Reducing cross terms effects in the Choi–Williams transform of mioelectric signals

Abstract

This study aims at investigating the effect of removing the negative values of Choi-Wiliams distribution (CWD) related to the electromyogram (EMG) for visualization and instantaneous median frequency (IMF) estimation. Beyond the EMG signals from triceps surae and biceps brachialis, the CWD was applied in a simulated sinusoidal signal as like in stationary and non-stationary simulated EMG signals (SES). The CWD negative values of all simulated and EMG signals were removed. The IMF values were obtained for SES and EMG. The CWD IMF values from SES and EMG were thus compared with the IMF values from short time Fourier transform (STFT) by means of correlation. The suppression of negative values from the CWD reduced cross terms influence and improved visualization, as shown by the increased correlation coefficient between the IMF values. Before this suppression, the extracted IMF values showed large oscillation along the time, with various spurious values beyond 500Hz, which disappeared after the suppression. Moreover, this procedure seems to be especially useful for non-stationary signals.

An Artificial Intelligence Approach to Fatigue Crack Length Estimation from Acoustic Emission Waves in Thin Metallic Plates

“…Finally, the Choi-Williams transform (CWT) is performed on the wave ("step 5"). The Choi-Williams distribution (CWD) is a time-frequency transform that is a Cohen class member, which is related to parameters such as the instantaneous median frequency and the instantaneous power (integral over all frequencies at each time) [38,39]. This CWT is a type of wavelet transform that yields an intensity plot that gives information of both the time and frequency domain of the AE wave simultaneously.…”

An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier

“…Pereira [33] proposed more cross-term reduction for Cohen's class so that only the positive matrix part contains the data information, while the negative part is related to the cross-terms and can be excluded from the Choi-Williams matrix result. This concept has been extended to various Cohen's class applications, such as electromyogram (EMG) signal and ultrasound signal.…”

Laser Ultrasound Inspection Based on Wavelet Transform and Data Clustering for Defect Estimation in Metallic Samples

“…There are other irrelative and undesirable components defined as cross-terms that are oscillating components found between the auto-terms. Cross-terms hide some information of the main signal and reduce the resolution, making it difficult to interpret the resulting distribution [29,33]. An alternative approach to the STFT, to overcome the constant resolution problem, and for the Cohen’s class distributions to overcome cross-terms overlapping, is the wavelet transform.…”