Why the study?
Does heart rate variability (HRV) analysis using time-frequency distribution accurately detect seizures in human neonates?
Does heart rate variability (HRV) analysis using time-frequency distribution accurately detect seizures in human neonates?
Heart rate variability analysis using time-frequency distribution, specifically LF mean frequency and HF variance, shows potential as a novel tool for detecting neonatal seizures.
May support HRV features for neonatal seizure detection; leaves open prospective validation before clinical use.
There are a number of automatic techniques available for detecting epileptic seizures using solely electroencephalogram (EEG), which has been the primary diagnosis tool in newborns. The electrocardiogram (ECG) has been much neglected in automatic seizure detection. Changes in heart rate and ECG rhythm were previously linked to seizure in case of adult humans and animals. However, little is known about heart rate variability (HRV) changes in human neonate during seizure. In this paper, we assess the suitability of HRV as a tool for seizure detection in newborns. The features of HRV in the low-frequency band (LF: 0.03–0.07 Hz), mid-frequency band (MF: 0.07–0.15 Hz), and high-frequency band (HF: 0.15–0.6 Hz) have been obtained by means of the time-frequency distribution (TFD). Results of ongoing time-frequency (TF) research are presented. Based on our preliminary results, the first conditional moment of HRV which is the mean/central frequency in the LF band and the variance in the HF band can be used as a good feature to discriminate the newborn seizure from the nonseizure.
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Malarvili et al. (2007) studied this question.
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