Reliable gradient search directions for kurtosis-based deflationary ICA: Application to physiological signal processing.

Efficient gradient search directions for the optimisation of the kurtosis-based deflationary RobustICA algorithm in the case of real-valued data are proposed in this paper. The proposed scheme employs, in the gradient-like algorithm typically used to optimise the considered kurtosis-based objective function, search directions computed from a more reliable approximation of the negentropy than the kurtosis. The proposed scheme inherits the exact line search of the conventional RobustICA for which a good convergence property through a given direction is guaranteed. The efficiency of the proposed scheme is evaluated in terms of estimation quality, the execution time and the iterations count as a function of the number of used sensors and for different signal to noise ratios in the contexts of non-invasive epileptic ElectroEncephaloGraphic (EEG) and Magnetic Resonance Spectroscopic (MRS) analysis. The obtained results show that the proposed approach offer the best estimation performance/iterations count and execution time trade-off, especially in the case of high number of sensors.

Low cost and efficient kurtosis-based deflationary ICA method: application to MRS sources separation problem.

Improving the execution time and the numerical complexity of the well-known kurtosis-based maximization method, the RobustICA, is investigated in this paper. A Newton-based scheme is proposed and compared to the conventional RobustICA method. A new implementation using the nonlinear Conjugate Gradient one is investigated also. Regarding the Newton approach, an exact computation of the Hessian of the considered cost function is provided. The proposed approaches and the considered implementations inherit the global plane search of the initial RobustICA method for which a better convergence speed for a given direction is still guaranteed. Numerical results on Magnetic Resonance Spectroscopy (MRS) source separation show the efficiency of the proposed approaches notably the quasi-Newton one using the BFGS method.

Feasibility of blind source separation methods for the denoising of dense-array EEG.

High-density electroencephalographic recordings have recently been proved to bring useful information during the pre-surgical evaluation of patients suffering from drug-resistant epilepsy. However, these recordings can be particularly obscured by noise and artifacts. This paper focuses on the denoising of dense-array EEG data (e.g. 257 channels) contaminated with muscle artifacts. In this context, we compared the efficiency of several Independent Component Analysis (ICA) methods, namely SOBI, SOBIrob, PICA, InfoMax, two different implementations of FastICA, COM2, ERICA, and SIMBEC, as well as that of Canonical Correlation Analysis (CCA). We evaluated the performance using the Normalized Mean Square Error (NMSE) criterion and calculated the numerical complexity. Quantitative results obtained on realistic simulated data show that some of the ICA methods as well as CCA can properly remove muscular artifacts from dense-array EEG.

Localization of spatially distributed brain sources after a tensor-based preprocessing of interictal epileptic EEG data.

This paper addresses the localization of spatially distributed sources from interictal epileptic electroencephalographic data after a tensor-based preprocessing. Justifying the Canonical Polyadic (CP) model of the space-time-frequency and space-time-wave-vector tensors is not an easy task when two or more extended sources have to be localized. On the other hand, the occurrence of several amplitude modulated spikes originating from the same epileptic region can be used to build a space-time-spike tensor from the EEG data. While the CP model of this tensor appears more justified, the exact computation of its loading matrices can be limited by the presence of highly correlated sources or/and a strong background noise. An efficient extended source localization scheme after the tensor-based preprocessing has then to be set up. Different strategies are thus investigated and compared on realistic simulated data: the "disk algorithm" using a precomputed dictionary of circular patches, a standardized Tikhonov regularization and a fused LASSO scheme.

Hippocampal effective synchronization values are not pre-seizure indicator without considering the state of the onset channels.

In this paper, a model-based approach is presented to quantify the effective synchrony between hippocampal areas from depth-EEG signals. This approach is based on the parameter identification procedure of a realistic Multi-Source/Multi-Channel (MSMC) hippocampal model that simulates the function of different areas of hippocampus. In the model it is supposed that the observed signals recorded using intracranial electrodes are generated by some hidden neuronal sources, according to some parameters. An algorithm is proposed to extract the intrinsic (solely relative to one hippocampal area) and extrinsic (coupling coefficients between two areas) model parameters, simultaneously, by a Maximum Likelihood (ML) method. Coupling coefficients are considered as the measure of effective synchronization. This work can be considered as an application of Dynamic Causal Modeling (DCM) that enables us to understand effective synchronization changes during transition from inter-ictal to pre -ictal state. The algorithm is first validated by using some synthetic datasets. Then by extracting the coupling coefficients of real depth-EEG signals by the proposed approach, it is observed that the coupling values show no significant difference between ictal, pre-ictal and inter-ictal states, i.e. either the increase or decrease of coupling coefficients has been observed in all states. However, taking the value of intrinsic parameters into account, pre-seizure state can be distinguished from inter-ictal state. It is claimed that seizures start to appear when there are seizure-related physiological parameters on the onset channel, and its coupling coefficient toward other channels increases simultaneously. As a result of considering both intrinsic and extrinsic parameters as the feature vector, inter-ictal, pre-ictal and ictal activities are discriminated from each other with an accuracy of 91.33% accuracy.

Optimal algorithm switching for the estimation of systole period from cardiac microacceleration signals (SonR).

Previous studies have shown that cardiac microacceleration signals, recorded either cutaneously, or embedded into the tip of an endocardial pacing lead, provide meaningful information to characterize the cardiac mechanical function. This information may be useful to personalize and optimize the cardiac resynchronization therapy, delivered by a biventricular pacemaker, for patients suffering from chronic heart failure (HF). This paper focuses on the improvement of a previously proposed method for the estimation of the systole period from a signal acquired with a cardiac microaccelerometer (SonR sensor, Sorin CRM SAS, France). We propose an optimal algorithm switching approach, to dynamically select the best configuration of the estimation method, as a function of different control variables, such as the signal-to-noise ratio or heart rate. This method was evaluated on a database containing recordings from 31 patients suffering from chronic HF and implanted with a biventricular pacemaker, for which various cardiac pacing configurations were tested. Ultrasound measurements of the systole period were used as a reference and the improved method was compared with the original estimator. A reduction of 11% on the absolute estimation error was obtained for the systole period with the proposed algorithm switching approach.

[Optical spectroscopy: a new approach to assess urological tumors]. / La spectroscopie optique : une nouvelle approche pour l'étude des tumeurs urologiques.

INTRODUCTION: Optical spectroscopy refers to a group of novel technologies that uses interaction of light with tissues to analyze their structure and chemical composition. The objective of this article is to describe these technologies and detail their potential for assessing urological tumors. MATERIAL AND METHODS: It has been shown that optical spectroscopy can accurately analyse multiple solid tumors. Several publications specifically aimed at assessing prostate cancers, renal carcinomas and urothelial tumors. RESULTS: There are three types of spectroscopy that all use light focussed on a tissue and thereafter collect a specific reflected optical signal. Optical spectroscopy can differentiate benign (adenoma or inflammation) and malignant (adenocarcinoma) prostatic tissues. It can also distinguish normal bladder tissue from inflammatory or cancerous cells. Regarding renal tumors, spectroscopy can identify normal and tumoral tissue and differentiate benign and malignant tumors. Its diagnostic accuracy is about 85%. However, reported studies only concentrate on in vitro or ex vivo specimen and the numbers of patients are quite small. CONCLUSION: Optical spectroscopy can be envisioned as an "optical biopsy" tool. Potential applications in the clinical field are promising. Larger studies on in vivo specimen need to be undertaken to confirm phase I preliminary reports.

Variation of bispectral index under TIVA with propofol in a paediatric population.

BACKGROUND: In this prospective observational study, we aim to explore the relationship between age and bispectral index (BIS) values at different plasma concentrations of propofol. METHODS: Fifty children aged from 3 to 15 yr were included. Anaesthesia was induced using a target-controlled infusion of propofol with the Kataria pharmacokinetic model together with a bolus of remifentanil followed by a continuous infusion rate at 0.2 microg kg(-1) min(-1). Target plasma propofol concentration was initially stabilized to 6 microg ml(-1) and continued for 6 min. The target was then decreased and stabilized to 4 microg ml(-1) and then to 2 microg ml(-1). BIS values, plasma propofol concentration, and EEG were continuously recorded. In order to explore the relationship between variations in propofol concentration and the EEG bispectrum, we used a multiple correspondence analysis (MCA). Results are shown in median (range). RESULTS: We found no statistical difference between BIS values with propofol 6 microg ml(-1) [23 (12-40)] and 4 microg ml(-1) [28 (9-67)]. At 2 microg ml(-1), BIS was significantly different [52 (24-71)], but a significant correlation between the age of children and BIS values was found (r2=0.66; P<0.01). There was little change in children's position between 6 and 4 microg ml(-1) in the structure model of the MCA. From 4 to 2 microg ml(-1), the position of children moved only on axis 2. CONCLUSIONS: These results showed the difficulty to interpret BIS values because of the absence of significant change for higher plasma propofol concentration variation or because of the link with age for the lower plasma concentration.

Brain activity modeling in general anesthesia: enhancing local mean-field models using a slow adaptive firing rate.

In this paper, an enhanced local mean-field model that is suitable for simulating the electroencephalogram (EEG) in different depths of anesthesia is presented. The main building elements of the model (e.g., excitatory and inhibitory populations) are taken from Steyn-Ross [M. L. Steyn-Ross, Phys. Rev. E 64, 011917 (2001), D. A. Steyn-Ross, Phys. Rev. E 64, 011918 (2001)] and Bojak and Liley [I. Bojak and D. T. Liley, Phys. Rev. E 71, 041902 (2005)] mean-field models and a new slow ionic mechanism is included in the main model. Generally, in mean-field models, some sigmoid-shape functions determine firing rates of neural populations according to their mean membrane potentials. In the enhanced model, the sigmoid function corresponding to excitatory population is redefined to be also a function of the slow ionic mechanism. This modification adapts the firing rate of neural populations to slow ionic activities of the brain. When an anesthetic drug is administered, the slow mechanism may induce neural cells to alternate between two levels of activity referred to as up and down states. Basically, the frequency of up-down switching is in the delta band (0-4 Hz) and this is the main reason behind high amplitude, low frequency fluctuations of EEG signals in anesthesia. Our analyses show that the enhanced model may have different working states driven by anesthetic drug concentration. The model is settled in the up state in the waking period, it may switch to up and down states in moderate anesthesia while in deep anesthesia it remains in the down state.

Comparison of three spike detectors dedicated to single unit action potentials of the auditory nerve.

This paper compares three methods for the detection of single unit action potentials in auditory nerve. The detector structures are similar consisting of a filtering procedure in the first stage and a decision rule in the second stage. The detection accuracy of each detector is characterized by the couple probability of a true detection vs. rates of false detection with synthetic data. The performance comparison between detectors shows that the detector using a band-pass finite-impulse-response filter with complex coefficients offers the best performance. This observation was especially evident for low signal to noise ratios. This finding is confirmed with real data and leads us to revise the protocol of spike detection in auditory nerve.

The impact of age on bispectral index values and EEG bispectrum during anaesthesia with desflurane and halothane in children.

BACKGROUND: The relationship between end-tidal sevoflurane concentration, bispectral index (BIS) and the EEG bispectrum in children appears to be age dependent. The aim of this study was to quantify the BIS values at 1 MAC (minimum alveolar concentration) for desflurane and halothane, and explore the relationship with age for these anaesthetic agents in children. METHODS: ECG, EEG and BIS were recorded continuously in 90 children aged 6-170 months requiring anaesthesia for elective surgery. Fifty children were anaesthetized with desflurane, and 40 children with halothane. Recordings were performed through to a steady state of 2 MAC, and thereafter at 1 and 0.5 MAC, respectively. The bispectrum of the EEG was estimated using MATLAB(c) software. A multiple correspondence analysis (MCA) was used. RESULTS: At a steady state of 1 MAC, BIS values were significantly higher with halothane 62 (43-80) than desflurane 34 (18-64). BIS values were significantly correlated with age in both groups: DES (r(2)=0.57; P<0.01) and HALO (r(2)=0.48; P<0.01). Changes in position in the structured model of the MCA (dependent on the pattern of the EEG bispectrum) were different for the two volatile anaesthetic agents. CONCLUSIONS: In children, BIS values are linked to age irrespective of the volatile anaesthetic agent used. The difference in BIS values for different agents at the same MAC can be explained by the specific effect on the EEG bispectrum induced by each anaesthetic agent, bringing into question the ability of the EEG bispectrum to accurately determine the depth of anaesthesia.

Effect of remifentanil with and without atropine on heart rate variability and RR interval in children.

Remifentanil can cause bradycardia either by parasympathetic activation or by other negative chronotropic effects. The high frequency (HF) component of heart rate variability (HRV) is a marker of parasympathetic activity. This study aimed to evaluate the effect of remifentanil on RR interval and on HRV in children. Forty children ASA I or II were studied after approval by the human studies committee and informed parental consent was obtained. After stabilisation at sevoflurane 1 MAC, they were randomly divided into two groups: one received a 20 microg.kg(-1) atropine injection (AT + REMI) and the other ringer lactate solution (REMI). Three minutes later, a 1 microg.kg(-1) bolus of remifentanil was administered over 1 min, followed by a continual infusion at 0.25 microg.kg(-1).min(-1) for 10 min increased to 0.5 microg.kg(-1).min(-1) for a further 10 min. A time varying, autoregressive analysis of RR sequences was used to estimate classical spectral parameters: low (0.04-0.15 Hz; LF) and high (0.15-0.45 Hz; HF) frequency, whereas the root mean square of successive differences of RR intervals (rmssd) was derived directly from the temporal sequence. Statistical analyses were conducted by means of the multiple correspondence analysis and with non parametrical tests. Remifentanil induced an RR interval lengthening, i.e. bradycardia, in both groups compared to pretreatment values and was associated with an increase of HF and rmssd only for the REMI group. The parasympathetic inhibition by atropine did not totally prevent remifentanil's negative chronotropic effect. A direct negative chronotropic effect of remifentanil is proposed.

Impact of age on both BIS values and EEG bispectrum during anaesthesia with sevoflurane in children.

BACKGROUND: The aim of this study was to evaluate the potential relationship between age, BIS (Aspect), and the EEG bispectrum during anaesthesia with sevoflurane. METHODS: BIS and raw EEG were recorded at a steady state of 1 MAC in 100 children, and during a decrease from 2 to 0.5 MAC in a sub-group of 29 children. The bispectrum of the EEG was estimated using MATLAB software. For analysis, the bispectrum was divided into 36 frequencies of coupling (P(i))--the MatBis. A multiple correspondence analysis (MCA) was used to establish an underlying structure of the pattern of each individual's MatBis at 1 MAC. Clustering of children into homogeneous groups was conducted by a hierarchical ascending classification (HAC). The level of statistical significance was set at 0.05. RESULTS: At 1 MAC, the BIS values for all children ranged from 20 to 74 (median 40). Projection of both age and BIS value recorded at 1 MAC onto the structured model of the MCA showed them to be distributed along the same axis, demonstrating that the different values of BIS obtained in younger or older children are mainly dependent on their MatBis. At 1 MAC, six homogeneous groups of children were obtained through the HAC. Groups 5 (30 months; range 23-49) and 6 (18 months; range 6-180) were the younger children and Group 1 (97 months; range 46-162) the older. Groups 5 and 6 had the highest median values of BIS (54; range 50-59) (55; range 26-74) and Group 1 the lowest values (29; range 22-37). CONCLUSION: The EEG bispectrum, as well as the BIS appeared to be strongly related to the age of children at 1 MAC sevoflurane.

Haemodynamic effects of remifentanil in children with and without intravenous atropine. An echocardiographic study.

BACKGROUND: Remifentanil is known to cause bradycardia and hypotension. We aimed to characterize the haemodynamic profile of remifentanil during sevoflurane anaesthesia in children with or without atropine. METHODS: Forty children who required elective surgery received inhalational induction of anaesthesia using 8% sevoflurane. They were allocated randomly to receive either atropine, 20 microg kg(-1) (atropine group) or Ringer's lactate (control group) after 10 min of steady-state 1 MAC sevoflurane anaesthesia (baseline). Three minutes later (T0), all children received remifentanil 1 microg kg(-1) injected over a 60 s period, followed by an infusion of 0.25 microg kg(-1) min(-1) for 10 min then 0.5 microg kg(-1) min(-1) for 10 min. Haemodynamic variables and echocardiographic data were determined at baseline, T0, T5, T10, T15 and T20 min. RESULTS: Remifentanil caused a significant decrease in heart rate compared with the T0 value, which was greater at T20 than T10 in the two groups: however, the values at T10 and T20 were not significantly different from baseline in the atropine group. In comparison with T0, there was a significant fall in blood pressure in the two groups. Remifentanil caused a significant decrease in the cardiac index with or without atropine. Remifentanil did not cause variation in stroke volume (SV). In both groups, a significant increase in systemic vascular resistance occurred after administration of remifentanil. Contractility decreased significantly in the two groups, but this decrease remained moderate (between -2 and +2 sd). CONCLUSION: Remifentanil produced a fall in blood pressure and cardiac index, mainly as a result of a fall in heart rate. Although atropine was able to reduce the fall in heart rate, it did not completely prevent the reduction in cardiac index.

Comparison of two estimators of time-frequency interdependencies between nonstationary signals: application to epileptic EEG.

Numerous works have been dedicated to the development of signal processing methods aimed at measuring the degree of association between EEG signals. This interdependency parameter is often used to characterize the functional coupling between different brain structures or regions during either normal or pathological processes. In this paper we focus on the time-frequency characterization of interdependencies between nonstationary signals. Particularly, we propose a novel estimator based on the cross correlation of narrow band filtered signals. In a simulation framework, results show that this estimator may exhibit higher statistical performances (bias and variance) compared to a more classical estimator based on the coherence function. On real data (intracerebral EEG signals), they show that this estimator enhances the readability of the time-frequency representation of the relationship and can thus improve the interpretation of nonstationary interdependencies in EEG signals. Finally, we illustrate the importance of characterizing the relationship in both time and frequency domains by comparing with frequency-independent methods (linear and nonlinear).

Estimation of pulmonary arterial pressure by a neural network analysis using features based on time-frequency representations of the second heart sound.

The objective of the study was to develop a non-invasive method for the estimation of pulmonary arterial pressure (PAP) using a neural network (NN) and features extracted from the second heart sound (S2). To obtain the information required to train and test the NN, an animal model of pulmonary hypertension (PHT) was developed, and nine pigs were investigated. During the experiments, the electrocardiogram, phonocardiogram and PAP were recorded. Subsequently, between 15 and 50 S2 heart sounds were isolated for each PAP stage and for each animal studied. A Coiflet wavelet decomposition and a pseudo smoothed Wigner-Ville distribution were used to extract features from the S2 sounds and train a one-hidden-layer NN using two-thirds of the data. The NN performance was tested on the remaining one-third of the data. NN estimates of the systolic and mean PAPs were obtained for each S2 and then ensemble averaged over the 15-50 S2 sounds selected for each PAP stage. The standard errors between the mean and systolic PAPs estimated by the NN and those measured with a catheter were 6.0 mmHg and 8.4 mmHg, respectively, and the correlation coefficients were 0.89 and 0.86, respectively. The classification accuracy, using 23 mmHg mean PAP and 30 mmHg systolic PAP thresholds between normal PAP and PHT, was 97% and 91%, respectively.

Water modeled signal removal and data quantification in localized MR spectroscopy using a time-scale postacquistion method.

We have previously shown the continuous wavelet transform (CWT), a signal-processing tool, which is based upon an iterative algorithm using a lorentzian signal model, to be useful as a postacquisition water suppression technique. To further exploit this tool we show its usefulness in accurately quantifying the signal metabolites after water removal. However, due to the static field inhomogeneities, eddy currents, and "radiation damping," the water signal and the metabolites may no longer have a lorentzian lineshape. Therefore, another signal model must be used. As the CWT is a flexible method, we have developed a new algorithm using a gaussian model and found that it fits the signal components, especially the water resonance, better than the lorentzian model in most cases. A new framework, which uses the two models, is proposed. The framework iteratively extracts each resonance, starting by the water peak, from the raw signal and adjusts its envelope to both the lorentzian and the gaussian models. The model giving the best fit is selected. As a consequence, the small signals originating from metabolites when selecting, removing, and quantifying the dominant water resonance from the raw time domain signal are preserved and an accurate estimation of their concentrations is obtained. This is demonstrated by analyzing (1H) magnetic resonance spectroscopy unsuppressed water data collected from a phantom with known concentrations at two different field strengths and data collected from normal volunteers using two different localization methods.

Lactate editing and lipid suppression by continuous wavelet transform analysis: application to simulated and (1)H MRS brain tumor time-domain data.

Determination of lactate concentrations in vivo is required in the noninvasive diagnosis, staging, and therapeutic monitoring of diseases such as cancer, heart disease, and stroke. An iterative filtering process based on the continuous wavelet transform (CWT) method in the time domain is proposed to isolate the lactate doublet signal from overlapping lipid resonances and estimate the magnetic resonance spectroscopy (MRS) parameters of the lactate methyl signal (signal amplitude, chemical shift, J-coupling and apparent transverse relaxation time (T*(2))). This method offers a number of advantages over the multiple quantum (MQ) and difference spectroscopy approaches, including: 1) full recovery of the lactate methyl signal, whereas the MQ methods usually detect 50% of the signal intensity; 2) in contrast to MQ methods, the lipid signal is retained together with J-coupling data on the lactate peak; 3) the CWT method is much less sensitive to motion artifacts than difference spectroscopy. Application of the method to simulated and real (1)H MRS data collected from human blood plasma and brain tumors demonstrated that this filter provides accurate estimates of the MRS parameters of the lactate doublet and efficiently removes lipid contributions.

Time-domain quantification of multiple-quantum-filtered (23)Na signal using continuous wavelet transform analysis.

The application of continuous wavelet transform (CWT) analysis technique is presented to analyze multiple-quantum-filtered (MQF) (23)Na magnetic resonance spectroscopy (MRS) data. CWT acts on the free-induction-decay (FID) signal as a time-frequency variable filter. The signal-to-noise ratio (SNR) and frequency resolution of the output filter are locally increased. As a result, MQF equilibrium longitudinal magnetization and the apparent fast and slow transverse relaxation times are accurately estimated. A developed iterative algorithm based on frequency signal detection and components extraction, already proposed, was used to estimate the values of the signal parameters by analyzing simulated time-domain MQF signals and data from an agarose gel. The results obtained were compared to those obtained by measurement of signal height in frequency domain as a function of MQF preparation time and those obtained by a simple time-domain curve fitting. The comparison indicates that the CWT approach provides better results than the other tested methods that are generally used for MQF (23)Na MRS data analysis, especially when the SNR is low. The mean error on the estimated values of the amplitude signal and the apparent fast and slow transverse relaxation times for the simulated data were 2.19, 6. 63, and 16.17% for CWT, signal height in frequency domain, and time-domain curve fitting methods, respectively. Another major advantage of the proposed technique is that it allows quantification of MQF (23)Na signal from a single FID and, thus, reduces the experiment time dramatically.

Pediatric anesthesia monitoring with the help of EEG and ECG.

This paper presents research regarding the monitoring of the brain and the adequacy of anesthesia during surgery. Particular variables are derived from EEG and ECG signals and are correlated to anesthetic gas (sevoflurane) concentration, in pediatric anesthesia. The methods used for parameter extraction are based on change detection theory and time-frequency representation. Preliminary results show that the expired anesthetic gas concentration modulates both the heart rate variability and the duration of the burst suppression. Monitors of the central nervous system and autonomic nervous system activities can be expected to be based on these variables.