Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders

U. Raghavendra, U. Rajendra Acharya, Hojjat Adeli

Research output: Contribution to journalReview article

Abstract

Background: Authors have been advocating the research ideology that a computer-aided diagnosis (CAD) system trained using lots of patient data and physiological signals and images based on adroit integration of advanced signal processing and artificial intelligence (AI)/machine learning techniques in an automated fashion can assist neurologists, neurosurgeons, radiologists, and other medical providers to make better clinical decisions. Summary: This paper presents a state-of-the-art review of research on automated diagnosis of 5 neurological disorders in the past 2 decades using AI techniques: Epilepsy, Parkinson's disease, Alzheimer's disease, multiple sclerosis, and ischemic brain stroke using physiological signals and images. Recent research articles on different feature extraction methods, dimensionality reduction techniques, feature selection, and classification techniques are reviewed. Key Message: CAD systems using AI and advanced signal processing techniques can assist clinicians in analyzing and interpreting physiological signals and images more effectively.

Original languageEnglish
JournalEuropean Neurology
DOIs
Publication statusAccepted/In press - 01-01-2019

Fingerprint

Artificial Intelligence
Nervous System Diseases
Research
Multiple Sclerosis
Parkinson Disease
Epilepsy
Alzheimer Disease
Stroke
Brain

All Science Journal Classification (ASJC) codes

  • Neurology
  • Clinical Neurology

Cite this

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Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders. / Raghavendra, U.; Acharya, U. Rajendra; Adeli, Hojjat.

In: European Neurology, 01.01.2019.

Research output: Contribution to journalReview article

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