No abstract

In this paper we introduce an adaptive rule based QRS detection algorithm using the Hilbert transform (adHQRS) for fetal magnetocardiography processing. Hilbert transform is used to combine multiple channel measurements and the adaptive rule based decision process is used to eliminate spurious beats. The algorithm has been tested with a large number of datasets and promising results were obtained.

Changes in fetal magnetocardiographic (fMCG) signals are indicators for fetal body movement. We propose a novel approach to reliably extract fetal body movements based on the field strength of the fMCG signal independent of its frequency. After attenuating the maternal MCG, we use a Hilbert transform approach to identify the R-wave. At each R-wave, we compute the center-ofgravity (cog) of the coordinate positions of MCG sensors, each weighted by the magnitude of the R-wave amplitude recorded at the corresponding sensor. We then define actogram as the distance between the cog computed at each R-wave and the average of the cog from all the R-waves in a 3-min duration. By applying a linear de-trending approach to the actogram we identify the fetal body movement and compare this with the synchronous occurrence of the acceleration in the fetal heart rate. Finally, we apply this approach to the fMCG recorded simultaneously with ultrasound from a single subject and show its improved performance over the QRS-amplitude based approach in the visually verified movements. This technique could be applied to transform the detection of fetal body movement into an objective measure of fetal health and enhance the predictive value of prevalent clinical testing for fetal wellbeing.

No abstract

OBJECTIVE-Intraventricular hemorrhage remains an important problem among very low birth weight infants and may result in long-term neurodevelopmental disabilities. Neonatologists have been unable to accurately predict impending intraventricular hemorrhage. Because alterations in the autonomic nervous system's control of heart rhythm have been associated with intraventricular hemorrhage after its development, we sought to determine if early subtle alterations of heart rhythm could be predictive of impending intraventricular hemorrhage in very low birth weight infants.METHODS-This case-control study included 10 newborn very low birth weight infants with intraventricular hemorrhage (5 grade IV, 4 grade III, and 1 grade II) and 14 control infants without intraventricular hemorrhage. Heart rhythm data from the first day of life before the development of intraventricular hemorrhage were evaluated. Detrended fluctuation analysis, a nonlinear fractal heart rate variability method, was used to assess the fractal dynamics of the heart rhythm. Fractal scaling exponents were calculated by using this analysis.RESULTS-Twenty-four infants (mean ± SD, birth weight: 845 ± 213g: gestational age: 26.1 ± 1.9 weeks) participated in the study. The short-term scaling exponent was significantly larger in infants who later developed intraventricular hemorrhage compared with those who did not (0.60 ± 0.1 vs 0.45 ± 0.1). A value of 0.52 resulted in 70% sensitivity and positive predictive value and 79% specificity and negative predictive value. The short-term scaling exponent was the only significant predictor of intraventricular hemorrhage.CONCLUSIONS-Fractal dynamics of the heart rhythm is significantly altered in very low birth weight infants before developing intraventricular hemorrhage and may be predictive of impending intraventricular hemorrhage. 8,9 Given this important public health problem, it would be quite valuable to identify VLBW infants who are at highest risk for developing IVH, before they actually develop it, so that they may benefit from prevention and intervention strategies. Unfortunately, accurate prediction methodologies of impending IVH have not been developed.Altered autonomic function has been observed by heart rate variability (HRV) analysis in premature infants after IVH. 10-12 HRV refers to the beat-to-beat (R-R interval from the electrocardiogram) fluctuation of heart rate and reflects the balance between parasympathetic and sympathetic impulses to the heart that is under central nervous system control. Researchers have used HRV analysis as a tool to estimate autonomic neural control of the heart and for predicting impending arrhythmia 13 and long-term outcome of VLBW infants. 11,14 The objective of this study was to determine if impending IVH in VLBW infants may be predicted by altered autonomic function. We hypothesized that autonomic dysfunction precedes the development of IVH in VLBW infants. We used a nonlinear method, called detrended fluctuation analysis (DFA), 11,15 to examine the HRV characte...

We propose a novel computational approach to automatically identify the fetal heart rate patterns (fHRPs), which are reflective of sleep/awake states. By combining these patterns with presence or absence of movements, a fetal behavioral state (fBS) was determined. The expert scores were used as the gold standard and objective thresholds for the detection procedure were obtained using Receiver Operating Characteristics (ROC) analysis. To assess the performance, intraclass correlation was computed between the proposed approach and the mutually agreed expert scores. The detected fHRPs were then associated to their corresponding fBS based on the fetal movement obtained from fetal magnetocardiogaphic (fMCG) signals. This approach may aid clinicians in objectively assessing the fBS and monitoring fetal wellbeing.

No abstract

The purpose of fetal magnetoencephalography (fMEG) is to record and analyze fetal brain activity. Unavoidably, these recordings consist of a complex mixture of bio-magnetic signals from both mother and fetus. The acquired data include biological signals that are related to maternal and fetal heart function as well as fetal gross body and breathing movements. Since fetal breathing generates a significant source of bio-magnetic interference during these recordings, the goal of this study was to identify and quantify the signatures pertaining to fetal breathing movements (FBM). The fMEG signals were captured using superconducting quantum interference devices (SQUIDs) The existence of FBM was verified and recorded concurrently by an ultrasound-based video technique. This simultaneous recording is challenging since SQUIDs are extremely sensitive to magnetic signals and highly susceptible to interference from electronic equipment. For each recording, an ultrasound-FBM (UFBM) signal was extracted by tracing the displacement of the boundary defined by the fetal thorax frame by frame. The start of each FBM was identified by using the peak points of the UFBM signal. The bio-magnetic signals associated with FBM were obtained by averaging the bio-magnetic signals time locked to the FBMs. The results showed the existence of a distinctive sinusoidal signal pattern of FBM in fMEG data.