Early Detection of Neurological Disorders Using Machine Learning Systems, 1st Edition

  • Sudip Paul
  • Pallab Bhattacharya
  • Arindam Bit
  • Published By:
  • ISBN-10: 1522585680
  • ISBN-13: 9781522585688
  • DDC: 616.8
  • 376 Pages | eBook
  • Original Copyright 2019 | Published/Released December 2019
  • This publication's content originally published in print form: 2019

  • Price:  Sign in for price



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. This title 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

Front Cover.
Title Page.
Copyright Page.
Advances in Medical Technologies and Clinical Practice (AMTCP) Book Series.
Table of Contents.
Detailed Table of Contents.
1: Mapping the Intellectual Structure of the Field Neurological Disorders: A Bibliometric Analysis.
2: Neurofeedback: Retrain the Brain.
3: Neurological Disorders, Rehabilitation, and Associated Technologies: An Overview.
4: Brain Tumor and Its Segmentation From Brain MRI Sequences.
5: Early Detection of Parkinson’s Disease: An Intelligent Diagnostic Approach.
6: Soft Computing-Based Early Detection of Parkinson’s Disease Using Non-Invasive Method Based on Speech Analysis.
7: Assessment of Gait Disorder in Parkinson's Disease.
8: Tremor Identification Using Machine Learning in Parkinson's Disease.
9: Epileptic Seizure Detection and Classification Using Machine Learning.
10: Neurocognitive Mechanisms for Detecting Early Phase of Depressive Disorder: Analysis of Event-Related Potentials in Human Brain.
11: Social Media Analytics to Predict Depression Level in the Users.
12: Linguistic Markers in Individuals With Symptoms of Depression in Bi-Multilingual Context.
13: Motor Imagery Classification Using EEG Signals for Brain-Computer Interface Applications.
14: Intelligent Big Data Analytics in Health.
15: Medical Image Segmentation: An Advanced Approach.
Compilation of References.
About the Contributors.