Dynamic Q value wavelet transform for IF estimation and seizure detection via QT plane-CNN framework
Abstract The instantaneous frequency (IF) is the underlying feature for analyzing the non-stationary signals. Separating the mono components present in a non-stationary multi-component signal is a crucial step for estimating their IF. In this paper, we suggest a new decomposition method to separate...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-07-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-025-01229-4 |
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| Summary: | Abstract The instantaneous frequency (IF) is the underlying feature for analyzing the non-stationary signals. Separating the mono components present in a non-stationary multi-component signal is a crucial step for estimating their IF. In this paper, we suggest a new decomposition method to separate the mono components from a multi-component non-stationary signal using a novel dynamic quality (Q) value wavelet transform (DQVWT). The signal is windowed in the time domain using a moving Gaussian function, and then, the components in the windowed signal are separated using the array of tunable-Q wavelet transform (TQWT) blocks. Here, the Q values are computed dynamically as the Gaussian window traverses over a multi-component signal. The components are added to generate mono components and then their IF is estimated. The proposed method is evaluated on multi-component non-stationary signals whose mono components are either very close in the T–F plane or overlapped in the T–F plane. Additionally, a Q vs time (QT) plane is proposed as a new feature for the classification of non-stationary signals. The efficacy of the proposed method is evaluated in terms of mean square error (MSE) and compared it with other existing methods under different signal-to-noise ratio (SNR) conditions. Through simulation results, it is shown that the MSE in IF estimation obtained using the proposed method is lower when compared with the other methods. Also, the proposed method is applied to a bat echo signal for the IF estimation, and the proposed QT contour is used for the classification of the EEG signals. |
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| ISSN: | 1687-6180 |