Wavelet analysis of compressed biomedical signals

2017 ◽  
Author(s):  
Andrey B. Stepanov
Keyword(s):  
Wavelet Analysis ◽  
Biomedical Signals

Wavelet analysis of biomedical signals

2006 ◽  
Vol 60 (8) ◽  
pp. 477-478
Author(s):  
N. Ambrosino ◽  
G. De Michele ◽  
S. Sello ◽  
S.-K. Strambi
Keyword(s):  
Wavelet Analysis ◽  
Biomedical Signals

scholarly journals A Wavelet-based Energetic Approach for the Analysis of Electroencephalogram

2012 ◽  
Vol 17 (2) ◽  
pp. 232 ◽  
Author(s):  
Abul Hasan Siddiqi ◽  
Hulya Kodal Sevindir
Keyword(s):  
Wavelet Analysis ◽  
Epileptic Activity ◽  
Frequency Resolution ◽  
Eeg Signals ◽  
Energy Redistribution ◽  
Signal Energy ◽  
Biomedical Signals ◽  
Energetic Approach ◽  
Epileptic Disorder ◽  
The Brain

Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. The main application of EEG is in the case of epilepsy, as epileptic activity can create clear abnormalities on a standard EEG study. EEG signals, like many biomedical signals, are highly non-stationary by their nature. Wavelet analysis has found a prominent position in the investigation of biomedical signals for its ability to analyze such signals, in particular EEG signals. Wavelet transform is capable of separating the signal energy among different frequency bands (i.e., different scales), achieving a good compromise between temporal and frequency resolution. The present study is an attempt at better understanding of the mechanism causing the epileptic disorder and accurate prediction of the occurrence of seizures. In the present paper we identify typical patterns of energy redistribution before and during a seizure using multi-resolution wavelet analysis.  

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

Study of Identification Method of Drunk Driving Based on Wavelet Analysis

ICCTP 2011 ◽  
2011 ◽  
Author(s):  
Xing-jian Zhang ◽  
Xiao-hua Zhao ◽  
Jian Rong ◽  
Shi-li Xu
Keyword(s):  
Wavelet Analysis ◽  
Drunk Driving ◽  
Identification Method

Efficiency of application of the phase-chronometric method and neurodiagnostics for monitoring the degradation of rolling bearings during operation

2020 ◽  
pp. 43-50
Author(s):  
A.S. Komshin ◽  
K.G. Potapov ◽  
V.I. Pronyakin ◽  
A.B. Syritskii
Keyword(s):  
Life Cycle ◽  
Wavelet Analysis ◽  
Metrological Support ◽  
Rolling Bearing ◽  
Technical Condition ◽  
Measurement Information ◽  
Rolling Bearings ◽  
Vibration Diagnostics ◽  
Bearing Life ◽  
Alternative Approach

The paper presents an alternative approach to metrological support and assessment of the technical condition of rolling bearings in operation. The analysis of existing approaches, including methods of vibration diagnostics, envelope analysis, wavelet analysis, etc. Considers the possibility of applying a phase-chronometric method for support on the basis of neurodiagnostics bearing life cycle on the basis of the unified format of measurement information. The possibility of diagnosing a rolling bearing when analyzing measurement information from the shaft and separator was evaluated.

WAVELET ANALYSIS OF CLIMATE FACTORS VARIATIONS OF KHANTY-­MANSIYSK CITY

Human Ecology ◽  
2017 ◽  
pp. 33-37 ◽  
Author(s):  
O. N. Ragozin ◽  
V. I. Korchin ◽  
E. Yu. Shalamova ◽  
E. R. Ragozina
Keyword(s):  
Wavelet Analysis ◽  
Climate Factors
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