Hierarchical and Partitional Cluster Analysis of Glucose and Insulin Data from the Oral Glucose Tolerance Test

The body's ability to regulate glucose homeostasis is commonly assessed through the oral glucose tolerance test (OGTT). Several variations of OGTT exists, but the most used in clinical practice is the 2-sample 2-hour OGTT, in which glucose is measured in fasting and two hours after a glucose load. In the 5-sample 2-hour OGTT, glucose is measured in fasting and every 30 minutes after a glucose load, during two hours. In these tests, besides glucose, insulin level can also be measured from the blood samples, increasing thus the number of variables to analyze and perform a better metabolic assessment. In this paper, a cluster analysis is carried using the levels of glucose and insulin from the 2-sample 2-hour OGTT and from the 5-sample 2-hour OGTT, from subjects with metabolic syndrome and professional marathon runners. Different configurations of k-means and agglomerative hierarchical clustering were used to perform the clustering of data and analyze the relationships between clusters with the study groups. Results show that the k-means clustering algorithm performs better than the agglomerative hierarchical clustering, and, with the Manhattan distance measure, k-means perfectly groups subjects using the ten variables from the 5-sample 2-hour OGTT

On-line apnea-bradycardia detection using hidden semi-Markov models.

International audienceIn this work, we propose a detection method that exploits not only the instantaneous values, but also the intrinsic dynamics of the RR series, for the detection of apnea-bradycardia episodes in preterm infants. A hidden semi-Markov model is proposed to represent and characterize the temporal evolution of observed RR series and different pre-processing methods of these series are investigated. This approach is quantitatively evaluated through synthetic and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU our best detector shows an improvement of around 13% in sensitivity and 7% in specificity. Furthermore, a reduced detection delay of approximately 3 seconds is obtained with respect to conventional detectors

Une approche multivariée pour la détection d'épisodes d'apnée-bradycardie par modèles semi-Markoviens cachés.

International audienceIn this work, hidden Markov models (HMM) and hidden semi-Markov models (HSMM) are adapted for the classification, according to the maximum likelihood, of the dynamics of multivariate time series, obtained before and after apnée-bradycardie events in perterm infants. A phase of preprocessing of the observations, including signal quantization and the integration of delayed versions of each data source, is proposed. Results highlight the importance of considering the dynamics of the signals, show that HSMM are better adapted than HMM to our problem and emphasize that, with a suitable preprocessing, such as the quantification of observations and the introduction of an optimal delay between the observables, a significant gain in performance can be obtained

Détection multivariée des épisodes d'apnée-bradycardie chez le prématuré par modèles semi-markovien cachés

Cette thèse a comme domaine applicatif la détection précoce des événements d'apnée-bradycardie (AB) chez le prématuré. Après avoir situé l'importance sur le plan clinique de la détection des AB, une démarche méthodologique est proposée. Elle s'appuie sur un processus de fouille de données qui inclut le nettoyage et l'extraction de caractéristiques. Au chapitre 3, une méthode originale à base d'algorithmes évolutionnaires, pour optimiser des seuils et fenêtres d'analyse, est proposée pour adapter les algorithmes de traitement du signal ECG aux caractéristiques spécifiques du prématuré, très différentes de l'EGC de l'adulte. Au chapitre 4, une approche semi-Markovienne est adaptée pour la modélisation des dynamiques et plusieurs améliorations sont proposées : hétérogénéité des modèles, adaptation au traitement en ligne, optimisation de la gamme dynamique, extension de l'observabilité. Au chapitre 5, ces propositions sont exploitées dans des expériences de classification et de détection en ligne, tant sur signaux simulés que réels. Les résultats mettent bien en exergue l'intérêt de prendre en compte la dynamique des signaux. Ils soulignent également qu'avec un prétraitement approprié tel que la quantification des observations, l'introduction du retard entre les observables, un gain notable en performance peut être observé.This dissertation studies the early detection of apnea-bradycardia (AB) events in preterm infants. After defining the importance of AB detection from a clinical point of view, a methodological approach is proposed. It relies on a data mining process that includes data cleansing and feature extraction. In chapter 3, a novel method based on evolutionary algorithms, for optimizing the thresholds and the analysis windows, is proposed to adapt the algorithms of the ECG signal to the specific characteristics of preterm infants, very different from the EGC of adult. In chapter 4, a semi-Markovian approach is adapted for modeling of dynamics and several improvements are proposed : heterogeneous models, adaptation to online processing, optimization of experiments, are reported on simulated and read signals. They clearly highlight the importance of considering the dynamic of the signals. They also emphasize that with a suitable pre-treatment such as the quantification of observations and the introduction of delay between the observable, a significant gain in performance can be observed.RENNES1-BU Sciences Philo (352382102) / SudocSudocFranceF

Towards the Prediction of Mortality in Intensive Care Units Patients: A Simple Correspondence Analysis Approach

Abstract In

LF/(LF+HF) index in ventricular repolarization variability correlated and uncorrelated with heart rate variability.

International audienceThe purpose of this study, was to asses whether LF/(LF+HF) obtained from ventricular repolarization variability (VRV) reflects the state of sympathovagal balance. The VRV time series and heart rate variability (HRV) time series from seventy two electrocardiogram (ECG) records in four different autonomic nervous system (ANS) profiles (athletes, cardiac transplant patient, heart failure patients and normal subjects) were extracted. A dynamic linear parametric model was applied to separate the VRV in two parts, VRV correlated with HRV (VRV(r)) and VRV uncorrelated with HRV (VRV(u)). Spectral indices were obtained from HRV, VRV, VRV(u) and VRV(u) time series. Changes of these indicators from rest to tilt position were analyzed. Results showed that: i) only LF/(LF+HF) from HRV time series increases significantly from rest to tilt in all ANS profiles, this information could not be retrieved in the other three series (VRV, VRV (u) and VRV(u)) ii) LF/(LF+HF) index in HRV series are significantly different between normal subjects and heart failure patients, while cardiac transplant patients show a low coherence between HRV and VRV power spectra and iii) HF rhythm in VRV series seem to be related to the mechanical effect of respiration

Cardiac autonomic modulation in response to a glucose stimulus

This paper focuses on the effect of a sudden increase of plasma glucose concentration in the cardiac autonomic modulation using time-domain and frequency-domain heart rate variability (HRV) measures. Plasma glucose and insulin levels, measured each 30 min during an oral glucose tolerance test, and RR ¯ $\overline {\text {RR}}$ (mean of the RR interval), SDNN (standard deviation of normal-to-normal heartbeats), rMSSD (root-mean-square of successive differences between normal heartbeats), TP (total spectral power), LF and HF (power of the low- and high-frequency bands), LF norm and HF norm (LF and HF in normalized units), and LF/HF ratio of the HRV signal, obtained from 5-min-long ECG recordings during each phase of the test, were analyzed for subjects with the metabolic syndrome, marathon runners, and a control group. Results show that, after the glucose load, subjects with the metabolic syndrome experienced an increased sympathetic and decreased parasympathetic tone, which suggests an imbalance in cardiac autonomic modulation as a consequence of hyperglycemia and hyperinsulinemia. The significance of this study lies in the use of the ECG to assess the effects of a sudden increase in plasma glucose concentration on the cardiac autonomic modulation in subjects with different cardiovascular and metabolic conditions. Graphical Abstract Time-domain and frequency-domain heart rate variability measures are altered in subjects with different cardiovascular and metabolic conditions during an oral glucose tolerance testThis paper focuses on the effect of a sudden increase of plasma glucose concentration in the cardiac autonomic modulation using time-domain and frequency-domain heart rate variability (HRV) measures. Plasma glucose and insulin levels, measured each 30 min during an oral glucose tolerance test, and RR ¯ $\overline {\text {RR}}$ (mean of the RR interval), SDNN (standard deviation of normal-to-normal heartbeats), rMSSD (root-mean-square of successive differences between normal heartbeats), TP (total spectral power), LF and HF (power of the low- and high-frequency bands), LF norm and HF norm (LF and HF in normalized units), and LF/HF ratio of the HRV signal, obtained from 5-min-long ECG recordings during each phase of the test, were analyzed for subjects with the metabolic syndrome, marathon runners, and a control group. Results show that, after the glucose load, subjects with the metabolic syndrome experienced an increased sympathetic and decreased parasympathetic tone, which suggests an imbalance in cardiac autonomic modulation as a consequence of hyperglycemia and hyperinsulinemia. The significance of this study lies in the use of the ECG to assess the effects of a sudden increase in plasma glucose concentration on the cardiac autonomic modulation in subjects with different cardiovascular and metabolic conditions. Graphical Abstract Time-domain and frequency-domain heart rate variability measures are altered in subjects with different cardiovascular and metabolic conditions during an oral glucose tolerance tes

Analysis of the QRS complex for apnea-bradycardia characterization in preterm infants.

International audienceThis work presents an analysis of the information content of new features derived from the electrocardiogram (ECG) for the characterization of apnea-bradycardia events in preterm infants. Automatic beat detection and segmentation methods have been adapted to the ECG signals from preterm infants, through the application of two evolutionary algorithms. ECG data acquired from 32 preterm infants with persistent apnea-bradycardia have been used for quantitative evaluation. The adaptation procedure led to an improved sensitivity and positive predictive value, and a reduced jitter for the detection of the R-wave, QRS onset, QRS offset, and iso-electric level. Additionally, time series representing the RR interval, R-wave amplitude and QRS duration, were automatically extracted for periods at rest, before, during and after apnea-bradycardia episodes. Significant variations (p<0.05) were observed for all time-series when comparing the difference between values at rest versus values just before the bradycardia event, with the difference between values at rest versus values during the bradycardia event. These results reveal changes in the R-wave amplitude and QRS duration, appearing at the onset and termination of apnea-bradycardia episodes, which could be potentially useful for the early detection and characterization of these episodes

Multivariate ECG analysis for apnoea?bradycardia detection and characterisation in preterm infants.

International audienceAn analysis of the information content of new features derived from the electrocardiogram of preterm infants is presented. Beat detection and segmentation methods, specifically adapted to this population through evolutionary algorithms, led to an improved sensitivity and positive predictive value. RR interval, R-wave amplitude and QRS duration time-series were extracted at rest (T1), before (T2), during (T3) and after (T4) apnoea-bradycardia episodes. Significant variations for T1-T2 vs. T1-T3 and T1-T3 vs. T1-T4 were observed. Results reveal changes in the R-wave amplitude and QRS duration at the onset and termination of apnoea-bradycardia episodes which could be useful for their detection and characterisation