scholarly journals Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders

2019 ◽  
Vol 82 (1-3) ◽  
pp. 41-64 ◽  
Author(s):  
U. Raghavendra ◽  
U. Rajendra Acharya ◽  
Hojjat Adeli
Keyword(s):  
Artificial Intelligence ◽  
Signal Processing ◽  
Neurological Disorders ◽  
Extraction Methods ◽  
Physiological Signals ◽  
Machine Learning Techniques ◽  
Automated Diagnosis ◽  
Cad System ◽  
Reduction Techniques ◽  
Processing Techniques

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.

Prediction of Neurological Disorders Using Visual Saliency

2021 ◽  
pp. 1-11
Author(s):  
Sreelakshmi S. ◽  
Anoop V. S.
Keyword(s):  
Neurological Disorders ◽  
Early Stage ◽  
Brain Magnetic Resonance Imaging ◽  
Visual Saliency ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Magnetic Resonance Imaging Mri ◽  
Processing Techniques ◽  
The Brain ◽  
Prompt Diagnosis

Neurological disorders are diseases of the central and peripheral nervous system and most commonly affect middle- or old-age people. Accurate classification and early-stage prediction of such disorders are very crucial for prompt diagnosis and treatment. This chapter discusses a new framework that uses image processing techniques for detecting neurological disorders so that clinicians prevent irreversible changes that may occur in the brain. The newly proposed framework ensures reliable and accurate machine learning techniques using visual saliency algorithms to process brain magnetic resonance imaging (MRI). The authors also provide ample hints and dimensions for the researchers interested in using visual saliency features for disease prediction and detection.

Exploring Modern Digital Signal Processing Techniques on Physiological Signals in Day-to-Day Life Applications

2020 ◽  
Vol 10 (1) ◽  
pp. 93-98 ◽  
Author(s):  
M. Sundar Prakash Balaji ◽  
R. Jayabharathy ◽  
Betty Martin ◽  
A. Parvathy ◽  
R.K. Arvind Shriram ◽  
...  
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital Signal ◽  
Physiological Signals ◽  
Processing Techniques ◽  
Signal Processing Techniques

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

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