Phase plane based identification of fetal heart rate patterns

Govindan, Rathinaswamy B.; Sriram, Bhargavi; Wilson, James D.; Preißl, Hubert; Eswaran, Hari

doi:10.1109/iembs.2011.6090337

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…In general, the peak of the QRS is detected to acquire the RR intervals and calculate the heart rate [13]. In our recent work we introduced a new HRV measure, Phase Plane Area (PPA) and applied it to fMCG recordings[15]. PPA is a non-linear metric based on the concept of Poincaré analysis that is used to characterize the recurrence property of trajectories in the phase space.…”

Section: Introductionmentioning

confidence: 99%

Differences in the sleep states of IUGR and low-risk fetuses: An MCG study

Sriram

Mencer

McKelvey

et al. 2013

Early Human Development

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Background Intrauterine growth restriction (IUGR) is a fetal condition characterized by growth-rate reduction. Afflicted fetuses tend to display abnormalities in heart rate. Objective To study the differences in the heart-rate variability of low-risk fetuses and IUGR fetuses during different behavioral states. Methods A total of 40 fetal magnetocardiograms were recorded from 20 low-risk and 20 IUGR fetuses using a 151-sensor SQUID-array system. The maternal cardiac signals were attenuated using signal-space projection. Fetal R waves were identified using an adaptive Hilbert transform approach and fetal heart rate calculated. In each three-minute window, the heart rate was classified into patterns reflective of quiet sleep (pattern A) and active sleep (pattern B) using the criteria of Nijhuis. Two adjacent 3-minute windows exhibiting the same pattern were selected for analysis from every dataset. Heart-rate variability in that 6-minute window was characterized using three measures, Standard Deviation of Normal to Normal (SDNN), Root Mean Square of Successive Differences (RMSSD) and Phase Plane Area (PPA). Results All three measures tended to be lower in the IUGR group compared to the low-risk group. However, when the measures were analyzed in patterns, only PPA showed significant difference between the risk groups in Pattern A, where as both PPA and SDNN showed highly significant risk-group differences in Pattern B. RMSSD did not show any significant risk-group difference. Conclusion The result signifies that the heart-rate variability of IUGR fetuses is different from that of low-risk fetuses, and only PPA was able to capture the HRV differences in both quiet and active states. The difference between these two groups of fetuses shows that the fetal-activity states are potential confounders when characterizing heart-rate variability.

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Section: Introductionmentioning

confidence: 99%

Differences in the sleep states of IUGR and low-risk fetuses: An MCG study

Sriram

Mencer

McKelvey

et al. 2013

Early Human Development

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“…Additional methods based on statistical physics such as detrended fluctuation analysis [3] and multifractal analysis [4], have also been used to characterize the low- and high-frequency fluctuations in the RRis. Likewise, novel methods such as phase-rectified signal averaging [5] and phase-plane analysis [6] also have been used to characterize the heart rate in time-domain. In the frequency domain RRis are analyzed using the power spectral approach [2, 7-9].…”

Section: Introductionmentioning

confidence: 99%

Mitigating the effect of non-stationarity in spectral analysis—An application to neonate heart rate analysis

Govindan

Massaro

Niforatos

et al. 2013

Computers in Biology and Medicine

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In order to mitigate the effect of non-stationarity in frequency domain analysis of data, we propose a modification to the power spectral estimation, a widely used technique to characterize physiological signals. Spectral analysis requires partitioning data into smaller epochs determined by the desired frequency resolution. The modified approach proposed here involves dividing the data within each epoch by the standard deviation of the data for that epoch. We applied this modified approach to cardiac beat-to-beat interval data recorded from a newborn infant undergoing hypothermia treatment for birth asphyxia. The critically ill infant had episodes of tachyarrhythmia, distributed sporadically throughout the study, which affected the stationarity of the heart rate. Over the period of continuous heart rate recording, the infant’s clinical course deteriorated progressively culminating in death. Coinciding with this clinical deterioration, the heart rate signal showed striking changes in both low-frequency and high-frequency power indicating significant impairment of the autonomic nervous system. The standard spectral approach failed to capture these phenomena because of the non-stationarity of the signal. Conversely, the modified approach proposed here captured the deteriorating physiology of the infant clearly.

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“…Further, pNNx, the probability that the current interval is greater than x milliseconds from the previous interval, has been used to quantify the (a) E-mail: rgovinda@childrensnational.org parasympathetic component of RRi [1]. Several novel time domain approaches based on the concepts derived from statistical physics [8,9], nonlinear dynamics [10,11] and information theory [12] have been developed to characterize the RRi.…”

mentioning

confidence: 99%

Detrended fluctuation analysis of non-stationary cardiac beat-to-beat interval of sick infants

Govindan¹,

Massaro²,

Al-Shargabi³

et al. 2014

EPL

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We performed detrended fluctuation analysis (DFA) of cardiac beat-to-beat intervals (RRis) collected from sick newborn infants over 1-4 day periods. We calculated four different metrics from the DFA fluctuation function: the DFA exponents αL (>40 beats up to one-fourth of the record length), αs (15-30 beats), root-mean-square (RMS) fluctuation on a short-time scale (20-50 beats), and RMS fluctuation on a long-time scale (110-150 beats). Except αL, all metrics clearly distinguished two groups of newborn infants (favourable vs. adverse) with well-characterized outcomes. However, the RMS fluctuations distinguished the two groups more consistently over time compared to αS. Furthermore, RMS distinguished the RRi of the two groups earlier compared to the DFA exponent. In all the three measures, the favourable outcome group displayed higher values, indicating a higher magnitude of (auto-)correlation and variability, thus normal physiology, compared to the adverse outcome group.

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Phase plane based identification of fetal heart rate patterns

Cited by 10 publications

References 12 publications

Differences in the sleep states of IUGR and low-risk fetuses: An MCG study

Differences in the sleep states of IUGR and low-risk fetuses: An MCG study

Mitigating the effect of non-stationarity in spectral analysis—An application to neonate heart rate analysis

Detrended fluctuation analysis of non-stationary cardiac beat-to-beat interval of sick infants

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