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Recent studies show that principal component analysis (PCA) of heartbeats is a well-performing method to derive a respiratory signal from ECGs. In this study, an improved ECG-derived respiration (EDR) algorithm based on kernel PCA (kPCA) is presented. KPCA can be seen as a generalization of PCA where nonlinearities in the data are taken into account by(More)
Early detection of seizures could reduce associated morbidity and mortality and improve the quality of life of patients with epilepsy. In this study, the aim was to investigate whether ictal tachycardia is present in focal and generalized epileptic seizures in children. We sought to predict in which type of seizures tachycardia can be identified before(More)
Artefacts can pose a big problem in the analysis of electrocardiogram (ECG) signals. Even though methods exist to reduce the influence of these contaminants, they are not always robust. In this work a new algorithm based on easy-to-implement tools such as autocorrelation functions, graph theory and percentile analysis is proposed. This new methodology(More)
Cerebral hemodynamics regulation consists of several mechanisms that try to keep brain homeostasis. In premature infants, due to the immaturity of their cerebral vascular bed, these mechanisms might be impaired exposing their brain to damage. The status of the cerebral regulation mechanism is classically assessed by measuring the coupling between some(More)
In this paper a methodology to identify sleep apnea events is presented. It uses four easily computable features, three generally known ones and a newly proposed feature. Of the three well known parameters, two are computed from the RR interval time series and the other one from the approximate respiratory signal derived from the ECG using principal(More)
One of the main challenges in unsupervised learning is to find suitable values for the model parameters. In kernel principal component analysis (kPCA), for example, these are the number of components, the kernel, and its parameters. This paper presents a model selection criterion based on distance distributions (MDDs). This criterion can be used to find the(More)
The effects of epilepsy on the autonomic control functions such as the heart rate and respiration can vary from one type of seizures to another. Complex-partial seizures originating in the temporal lobe for example, are accompanied by apnoea episodes, while absence seizures do not seem to alter the cardio-respiratory control in any significant manner. This(More)
BACKGROUND Autonomic dysfunctions occur during but also in between seizures. During seizures, the direct involvement of central autonomic control centers cause specific changes in heart rate and respiration. The pathophysiology of autonomic dysfunctions that are observed in the interictal period is more difficult to explain. These alterations are most(More)
GOAL This paper presents a methodology for the automatic detection of sleep apnea from single-lead ECG. METHODS It uses two novel features derived from the ECG, and two well-known features in heart rate variability analysis, namely the standard deviation and the serial correlation coefficients of the RR interval time series. The first novel feature uses(More)
The tachogram is typically constructed by detecting the R peaks in the electrocardiogram (ECG). Sometimes the ECG is however very noisy, which makes it hard to find the R peaks in these cases by using only the ECG. Information from other signals can then be used in order to find the R peaks. In this paper, a method is suggested that is able to automatically(More)