Adaptive rule based fetal QRS complex detection using hilbert transform

Ulusar, Umit Deniz; Govindan, Rathinaswamy B.; Wilson, James D.; Lowery, Curtis L.; Preißl, Hubert; Eswaran, Hari

doi:10.1109/iembs.2009.5334180

Cited by 45 publications

(34 citation statements)

References 9 publications

(11 reference statements)

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“…The R-waves were identified using a combination of Hilbert transform and an adaptive threshold detection approach, 15 and heart rate was defined. The artifacts in the heart rate data were cleaned using an automated approach 16 .…”

Section: Methodsmentioning

confidence: 99%

Inflammatory cytokine response and reduced heart rate variability in newborns with hypoxic-ischemic encephalopathy

et al. 2017

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Objective To determine whether systemic inflammation-modulating cytokine expression is related to heart rate variability (HRV) in newborns with hypoxic ischemic encephalopathy (HIE). STUDY DESIGN Data from 30 newborns with HIE were analyzed. Cytokine levels (IL-2, IL-4, IL-6, IL-8, IL-10, IL-13, IL-1β, TNF-α, IFN-λ) were measured either at 24 hours of cooling (n=5), 72 hours of cooling (n=4), or at both timepoints (n=21). The following HRV metrics were quantified in the time domain: alpha_S, alpha_L, root mean square (RMS) at short time scales (RMS_S), RMS at long time scales (RMS_L), while low frequency power (LF) and high frequency power (HF) were quantified in the frequency domain. The relationships between HRV metrics and cytokines were evaluated using mixed-models. Results IL-6, IL-8, IL-10, and IL-13 levels were inversely related to selected HRV metrics. Conclusion Inflammation-modulating cytokines may be important mediators in the autonomic dysfunction observed in newborns with HIE.

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Section: Methodsmentioning

confidence: 99%

Inflammatory cytokine response and reduced heart rate variability in newborns with hypoxic-ischemic encephalopathy

et al. 2017

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“…(12, 16) Briefly, after the data were bandpass filtered between 0.5–70Hz, and semi-automated artifact rejection applied, the R-wave was identified using adaptive Hilbert transform approach (31, 32). For spectral analysis the RRi was converted into evenly sampled data using cubic-spline interpolation at a sample rate of 4 Hz.…”

Section: Methodsmentioning

confidence: 99%

Effect of Temperature on Heart Rate Variability in Neonatal ICU Patients With Hypoxic-Ischemic Encephalopathy

Massaro

Campbell

Metzler

et al. 2017

Pediatric Critical Care Medicine

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Objective To determine whether measures of heart rate variability (HRV) are related to changes in temperature during rewarming after therapeutic hypothermia (TH) for hypoxic ischemic encephalopathy (HIE). Design Prospective observational study. Setting Level 4 neonatal intensive care unit in a free-standing academic children's hospital. Patients Forty-four infants with moderate to severe HIE treated with TH. Interventions Continuous EKG data from 2 hours prior to rewarming through 2 hours after completion of rewarming (up to 10 hours) were analyzed. Measurements and Main Results Median beat-to-beat interval (RRi) and measures of HRV were quantified including RRi standard deviation (SD), low (LF) and high (HF) frequency relative spectral power, detrended fluctuation analysis short- and long- α exponents (αS, αL) and root mean square short- and long- time scales (RMSS, RMSL). The relationships between HRV measures and esophageal/axillary temperatures were evaluated. HRV measures LF, αS, RMSS, and RMSL were negatively associated, while αL was positively associated, with temperature (P<0.01). These findings signify an overall decrease in HRV as temperature increased towards normothermia. Conclusions Measures of HRV are temperature dependent in the range of TH to normothermia. Core body temperature needs to be considered when evaluating HRV metrics as potential physiological biomarkers of illness severity in HIE infants undergoing TH.

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“…These recordings were retrieved from the clinical database and exported into MATLAB (MathWorks, Inc., MA, USA) for further processing. ECG was bandpass filtered between 0.05 and 80 Hz and the R-wave was identified using an adaptive Hilbert transform approach [21]. The heart rate was calculated as 60 divided by the time between successive R waves expressed in seconds.…”

Section: Methodsmentioning

confidence: 99%