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This paper considers the general problem of detecting change in non-stationary signals using features observed in the time-frequency (t, f) domain, obtained using a class of quadratic timefrequency distributions (QTFDs). The focus of this study is to propose a methodology to define new (t, f) features by extending time-only and frequency-only features to the joint (t, f) domain for detecting changes in non-stationary signals. The (t, f) features are used as a representative subset characterizing the status of the observed non-stationary signal. Change in the signal is then reflected as a change in the (t, f) features. This (t, f) approach is applied to the problem of detecting abnormal brain activity in newborns (e.g. seizure) using measurements of the EEG for diagnosis and prognosis. In addition, a pre-processing stage for detecting artifacts in EEG signals for signal enhancement is studied and implemented separately. Overall results indicate that, in general, the (t, f) approach results in an improved performance in detecting artifacts and seizures in newborn EEG signals as compared to time-only or frequency-only features.

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SummaryThis paper presents a locally adaptive time‐frequency (t,f) method for estimating the instantaneous frequency (IF) of multi‐component signals. A high‐resolution adaptive directional time‐frequency distribution (ADTFD) is defined by locally adapting the direction of its smoothing kernel at each (t,f) point based on the direction of the energy distribution in the (t,f) domain. The IF of signal components is then estimated from the ADTFD using an image processing algorithm. Using the mean square error between the original IF and estimated IF as a performance criterion, experimental results indicate that the ADTFD gives better IF estimation performance compared with other TFDs for a multi‐component signal. For example, for signal‐to‐noise ratio of 12dB, the IF estimate obtained using the ADTFD achieves a mean square error of −42dB for a weak signal component, which is an improvement of −12dB compared with other TFDs. Copyright © 2015 John Wiley & Sons, Ltd.

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