Non-invasive fetal heart rate is of great relevance in clinical practice to monitor fetal health state during pregnancy. To date, however, despite significant advances in the field of electrocardiography, the analysis of abdominal fetal ECG is considered a challenging problem for biomedical and signal processing communities. This is mainly due to the low signal-to-noise ratio of fetal ECG and difficulties in cancellation of maternal QRS complexes, motion and electromyographic artefacts. In this paper we present an efficient unsupervised algorithm for fetal QRS complex detection from abdominal multichannel signal recordings combining ICA and maternal ECG cancelling, which outperforms each single method. The signal is first pre-processed to remove impulsive artefacts, baseline wandering and power line interference. The following steps are then applied: maternal ECG extraction through independent component analysis (ICA); maternal QRS detection; maternal ECG cancelling through weighted singular value decomposition; enhancing of fetal ECG through ICA and fetal QRS detection. We participated in the Physionet/Computing in Cardiology Challenge 2013, obtaining the top official scores of the challenge (among 53 teams of participants) of event 1 and event 2 concerning fetal heart rate and fetal interbeat intervals estimation section. The developed algorithms are released as open-source on the Physionet website.
Heart rate variability (HRV) is a well-known phenomenon whose characteristics are of great clinical relevance in pathophysiologic investigations. In particular, respiration is a powerful modulator of HRV contributing to the oscillations at highest frequency. Like almost all natural phenomena, HRV is the result of many nonlinearly interacting processes; therefore any linear analysis has the potential risk of underestimating, or even missing, a great amount of information content. Recently the technique of Empirical Mode Decomposition (EMD) has been proposed as a new tool for the analysis of nonlinear and nonstationary data. We applied EMD analysis to decompose the heartbeat intervals series, derived from one electrocardiographic (ECG) signal of 13 subjects, into their components in order to identify the modes associated with breathing. After each decomposition the mode showing the highest frequency and the corresponding respiratory signal were Hilbert transformed and the instantaneous phases extracted were then compared. The results obtained indicate a synchronization of order 1:1 between the two series proving the existence of phase and frequency coupling between the component associated with breathing and the respiratory signal itself in all subjects.
Atrial fibrillation (AF) is the most common cardiac arrhythmia and entails an increased risk of thromboembolic events. Prediction of the termination of an AF episode, based on noninvasive techniques, can benefit patients, doctors and health systems. The method described in this paper is based on two-lead surface electrocardiograms (ECGs): 1-min ECG recordings of AF episodes including N-type (not terminating within an hour after the end of the record), S-type (terminating 1 min after the end of the record) and T-type (terminating immediately after the end of the record). These records are organised into three learning sets (N, S and T) and two test sets (A and B). Starting from these ECGs, the atrial and ventricular activities were separated using beat classification and class averaged beat subtraction, followed by the evaluation of seven parameters representing atrial or ventricular activity. Stepwise discriminant analysis selected the set including dominant atrial frequency (DAF, index of atrial activity) and average HR (HRmean, index of ventricular activity) as optimal for discrimination between N/T-type episodes. The linear classifier, estimated on the 20 cases of the N and T learning sets, provided a performance of 90% on the 30 cases of a test set for the N/T-type discrimination. The same classifier led to correct classification in 89% of the 46 cases for N/S-type discrimination. The method has shown good results and seems to be suitable for clinical application, although a larger dataset would be very useful for improvement and validation of the algorithms and the development of an earlier predictor of paroxysmal AF spontaneous termination time.
Epilepsy is often associated with modifications in autonomic nervous system, which usually precede the onset of seizures of several minutes. Thus, there is a great interest in identifying these modifications enough time in advance to prevent a dangerous effect and to intervene. In addition, these changes can be a risk factor for epileptic patients and can increase the possibility of death. Notably autonomic changes associated to seizures are highly depended of seizure type, localization and lateralization. The aim of this study was to develop a patient-specific approach to predict seizures using electrocardiogram (ECG) features. Specifically, from the RR series, both time and frequency variables and features obtained by the recurrence quantification analysis were used. The algorithm was applied in a dataset of 15 patients with 38 different types of seizures. A feature selection step, was used to identify those features that were more significant in discriminating preictal and interictal phases. A preictal interval of 15 minutes was selected. A support vector machine (SVM) classifier was then built to classify preictal and interictal phases. First, a classifier was set up to classify preictal and interictal segments of each patient and an average sensibility of 89.06% was obtained, with a number of false positive per hour (FP/h) of 0.41. Then, in those patients who had at least 3 seizures, a double-cross-validation approach was used to predict unseen seizures on the basis of a training on previous ones. The results were quite variable according to seizure type, achieving the best performance in patients with more stereotypical seizure. The results of the proposed approach show that it is feasible to predict seizure in advance, considering patient-specific, and possible seizure specific, characteristics.
In the context of HRV analysis, we evaluated the information content of two measures that can easily be derived from the classical RR time-domain indexes. The two measures are: 1) the ratio sd/rmssd, where sd is the RR standard deviation and rmssd is the root mean square of squared differences of consecutive RR beats; and 2) the ratio sd2/sd1, where sd2 and sd1 are extracted from the Poincaré plot and represent the transversal and longitudinal dispersion of the cloud of points (RR(i),RR(i)(+1)). We compared the performance of the two measures with that of the classical LF/HF ratio in a group of healthy subjects who underwent a 70 degrees upright tilt test. The goodness of the results obtained by the two measures, the simplicity of their calculation and their applicability free from a priori assumptions on the characteristics of the data are proposed to the attention of the community involved in the HRV analysis as a possible alternative to the LF/HF ratio.
Background: Autism Spectrum Disorders (ASD) are a heterogeneous group of neurodevelopmental disorders featuring early impairments in social domain, with autonomic nervous system (ANS) unbalance possibly representing a useful marker for such disturbances. Impairments in joint attention (JA) are one of the earliest markers of social deficits in ASD. In this study, we assessed the feasibility of using wearable technologies for characterizing the ANS response in ASD toddlers during the presentation of JA stimuli.Methods: Twenty ASD toddlers and 20 age- and gender-matched typically developed (TD) children were recorded at baseline and during a JA task through an unobtrusive chest strap for electrocardiography (ECG). Specific algorithms for feature extraction, including Heart Rate (HR), Standard Deviation of the Normal-to-Normal Intervals (SDNN), Coefficient of Variation (CV), pNN10 as well as low frequency (LF) and high frequency (HF), were applied to the ECG signal and a statistical comparison between the two groups was performed.Results: As regards the single phases, SDNN (p = 0.04) and CV (p = 0.021) were increased in ASD at baseline together with increased LF absolute power (p = 0.034). Moreover, CV remained higher in ASD during the task (p = 0.03). Considering the phase and group interaction, LF increased from baseline to task in TD group (p = 0.04) while it decreased in the ASD group (p = 0.04).Conclusions: The results of this study indicate the feasibility of characterizing the ANS response in ASD toddlers through a minimally obtrusive tool. Our analysis showed an increased SDNN and CV in toddlers with ASD particularly at baseline compared to TD and lower LF during the task. These findings could suggest the possibility of using the proposed approach for evaluating physiological correlates of JA response in young children with ASD.
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