Neonatal Seizure Detection using Time-Frequency Renyi Entropy of HRV signals

The Time-Frequency Renyi Entropy (TFRE) uses a timefrequency distribution (TFD) of a signal to provide a measure of the signal information content and complexity in the timefrequency (TF) plane. The concept is applied to the problem of newborn seizure detection using the HRV signal. This papers provides an experimental comparison of the performance of the TFRE obtained from selected TFDs, including the WignerVille distribution (WVD), the spectrogram (SPEC), the ChoiWilliam Distribution (CWD), the Born-Jordan Distribution (BJD), and the Modified B Distribution (MBD). The test signals include a multi-component Gabor logon and a three component linear FM signals, as well as real-life signals. Such a choice of synthetic test signals has been motivated by the fact that Gabor logons are essentially the building blocks in the TF plane of all signals, while the LFM is a good model for many real-life signals. The comparison results provided in this paper, illustrate the effects of the signals TF parameters (namely, the components time and frequency separation, their amplitude modulation, the changes in the components time duration, as well as the bandwidth variations and noise effects) on the TFRE evaluated from the considered TFDs. The results are shown to benefit practical applications of the TFRE in general. For the specific application considered in this paper, it has been shown that the MBD based TFRE of newborn heart rate variability (HRV) can be successfully used as a critical feature in neonatal seizure detection.