Key result
The proposed differential frequency window S-Transform algorithm produced better enhanced energy localization and lower mean square error compared to standard S-Transform, STFT, and CWT on ECG data.
A novel differential frequency window S-Transform algorithm improves time-frequency energy localization for non-stationary signals like ECGs with abrupt changes.
The S transform is an extension of Short Time Fourier Transform and Wavelet transform, has a time frequency resolution which is far from ideal. A differential frequency window is proposed in this paper to enhance the time frequency energy localization. When a non stationary signal consists of abrupt amplitude variation equal to peak of Gaussian function at initial intervals of chosen guassian window, then some part of the signal amplitude will be nullified during transform projection. The major function of differential frequency window is to track all abrupt amplitude-frequency variations which exploits in non – stationary signals. A mathematical method namely Newton Raphson method is adopted for this trace. The proposed scheme is tested for ECG data in presence of noise environment and results shows that proposed algorithm produces better enhanced energy localization in comparison to the standard S – Transform, STFT, and CWT. Furthermore the above algorithm is implemented on FPGA for real time applications.
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Krishna et al. (2018) studied ECG signal analysis. Differential frequency window S-Transform vs. Standard S-Transform, STFT, CWT was evaluated on Time frequency energy localization and Mean Square Error (MSE). The proposed differential frequency window S-Transform algorithm produced better enhanced energy localization and lower mean square error compared to standard S-Transform, STFT, and CWT on ECG data.
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