scholarly journals Pattern Recognition and ERP Waveform Analysis Using Wavelet Transform

2000 ◽  
Author(s):  
Hong Qi
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Waveform Analysis

Techniques for Decomposition of EMG Signals

2011 ◽  
pp. 467-477
Author(s):  
Arun Kumar Wadhwani ◽  
Sulochana Wadhwani
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Muscle Fatigue ◽  
Cross Correlation ◽  
Neuromuscular Disorders ◽  
Clinical Importance ◽  
Neuromuscular Control ◽  
Diagnostic Information ◽  
Self Organizing Map ◽  
Statistical Pattern

The information extracted from the EMG recordings is of great clinical importance and is used for the diagnosis and treatment of neuromuscular disorders and to study muscle fatigue and neuromuscular control mechanism. Thus there is a necessity of efficient and effective techniques, which can clearly separate individual MUAPs from the complex EMG without loss of diagnostic information. This chapter deals with the techniques of decomposition based on statistical pattern recognition, cross-correlation, Kohonen self-organizing map and wavelet transform.

Invariant pattern-recognition filter based on the wavelet transform

1996 ◽  
Author(s):  
Zeev Zalevsky ◽  
David Mendlovic ◽  
Carlos Ferreira
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform

Application of Pattern Recognition Techniques to Breast Cancer Detection: Ultrasonic Analysis of 100 Pathologically Confirmed Tissue Areas

1982 ◽  
Vol 4 (4) ◽  
pp. 378-396 ◽  
Author(s):  
Morris S. Good ◽  
Joseph L. Rose ◽  
Barry B. Goldberg
Keyword(s):  
Pattern Recognition ◽  
Ultrasonic Pulse ◽  
Waveform Analysis ◽  
Algorithm Performance ◽  
Linear Discriminant ◽  
Pattern Recognition Techniques ◽  
Pattern Recognition Methods ◽  
Jackknife Procedure ◽  
Pulse Echo

Ultrasonic pulse-echo rf waveform analysis and selected pattern recognition methods were applied to classification of breast tissue. Emphasis was placed on the classification of solid tissue areas since fluid areas are easily identified by present B-scan techniques. Pattern recognition techniques such as the Fisher Linear Discriminant (FLD), Probability Density Function (PDF) curves, jackknife estimate and committee vote were used to construct and evaluate a two class algorithm, malignant versus benign tissue areas. A data base consisting of frequency domain features from 100 pathologically confirmed tissue areas from 87 patients were used to train the algorithm. Algorithm performance was acquired via the generalized jackknife procedure to significantly reduce the bias frequently encountered in algorithm evaluation. Estimated values of algorithm performance are sensitivity and specificity values of 96 percent and 68 percent, respectively.

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