Study of drowsiness from simple physiological signals testing: A signal processing perspective

2020 ◽  
Author(s):  
Noppawit Aeimpreeda ◽  
Pornkanok Sukaimod ◽  
Pinkaew Khongsabai ◽  
Charat Thothong ◽  
Direk Sueaseenak
Keyword(s):  
Signal Processing ◽  
Physiological Signals

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.

The Realization of CWT for Physiological Signals

2014 ◽  
Vol 556-562 ◽  
pp. 5028-5030
Author(s):  
Guang Jian Ye ◽  
Mai Xin ◽  
Wei Feng Wang
Keyword(s):  
Signal Processing ◽  
Heart Sound ◽  
Sound Signal ◽  
Physiological Signals ◽  
Sound Analysis ◽  
Design Algorithm ◽  
Filtering Theory ◽  
Filtering Method ◽  
The Way ◽  
Heart Sound Signal

Purpose: We do it to remove the clutters and overcome the limitations on resolution of STFT method, head to improve the accuracy and timeliness on heart sound analysis. Method: We recommend CWT filtering theory, then design algorithm based on the theory and use the way of LabVIEW2011 to program for achieving in the application. Result: We have successfully used the CWT filtering method to carry out clutters. Conclusion: Using the method described above can achieve the goal for the optimization of the heart sound signal processing.

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

The Realization of Time-Varying Filtering for Physiological Signals

2014 ◽  
Vol 556-562 ◽  
pp. 5031-5033
Author(s):  
Guang Jian Ye ◽  
Mai Xin ◽  
Tao Long
Keyword(s):  
Signal Processing ◽  
Heart Sound ◽  
Sound Signal ◽  
Physiological Signals ◽  
Time Varying ◽  
Filtering Theory ◽  
Filtering Method ◽  
Heart Sound Signal

Purpose: We do it to eliminate the influence of clutters to the whole of system, improve identification for waveform, and then make ready for the next analysis. Method: We recommend time-varying filtering theory, make this theory as the basis to delimit experimental indicators for the specific requirements, then used LabVIEW2011`s paramount means packet for programming. Result: We have successfully used the varying filtering method to carry out clutters, improved the accuracy of the signal waveforms. Conclusion: Using the method described above can achieve the goal for the optimization on the heart sound signal processing, and also provide the reliable indemnification for analysis from the root.

Sparse Image and Signal Processing

2009 ◽  
Author(s):  
Jean-Luc Starck ◽  
Fionn Murtagh ◽  
Jalal Fadili
Keyword(s):  
Signal Processing
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