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MLA

Paul, Sudip, et al., editors. Early Detection of Neurological Disorders Using Machine Learning Systems. IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8567-1

APA

Paul, S., Bhattacharya, P., & Bit, A. (Eds.). (2019). Early Detection of Neurological Disorders Using Machine Learning Systems. IGI Global. https://doi.org/10.4018/978-1-5225-8567-1

Chicago

Paul, Sudip, Pallab Bhattacharya, and Arindam Bit, eds. Early Detection of Neurological Disorders Using Machine Learning Systems. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8567-1

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While doctors and physicians are more than capable of detecting diseases of the brain, the most agile human mind cannot compete with the processing power of modern technology. Utilizing algorithmic systems in healthcare in this way may provide a way to treat neurological diseases before they happen.

Early Detection of Neurological Disorders Using Machine Learning Systems provides innovative insights into implementing smart systems to detect neurological diseases at a faster rate than by normal means. The topics included in this book are artificial intelligence, data analysis, and biomedical informatics. It is designed for clinicians, doctors, neurologists, physiotherapists, neurorehabilitation specialists, scholars, academics, and students interested in topics centered on biomedical engineering, bio-electronics, medical electronics, physiology, neurosciences, life sciences, and physics.

Table of Contents

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Front Materials

Title Page

Copyright Page

Advances in Medical Technologies and Clinical Practice (AMTCP) Book Series

Preface

Chapters

Chapter 1

Mapping the Intellectual Structure of the Field Neurological Disorders: A Bibliometric Analysis (pages 1-12)

S. Ravikumar

The chapter utilizes bibliometric tools to explore papers in the field of neurological disorders and to examine the scientific development in the above subject domain. The research data were retrieved from the WOS database, which...

Chapter 2

Neurofeedback: Retrain the Brain (pages 13-25)

Meena Gupta, Dinesh Bhatia

Neurofeedback (NF) is a type of brain wave training based on operant learning. NF has been employed in research and clinical settings for the investigation and treatment of a growing number of psychological illnesses. This technique...

Chapter 3

Neurological Disorders, Rehabilitation, and Associated Technologies: An Overview (pages 26-38)

Sateesh Reddy Avutu, Dinesh Bhatia

Patients with neurological disorders are increasing globally due to various factors such as change in lifestyle patterns, professional and personal stress, small nuclear families, etc. Neurological rehabilitation is an area focused...

Chapter 4

Brain Tumor and Its Segmentation From Brain MRI Sequences (pages 39-60)

Sanjay Saxena, Puspanjali Mohapatra, Swati Pattnaik

Automated segmentation of tumorous region from the brain magnetic resonance image (MRI) is the procedure of extrication anomalous tissues from regular tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid...

Chapter 5

Early Detection of Parkinson's Disease: An Intelligent Diagnostic Approach (pages 61-95)

Debashree Devi, Saroj K. Biswas, Biswajit Purkayastha

Parkinson's disease (PD) is a neurodegenerative disorder that occurs due to corrosion of the substantia nigra, located in the thalamic region of the human brain, and is responsible for transmission of neural signals throughout...

Chapter 6

Soft Computing-Based Early Detection of Parkinson's Disease Using Non-Invasive Method Based on Speech Analysis (pages 96-107)

Chandrasekar Ravi

This chapter aims to use the speech signals that are a behavioral bio-marker for Parkinson's disease. The victim's vocabulary is mostly lost, or big gaps are observed when they are talking or the conversation is abruptly...

Chapter 7

Assessment of Gait Disorder in Parkinson's Disease (pages 108-127)

Divya Govindaraju, Gururaj Nagarajan, Paramasivam Alagumariappan

Neurological disorders are some of the leading chronic disorders that impose a massive burden on low-income and developing countries. The disability resulting from the neurological disorder increases the severity and costs during the...

Chapter 8

Tremor Identification Using Machine Learning in Parkinson's Disease (pages 128-151)

Angana Saikia, Vinayak Majhi, Masaraf Hussain, Sudip Paul, Amitava Datta

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both...

Chapter 9

Epileptic Seizure Detection and Classification Using Machine Learning (pages 152-164)

Rekh Ram Janghel, Yogesh Kumar Rathore, Gautam Tatiparti

Epilepsy is a brain ailment identified by unpredictable interruptions of normal brain activity. Around 1% of mankind experience epileptic seizures. Around 10% of the United States population experiences at least a single seizure in...

Chapter 10

Neurocognitive Mechanisms for Detecting Early Phase of Depressive Disorder: Analysis of Event-Related Potentials in Human Brain (pages 165-198)

Shashikanta Tarai

This chapter discusses neurocognitive mechanisms in terms of latency and amplitudes of EEG signals in depression that are presented in the form of event-related potentials (ERPs). Reviewing the available literature on depression...

Chapter 11

Social Media Analytics to Predict Depression Level in the Users (pages 199-215)

Mohammad Shahid Husain

As people around the world are spending increasing amounts of time online, the question of how online experiences are linked to health and wellbeing is essential. Depression has become a public health concern around the world....

Chapter 12

Linguistic Markers in Individuals With Symptoms of Depression in Bi-Multilingual Context (pages 216-240)

Anbu Savekar, Shashikanta Tarai, Moksha Singh

Depression has been identified as the most prevalent mental disorder worldwide. Due to the stigma of mental illness, the population remains unidentified, undiagnosed, and untreated. Various studies have been carried out to detect and...

Chapter 13

Motor Imagery Classification Using EEG Signals for Brain-Computer Interface Applications (pages 241-251)

Subrota Mazumdar, Rohit Chaudhary, Suruchi Suruchi, Suman Mohanty, Divya Kumari, Aleena Swetapadma

In this chapter, a nearest neighbor (k-NN)-based method for efficient classification of motor imagery using EEG for brain-computer interfacing (BCI) applications has been proposed. Electroencephalogram (EEG) signals are obtained from...

Chapter 14

Intelligent Big Data Analytics in Health (pages 252-291)

Ebru Aydindag Bayrak, Pinar Kirci

Intelligent big data analytics and machine learning systems have been introduced to explain for the early diagnosis of neurological disorders. A number of scholarly researches about intelligent big data analytics in healthcare and...

Chapter 15

Medical Image Segmentation: An Advanced Approach (pages 292-321)

Ramgopal Kashyap, Surendra Rahamatkar

Today, IoT in therapeutic administrations has ended up being more productive in light of the fact that the correspondence among authorities and patients has been improved with versatile applications. These applications are made by...

Back Materials

Compilation of References

About the Contributors

Index

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