scholarly journals Shift Invariant Biorthogonal Discrete Wavelet Transform for EEG Signal Analysis

10.5772/30087 ◽  
2011 ◽  
Author(s):  
Juuso T. ◽  
Hannu Olkkone
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Signal Analysis ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signal Analysis

A Discrete-Wavelet-Transform- and Hidden-Markov-Model-Based Approach for Epileptic Focus Localization

2018 ◽  
pp. 34-45 ◽  
Author(s):  
Deba Prasad Dash ◽  
Maheshkumar .H Kolekar
Keyword(s):  
Wavelet Transform ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Discrete Wavelet Transform ◽  
Hidden Markov ◽  
Focal Seizure ◽  
Epileptic Focus ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Focus Localization

Epilepsy is the most common neurological disorder with 40-50 million people suffering with it worldwide. Epilepsy is not life threatening but it disables the person to a greater extent due to its uncertainty of occurrences. Epilepsy is detected by repeated occurrences of seizure. Seizure can be generated in brain due to abnormal activity of group of neurons caused by brain tumor, genetic problem, infection, hemorrhage etc. Seizure can be detected by observing the variation in Electroencephalogram (EEG) signal. Focal seizure is defined as seizure localized in one lobe of brain. In this chapter discrete wavelet transform and Hidden Markov Model based focal seizure detection method is proposed for epileptic focus localization. EEG signal was decomposed up to level 5 using dual tree complex wavelet transform and entropy features such as collision entropy, minimum and modified sample entropy were extracted. Hidden Markov model was used for classification purpose. Maximum 80% accuracy was achieved in detecting focal and non-focal EEG signal.

scholarly journals Fault Diagnosis of Industrial Robot Bearings Based on Discrete Wavelet Transform and Artificial Neural Network

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Alaa Abdulhady Jaber ◽  
Robert Bicker
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Discrete Wavelet Transform ◽  
Signal Analysis ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Discrete Wavelet ◽  
Bearing Faults

Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. An unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. The majority of the previous research on industrial robots health monitoring is focused on monitoring of a limited number of faults, such as backlash in gears, but does not diagnose the other gear and bearing faults. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults that could be progressed in the bearings of industrial robot joints, such as inner/outer race bearing faults, using vibration signal analysis. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults, and the artificial neural network (ANN) is used for faults classification. A data acquisition system based on National Instruments (NI) software and hardware was developed for robot vibration analysis and feature extraction. An experimental investigation was accomplished using the PUMA 560 robot. Firstly, vibration signals are captured from the robot when it is moving one joint cyclically. Then, by utilising the wavelet transform, signals are decomposed into multi-band frequency levels starting from higher to lower frequencies. For each of these levels the standard deviation feature is computed and used to design, train and test the proposed neural network. The developed system has showed high reliability in diagnosing several seeded faults in the robot.

Sign in / Sign up

Export Citation Format

Share Document