1,721,003 research outputs found
Bifurcation analysis of a physiological model of the baroreceptive control
No full textThe observation of biological systems suggests the hypothesis that nonlinear mechanisms could be involved in the control of their functions. The analysis of cardiovascular system, starting from the measurement of its state variables, seems to confirm the nonlinear nature of the control mechanisms and the presence of fractal structures in those signals. The goal of this study is to verify if a physiological control system is able to generate complex and also chaotic dynamics when periodically forced by a sinusoidal input at different frequencies. The paper analyzes a simple physiological model which accounts for the oscillations in the arterial blood pressure signal generated by the action of the baroreceptive control. The model was proposed by Kitney in 1979 and it considers the effect of the respiration signal like an external periodically forcing term. Using this model, a variety of nonlinear behaviors like the frequency entrainment, the phase locking and the frequency shift can be reproduced in different experimental situations. A study of the dynamics of the baroreceptive model through a structural stability analysis is proposed. The bifurcation diagrams classifies the different dynamical behavior of the model for different values of respiratory frequency and gain of baroreceptive system parameters. Other model parameters are fixed at realistic values. The large number of bifurcations of different types indicate that the dynamics of the model can be very complex. In fact, for values of parameters in physiological range, multiplicity of attractors, subharmonics of various periods, period doublings, quasiperiodic solutions and strange attractors get up. Results are in agreement with the hypothesis that a nonlinear dynamic model underlines the variability control
Nonlinearity parameters for the classification of high risk Myocardial Infarction subjects
No full textThe paper presents the analysis of the Heart Rate Variability (HRV) signal in 19 subjects who recently had a Myocardial Infarction episode (MI). The study follows a nonlinear approach based on the multiparametric analysis of some invariant properties of the dynamical system generating the time series. First we reconstruct the system embedding space from the HRV time series. The False Nearest Neighbors (FNN) criterion provides the real embedding dimension value. Results show that through the FNN method it is possible to identify the correct number of LE in the system. Parameter values significantly separate subjects who after MI keep a good performance of the cardiac pump (normal ventricular ejection function, NEF) vs. the group which after MI shows a reduced ventricular ejection fraction (REF)
A deep learning mixed-data type approach for the classification of FHR signals
No full textThe Cardiotocography (CTG) is a widely diffused monitoring practice, used in Ob-Gyn Clinic to assess the fetal well-being through the analysis of the Fetal Heart Rate (FHR) and the Uterine contraction signals. Due to the complex dynamics regulating the Fetal Heart Rate, a reliable visual interpretation of the signal is almost impossible and results in significant subjective inter and intra-observer variability. Also, the introduction of few parameters obtained from computer analysis did not solve the problem of a robust antenatal diagnosis. Hence, during the last decade, computer aided diagnosis systems, based on artificial intelligence (AI) machine learning techniques have been developed to assist medical decisions. The present work proposes a hybrid approach based on a neural architecture that receives heterogeneous data in input (a set of quantitative parameters and images) for classifying healthy and pathological fetuses. The quantitative regressors, which are known to represent different aspects of the correct development of the fetus, and thus are related to the fetal healthy status, are combined with features implicitly extracted from various representations of the FHR signal (images), in order to improve the classification performance. This is achieved by setting a neural model with two connected branches, consisting respectively of a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN). The neural architecture was trained on a huge and balanced set of clinical data (14.000 CTG tracings, 7000 healthy and 7000 pathological) recorded during ambulatory non stress tests at the University Hospital Federico II, Napoli, Italy. After hyperparameters tuning and training, the neural network proposed has reached an overall accuracy of 80.1%, which is a promising result, as it has been obtained on a huge dataset
Advanced signal processing techniques for CTG analysis
No full textThe paper aims at presenting and discussing some key points about the analysis of fetal heart rate (FHR) recorded by means of CardioTocographv (CTG). Starting from a brief history of CTG computerized analysis, the paper describes how the integration of various computational methods for extracting reliable parameters from FHR variability can help the pre natal diagnosis. The approaches adopted for the analysis are briefly illustrated, considering both traditional time domain parameters as well as new indices in the nonlinear field such as entropy measures, complexity parameters and indices derived from phase rectified signal averaging method. IUGR fetuses can be separated from Normal ones by parameters with high levels of significance. Moreover, collecting few of them allow obtaining classification models able to provide correct classification for more than 90% fetuses. Results obtained from Normal and IUGR populations of fetuses show that i) the integration of linear and nonlinear parameters provide reliable indications about pathophysiologic fetal states; ii) could support early clinical diagnosis of fetal pathologies; iii) should be considered to design novel fetal monitoring systems
Statistical long-term correlations in dissociated cortical neuron recordings.
No full textThe study of nonlinear long-term correlations in neuronal signals is a central topic for advanced neural signal processing. In particular, the existence of long-term correlations in neural signals recorded via multielectrode array (MEA) could provide interesting information about changes in interneuron communications. In this study we propose a new method for
long-term correlation analysis of neuronal burst activity based
on the periodogram slope estimation of the MEA signal. We
applied our method to recordings taken from cultured networks of
dissociated rat cortical neurons. We show the effectiveness of the method in analyzing the activity changes as well as the temporal dynamics that take place during the development of such cultures. Results demonstrate that the parameter is able to divide the network development in three well-defined stages, showing pronounced variations in the long-term correlation among bursts
Linear and Nonlinear Parameters for the Analysis of Fetal Heart Rate Signal from Cardiotocographic Recordings
No full textAntepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and simple tool for checking fetal status. Its introduction in the clinical routine limited the occurrence of fetal problems leading to a reduction of the precocious child mortality. Nevertheless, very poor indications on fetal pathologies can be inferred from the even automatic CTG analysis methods, which are actually employed. The feeling is that fetal heart rate (FHR) signals and uterine contractions carry much more information on fetal state than is usually extracted by classical analysis methods. In particular, FHR signal contains indications about the neural development of the fetus. However, the methods actually adopted for judging a CTG trace as "abnormal" give weak predictive indications about fetal dangers. We propose a new methodological approach for the CTG monitoring, based on a multiparametric FHR analysis, which includes spectral parameters from autoregressive models and nonlinear algorithms (approximate entropy). This preliminary study considers 14 normal fetuses, eight cases of gestational (maternal) diabetes, and 13 intrauterine growth retarded fetuses. A comparison with the traditional time domain analysis is also included. This paper shows that the proposed new parameters are able to separate normal from pathological fetuses. Results constitute the first step for realizing a new clinical classification system for the early diagnosis of most common fetal pathologies
Innovative technologies and signal processing in perinatal medicine: Volume 1
No full textPregnancy is a critical time for the health of the mother and the fetus, with important potential risks for both. Tools for antenatal diagnosis and pregnancy monitoring can support prevention and management of potential risks and complications. In particular, the perinatal period, spanning from the third trimester of pregnancy up to one month after birth, is the most critical for the baby. For this reason, in the last decades, biomedical engineering supported and fostered the scientific research towards the identification of new models, parameters, algorithms, and tools that can improve the quality of fetal monitoring, predict the outcomes and allow physicians to intervene in an appropriate manner to ensure a healthy future for the baby. This book follows the First International Summer School on Technologies and Signal Processing in Perinatal Medicine and reflects some of its most important master lectures. It represents a valuable guide for students and young researchers approaching this topic for the first time, as well as experienced researchers and practitioners looking for a clear representation of the themes and techniques presented by recognized experts in the field
Fetal Heart Rate Variability Due to Vibroacoustic Stimulation: Linear and Nonlinear Contribution
No full textObjectives: This work aims at characterizing the variation of fetal heart rate (FHR) provoked by vibroacoustic stimulation (VAS). The FHR signal is analyzed by means of a multiparametric approach consisting of linear and nonlinear indices. Methods: The FHR signals of 13 fetuses were collected through a US standard CTG monitor (HP1351A) and were sampled at a frequency of 2 Hz. The VAS was provided after a period of quiet of 10 minutes. The analysis was performed on the quiet period and on two successive time windows of 10 minutes each, after the stimulation. FHR classical parameters (delta, short term variability, long term irregularity; accelerations and decelerations) as well as power spectral density (PSD) and approximate entropy (ApEn) were computed for each period. Results: Results confirm that there is a significant change in fetal conditions after the stimulus is applied. This change can be clearly observed either in time domain parameters and in the regularity index (ApEn). Individual data are all consistent with an increase of variability and a decrease of regularity after VAS. Conclusions: The obtained results give further strength to the hypothesis that vibratory stimuli tests represent a reliable method for monitoring the neural development of the fetus during pregnancy
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