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Biomedical subjects

L Senhadji

Publications and source records attributed to L Senhadji.

14 recordsLinked to original sources
PubMed2006

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.

Adolescent↗
PubMed2005

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.

Adolescent↗
PubMed2005

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.

Adolescent↗
PubMed2004

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.

Adjuvants, Anesthesia↗
PubMed2002

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.

Animals↗
PubMed2001

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.

Artifacts↗
PubMed2000

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.

Adult↗
PubMed2000

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.

Magnetic Resonance Spectroscopy↗
PubMed2000

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.

Anesthesia, General↗
PubMed1998

Analysis-synthesis of the phonocardiogram based on the matching pursuit method.

The matching pursuit method of Mallat and Zhang is applied to the analysis and synthesis of phonocardiograms (PCG's). The method is based on a classical Gabor wavelet or time-frequency atom which is the product of a sinusoid and a Gaussian window function. It decomposes a signal into a series of time-frequency atoms by an iterative process based on selecting the largest inner product of the signal (and the subsequent residues) with atoms from a redundant dictionary. The Gaussian window controls the envelope duration and time position of each atom; and the sinusoid represents the frequency. The method was applied to two sets of PCG's: one with very low-noise level and the other with 10% noise energy. Each data base includes 11 PCG's representing the normal and the pathological conditions of the heart. The normalized root-mean-square error (NRMSE) was computed between the original and the reconstructed signals. The results show that the matching pursuit method is very suitable to the transient and complex properties of the PCG's, as it yielded excellent NRMSE's around 2.2% for the two sets of 11 PCG's tested.

Heart Murmurs↗
PubMed1998

Time-frequency scaling transformation of the phonocardiogram based of the matching pursuit method.

A time-frequency scaling transformation based on the matching pursuit (MP) method is developed for the phonocardiogram (PCG). The MP method decomposes a signal into a series of time-frequency atoms by using an iterative process. The modification of the time scale of the PCG can be performed without perceptible change in its spectral characteristics. It is also possible to modify the frequency scale without changing the temporal properties. The technique has been tested on 11 PCG's containing heart sounds and different murmurs. A scaling/inverse-scaling procedure was used for quantitative evaluation of the scaling performance. Both the spectrogram and a MP-based Wigner distribution were used for visual comparison in the time-frequency domain. The results showed that the technique is suitable and effective for the time-frequency scale transformation of both the transient property of the heart sounds and the more complex random property of the murmurs. It is also shown that the effectiveness of the method is strongly related to the optimization of the parameters used for the decomposition of the signals.

Fourier Analysis↗
PubMed1997

Time-domain quanification of amplitude, chemical shift, apparent relaxation time T2, and phase by wavelet-transform analysis. Application to biomedical magnetic resonance spectroscopy.

The wavelet-transform method is used to quantify the magnetic resonance spectroscopy (MRS) parameters: chemical shift, apparent relaxation time T2, resonance amplitude, and phase. Wavelet transformation is a time-frequency representation which separates each component from the FID, then successively quantifies it and subtracts it from the raw signal. Two iterative procedures have been developed. They have been combined with a nonlinear regression analysis method and tested on both simulated and real sets of biomedical MRS data selected with respect to the main problems usually encountered in quantifying biomedical MRS, specifically "chemical noise," resulting from overlapping resonances, and baseline distortion. The results indicate that the wavelet-transform method can provide efficient and accurate quantification of MRS data.

Algorithms↗
PubMedDate not supplied

Wavelet analysis of EEG for three-dimensional mapping of epileptic events.

This paper is aimed at understanding epileptic patient disorders through the analysis of surface electroencephalograms (EEG). It deals with the detection of spikes or spike-waves based on a nonorthogonal wavelet transform. A multilevel structure is described that locates the temporal segments where abnormal events occur. These events are then visually interpreted by means of a 3D mapping technique. This 3D display makes use of a ray tracing scheme and combines both the functional (the EEG but also its wavelet representation) and the morphological data (acquired from computed tomography [CT] or magnetic resonance imaging [MRI] devices). The results show that a significant reduction of the clinical workload is obtained while the most important episodes are better reviewed and analyzed.

Algorithms↗