Out-of-sample extension of kernel-based interpretation models for SVM regression usingoblique subspace projections

In this paper, we propose the use of nonlinear oblique subspace projections (NObSP) with support vector machines (SVM), as well as an out-of-sample extension of this algorithm to reduce its arithmetic complexity. NObSP was originally developed for least-Squares SVM, and it aims at decomposing the model output into additive components. Each component represents the partial (non)linear contribution of each input regressor, and their interaction effects, on the output. The original NObSP has an arithmetic complexity of O(N3), being N the number of observations. The proposed out-of-sample extension, combined with SVM, reduces the arithmetic complexity of NObSP to O(N*d2), with d the number of support vectors.Peer ReviewedPostprint (published version

Decomposition of a multiscale entropy tensor for sleep stage identification in preterm infants

Established sleep cycling is one of the main hallmarks of early brain development in preterm infants, therefore, automated classification of the sleep stages in preterm infants can be used to assess the neonate's cerebral maturation. Tensor algebra is a powerful tool to analyze multidimensional data and has proven successful in many applications. In this paper, a novel unsupervised algorithm to identify neonatal sleep stages based on the decomposition of a multiscale entropy tensor is presented. The method relies on the difference in electroencephalography(EEG) complexity between the neonatal sleep stages and is evaluated on a dataset of 97 EEG recordings. An average sensitivity, specificity, accuracy and area under the receiver operating characteristic curve of 0.80, 0.79, 0.79 and 0.87 was obtained if the rank of the tensor decomposition is selected based on the age of the infant. © 2019 by the authors

Measurement of neurovascular coupling in neonates

Neurovascular coupling refers to the mechanism that links the transient neural activity to the subsequent change in cerebral blood flow, which is regulated by both chemical signals and mechanical effects. Recent studies suggest that neurovascular coupling in neonates and preterm born infants is different compared to adults. The hemodynamic response after a stimulus is later and less pronounced and the stimulus might even result in a negative (hypoxic) signal. In addition, studies both in animals and neonates confirm the presence of a short hypoxic period after a stimulus in preterm infants. In clinical practice, different methodologies exist to study neurovascular coupling. The combination of functional magnetic resonance imaging or functional near-infrared spectroscopy (brain hemodynamics) with EEG (brain function) is most commonly used in neonates. Especially near-infrared spectroscopy is of interest, since it is a non-invasive method that can be integrated easily in clinical care and is able to provide results concerning longer periods of time. Therefore, near-infrared spectroscopy can be used to develop a continuous non-invasive measurement system, that could be used to study neonates in different clinical settings, or neonates with different pathologies. The main challenge for the development of a continuous marker for neurovascular coupling is how the coupling between the signals can be described. In practice, a wide range of signal interaction measures exist. Moreover, biomedical signals often operate on different time scales. In a more general setting, other variables also have to be taken into account, such as oxygen saturation, carbon dioxide and blood pressure in order to describe neurovascular coupling in a concise manner. Recently, new mathematical techniques were developed to give an answer to these questions. This review discusses these recent developments. © 2019 Hendrikx, Smits, Lavanga, De Wel, Thewissen, Jansen, Caicedo, Van Huffel and Naulaers. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms

Dynamic cerebral autoregulation reproducibility is affected by physiological variability

Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis. DCA methods were grouped into three broad categories, depending on output types: (1) transfer function analysis (TFA); (2) autoregulation index (ARI); and (3) correlation coefficient. Only TFA gain in the low frequency (LF) band showed good reproducibility in approximately half of the estimates of gain, defined as an intraclass correlation coefficient (ICC) of > 0.6. None of the other DCA metrics had good reproducibility. For TFA-like and ARI-like methods, ICCs were lower than values obtained with surrogate data (p less than 0.05). For TFA-like methods, ICCs were lower for the very LF band (gain 0.38 ± 0.057, phase 0.17 ± 0.13) than for LF band (gain 0.59 ± 0.078, phase 0.39 ± 0.11, p ? 0.001 for both gain and phase). For ARI-like methods, the mean ICC was 0.30 ± 0.12 and for the correlation methods 0.24 ± 0.23. Based on comparisons with ICC estimates obtained from surrogate data, we conclude that physiological variability or non-stationarity is likely to be the main reason for the poor reproducibility of DCA parameters. Copyright © 2019 Sanders, Elting, Panerai, Aries, Bor-Seng-Shu, Caicedo, Chacon, Gommer, Van Huffel, Jara, Kostoglou, Mahdi, Marmarelis, Mitsis, Müller, Nikolic, Nogueira, Payne, Puppo, Shin, Simpson, Tarumi, Yelicich, Zhang and Claassen

LS-SVM ponderado para la estimación de funciones aplicada a la eliminación de artefactos en el procesamiento de señales biológicas

Weighted LS-SVM is normally used for function estimation from highly corrupted data in order to decrease the impact of outliers. However, this method is limited in size and big time series should be segmented in smaller groups. Therefore, border discontinuities represent a problem in the final estimated function. Several methods such as committee networks or multilayer networks of LS-SVMs are used to address this problem, but these methods require extra training and hence the computational cost is increased. In this paper a technique that includes an extra weight vector in the formulation of the cost function for the LS-SVM problem is proposed as an alternative solution. The method is then applied to the removal of some artifacts in biomedical signals

Preprocesamiento mediante proyecciones subespaciales para la evaluación continua de la autorregulación cerebral mediante NIRS

Cerebral Autoregulation (CA) refers to the capability of the brain to maintain a more or less stable cerebral blood flow (CBF), despite the changes in blood perfusion. Monitoring this mechanism is of vital importance, especially in neonates, in order to prevent damage due to ischemia or hemorrhage. In clinical practice near-infrared spectroscopy (NIRS) measurements are used as a surrogate measurement for CBF. However, NIRS signals are highly dependent on the variations in arterial oxygen saturation (SaO 2 ). Therefore, only segments with relatively constant SaO 2 are used for CA assessment; which limits the possibilities of the use of NIRS for online monitoring. In this paper we propose the use of subspace projections to subtract the influence of SaO 2 from NIRS measurements. Since this approach will be used in an online monitoring system, this preprocessing is carried out in a window-by-window framework. However, the use of subspace projections in consecutive segments produces discontinuities; we propose a methodology to reduce these effects. Obtained results indicate that the proposed method reduces the effect of discontinuities between consecutive segments. In addition, this methodology is able to subtract the influence of SaO 2 from NIRS measurements. This approach facilitates the introduction of NIRS for online CA assessment

Análisis de las influencias respiratorias no lineales en la clasificación de la apnea del sueño

In this paper we propose the use of Kernel Principal Component Regression (KPCR) in order to model the nonlinear interaction between heart rate (HR) and respiration. We used wavelets in order to decompose the respiratory signal in 2 different frequency bands; namely, the low frequency band (LF) 0-0.078Hz, and the high frequency band (HF) 0.078-2.5Hz. We used the decomposed respiration as regressors in the KPCR model. Using the results provided by KPCR we computed the coupling between HR and the respiration in the LF and HF bands, separately. We evaluated the predictive power of these scores using the Physionet Sleep Apnea Dataset. In addition, we compare these results with the ones previously reported in our group, where we used a linear model based on orthogonal subspace projections and wavelet regression. We found that the features extracted using the nonlinear model improved the classification rate for apneic episodes when compared to the linear model, AUC =92.36 vs AUC = 88.29%

Uso de espectroscopia de infrarrojo cercano en la unidad de cuidados intensivos neonatales

Near-infrared spectroscopy was first described in 1977 as a non-invasive technique to measure the cerebral oxygenation and cytochrome oxydase. Different techniques have been developed resulting in new instruments that make it possible to measure cerebral oxygenation in a non-invasive way. In this chapter the physiology and pathophysiology in relation to the measurement of cerebral oxygenation are explained and the direct possible clinical use enlightened, with special focus on measurement of ischemic cerebral hypoxia. The measurement of other organs like the liver, the bowel and the peripheral circulation are described. At the end, a short overview of future possible bed-side measurements like functional near-infrared spectroscopy, near-infrared imaging and photoacoustic measurements are given

Preprocessing by means of subspace projections for continuous Cerebral Autoregulation assessment using NIRS

Cerebral Autoregulation (CA) refers to the capability of the brain to maintain a more or less stable cerebral blood flow (CBF), despite the changes in blood perfusion. Monitoring this mechanism is of vital importance, especially in neonates, in order to prevent damage due to ischemia or hemorrhage. In clinical practice near-infrared spectroscopy (NIRS) measurements are used as a surrogate measurement for CBF. However, NIRS signals are highly dependent on the variations in arterial oxygen saturation (SaO2). Therefore, only segments with relatively constant SaO2 are used for CA assessment; which limits the possibilities of the use of NIRS for online monitoring. In this paper we propose the use of subspace projections to subtract the influence of SaO2 from NIRS measurements. Since this approach will be used in an online monitoring system, this preprocessing is carried out in a window-by-window framework. However, the use of subspace projections in consecutive segments produces discontinuities; we propose a methodology to reduce these effects. Obtained results indicate that the proposed method reduces the effect of discontinuities between consecutive segments. In addition, this methodology is able to subtract the influence of SaO2 from NIRS measurements. This approach facilitates the introduction of NIRS for online CA assessment.status: publishe

Analysis of non-linear respiratory influences on sleep apnea classification

In this paper we propose the use of Kernel Principal Component Regression (KPCR) in order to model the nonlinear interaction between heart rate (HR) and respiration. We used wavelets in order to decompose the respiratory signal in 2 different frequency bands; namely, the low frequency band (LF) 0-0. 078Hz, and the high frequency band (HF) 0.078-2. 5Hz. We used the decomposed respiration as regressors in the KPCR model. Using the results provided by KPCR we computed the coupling between HR and the respiration in the LF and HF bands, separately. We evaluated the predictive power of these scores using the Physionet Sleep Apnea Dataset. In addition, we compare these results with the ones previously reported in our group, where we used a linear model based on orthogonal subspace projections and wavelet regression. We found that the features extracted using the nonlinear model improved the classification rate for apneic episodes when compared to the linear model, AUC =92.36 vs AUC = 88.29%.status: publishe