Noise-assisted estimation of attractor invariants

In this article, the noise-assisted correlation integral (NCI) is proposed. The purpose of the NCI is to estimate the invariants of a dynamical system, namely the correlation dimension (D), the correlation entropy (K2), and the noise level (σ). This correlation integral is induced by using random noise in a modified version of the correlation algorithm, i.e., the noise-assisted correlation algorithm. We demonstrate how the correlation integral by Grassberger et al. and the Gaussian kernel correlation integral (GCI) by Diks can be thought of as special cases of the NCI. A third particular case is the U-correlation integral proposed herein, from which we derived coarse-grained estimators of the correlation dimension (DmU), the correlation entropy (KmU), and the noise level (σmU). Using time series from the Henon map and the Mackey-Glass system, we analyze the behavior of these estimators under different noise conditions and data lengths. The results show that the estimators DmU and σmU behave in a similar manner to those based on the GCI. However, for the calculation of K2, the estimator KmU outperforms its GCI-based counterpart. On the basis of the behavior of these estimators, we have proposed an automatic algorithm to find D,K2, and σ from a given time series. The results show that by using this approach, we are able to achieve statistically reliable estimations of those invariants.Fil: Restrepo Rinckoar, Juan Felipe. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Entre Ríos. Universidad Nacional de Entre Ríos. Centro de Investigaciones y Transferencia de Entre Ríos; Argentin

Sleep-wake stages classification using heart rate signals from pulse oximetry

The most important index of obstructive sleep apnea/hypopnea syndrome (OSAHS) is the apnea/hyponea index (AHI). The AHI is the number of apnea/hypopnea events per hour of sleep. Algorithms for the screening of OSAHS from pulse oximetry estimate an approximation to AHI counting the desaturation events without consider the sleep stage of the patient. This paper presents an automatic system to determine if a patient is awake or asleep using heart rate (HR) signals provided by pulse oximetry. In this study, 70 features are estimated using entropy and complexity measures, frequency domain and time-scale domain methods, and classical statistics. The dimension of feature space is reduced from 70 to 40 using three different schemes based on forward feature selection with support vector machine and feature importance with random forest. The algorithms were designed, trained and tested with 5000 patients from the Sleep Heart Health Study database. In the test stage, 10-fold cross validation method was applied obtaining performances up to 85.2% accuracy, 88.3% specificity, 79.0% sensitivity, 67.0% positive predictive value, and 91.3% negative predictive value. The results are encouraging, showing the possibility of using HR signals obtained from the same oximeter to determine the sleep stage of the patient, and thus potentially improving the estimation of AHI based on only pulse oximetry.Fil: Casal, Ramiro. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; ArgentinaFil: Di Persia, Leandro Ezequiel. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; ArgentinaFil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; Argentin

Automatic Classification of Sustained Vowels Based on Signal Regularity Measures

En el año 1995 Ingo Titze propuso un sistema de clasificación de fonemas vocálicos en tres tipos (Tipo I,Tipo II y Tipo III) en base a la regularidad de la señal de voz cuasiperiódica correspondiente. En la práctica clı́nica fonoaudiológica, esta clasificación se realiza en base a la inspección visual de espectrogramas, no siendo claros los criterios que diferencian un tipo de voz de otra, especialmente entre los tipos I y II. En consecuencia, existe una granvariación interprofesional y una fuerte dependencia de la experiencia de cada especialista. Con el fin de lograr una clasificación objetiva basada en parámetros cuantitativos, se buscó extraer caracterı́sticas capaces de representar las diferencias fundamentales entre las voces de Tipos I y II, para luego clasificar una base de datos anotada. Se extrajeron parámetros acústicos clásicos, como medidas de Jitter y Shimmer, y harmonics to noise ratio (HNR) calculados utilizando PRAAT. También se propuso la utilización de la amplitud del primer ramónico (R1) y dos caracterı́sticas ideadas porlos autores de este trabajo: varianza normalizada de la primera componente principal (VNCP) y razones pico-valle (PV) espectrales. La clasificación se realizó mediante máquinas de soporte vectorial (SVM) de kernel lineal utilizando las caracterı́sticas que minimizan el error del clasificador. Como resultado, se obtuvo un error de validación cruzada de 11.61%, con porcentajes de acierto del 93.24% y 83.95%, para voces Tipo I y Tipo II respectivamente.Fil: Miramont, Juan Manuel. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; ArgentinaFil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; Argentin

Transfer entropy rate through Lempel-Ziv complexity

The transfer entropy and the transfer entropy rate are closely related concepts that measure information exchange between two dynamical systems. These measures allow us to study linear and nonlinear causality relations and can be estimated through the use of different methodologies. However, some of them assume a data model and/or are computationally expensive. This article depicts a methodology to estimate the transfer entropy rate between two systems through the Lempel-Ziv complexity. This methodology offers a set of advantages: It estimates the transfer entropy rate from two single discrete series of measures, it is not computationally expensive, and it does not assume any data model. The simulation results over three different unidirectional coupled dynamical systems suggest that this methodology can be used to assess the direction and strength of the information flow between systems. Moreover, it provides good estimations for short-length time series.Fil: Restrepo Rinckoar, Juan Felipe. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; ArgentinaFil: Mateos, Diego Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Universidad Autónoma de Entre Ríos. Facultad de Ciencia y Tecnología; ArgentinaFil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentin

A Pattern Recognition Approach to Spasmodic Dysphonia and Muscle Tension Dysphonia Automatic Classification

Spasmodic dysphonia (SD) and muscle tension dysphonia (MTD) are two voice disorders that present similar characteristics. Usually, they can be differentiated only by experienced voice clinicians. There are many reasons that support the idea that SD is a neurological disease, requiring surgical treatments or, more usually, laryngeal botulinum toxin A injections as a therapeutic option. On the other hand, MTD is a functional disorder correctable with voice therapy. The importance of a correct diagnosis of these two disorders is critical at the treatment-selection moment. In this article, we present and compare the results of neural network and support vector machine-based methods that can help the clinicians to confirm their diagnosis. As a preliminary approach to the problem, we used only a sustained vowel /a/ to extract eight acoustic parameters. Then, a pattern recognition algorithm classifies the voice as normal, SD, or MTD. For comparison with previous works, we also separated the voices into normal and pathological (SD and MTD) voices with the methods proposed here. The results overcome the best classification rates between normal and pathological voices that have been previously reported, and demonstrate that our methods are very effective in distinguishing between MTD and SD.Fil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Torres, Maria Eugenia. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Jackson Menaldi, María Cristina. Wayne State University (wayne State University); Estados Unido

Noise-Assisted EMD Methods in Action

In this work we explore the capabilities of two noise-assisted EMD methods: Ensemble EMD (EEMD) and the recently proposed Complete Ensemble EMD with Adaptive Noise (CEEMDAN), to recover a pure tone embedded in dierent kinds of noise, both stationary and nonstationary. Experiments are carried out for assessing their performances with respect to the level of the added noise and the number of realizations used for averaging. The obtained results partly support empirical recommendations reported in the literature while evidencing new distinctive features. While EEMD presents quite dierent behaviors for dierent situations, CEEMDAN evidences some robustness with an almost unaected performance for the studied cases.Fil: Colominas, Marcelo Alejandro. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; ArgentinaFil: Flandrin, Patrick. Centre National de la Recherche Scientifique; Franci

Simplified sleep resistance test for daytime sleepiness detection

Objectives: Excessive daytime sleepiness (EDS) is a highly prevalent symptom that increases the risk of traffic accidents and deteriorates the quality of life. The diagnosis of EDS is difficult because of the complex infrastructure that is required. The new test here proposed assesses the ability of a simple test of simplify the detection of daytime sleepiness compared with the OSLER test. Material and Methods: In the new test, during 20 minute subjects were asked to pass a finger by a groove in response to a light emitting diode, inside dark glasses, which was lit for 1s in every three, with headphones that reduce the ambient noise and was compared with the OSLER test on each subject in random order. Results: The proposed method showed a sensitivity of 100% and a specificity of 61%, with a positive predictive value of 67% and negative predictive value of 100% when compared with the OSLER test. The value of area under the ROC curve was 0.81 (0.62-0.99), p=0.013. In a Bland-Altman plot, most of the latency times differences are in the 95% agreement interval (p=0.05). In addition, the confidence interval of the mean and most of the positive results are above the zero line. The Cohens Kappa coefficient obtained is 0.58 (95% CI 0.29-0.88). Conclusion: In this sample of patients, the proposed method detects EDS in a similar way as OSLER test and can be performed in different environments without requiring special infrastructure or expert personnel.Fil: Larrateguy, Luis Darío. Centro Privado de Medicina Respiratoria; ArgentinaFil: Pais, Carlos Marcelo. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaFil: Larrateguy, Luis Ignacio. Centro Privado de Medicina Respiratoria; ArgentinaFil: Larrateguy, Santiago Darío. Centro Privado de Medicina Respiratoria; ArgentinaFil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentin

Modeling and joint estimation of glottal source and vocal tract filter by state-space methods

Accurate estimation of the glottal source from a voiced sound is a difficult blind separation problem in speech signal processing. In this work, state-space methods are investigated to enhance the joint estimation of the glottal source and the vocal tract information. The aim of this paper is twofold. First, a stochastic glottal source is proposed, based on deterministic Liljencrants–Fant model and ruled by a stochastic difference equation. Such a representation allows to accurately capture any perturbation occurring at glottal level in real voices. A state-space voice model is formulated considering the stochastic glottal source. Then, combining this voice model and the state-space framework, an inverse filtering method is developed that allows to jointly estimate both glottal source and vocal tract filter. The performance of this method is studied by means of experiments with voices synthesized by applying both the source-filter theory and a physical based voice model. The method is also test using human voice signals. The results demonstrate that accurate estimates of the glottal source and the vocal tract filter can be obtained over several scenarios. Moreover, the method is shown to be robust with respect to different phonation types.Fil: Alzamendi, Gabriel Alejandro. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Entre Ríos. Universidad Nacional de Entre Ríos. Centro de Investigaciones y Transferencia de Entre Ríos; ArgentinaFil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Entre Ríos. Universidad Nacional de Entre Ríos. Centro de Investigaciones y Transferencia de Entre Ríos; Argentin

Automatic estimation of attractor invariants

The invariants of an attractor have been the most used resource to characterize a nonlinear dynamics. Their estimation is a challenging endeavor in short-time series and/or in presence of noise. In this article, we present two new coarse-grained estimators for the correlation dimension and for the correlation entropy. They can be easily estimated from the calculation of two U-correlation integrals. We have also developed an algorithm that is able to automatically obtain these invariants and the noise level in order to process large data sets. This method has been statistically tested through simulations in low-dimensional systems. The results show that it is robust in presence of noise and short data lengths. In comparison with similar approaches, our algorithm outperforms the estimation of the correlation entropy.Fil: Restrepo Rinckoar, Juan Felipe. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Analysis of hydroclimatic variability and trends using a novel empirical mode decomposition: Application to the Paraná River Basin

The current understanding of hydroclimatic processes is largely based on time series analysis of observations such as river discharge. Although records of these variables are often nonlinear and nonstationary, they have been commonly analyzed by classical methods designed for linear and/or stationary data. This study investigates the possibility of analyzing hydroclimatic time series using a novel data-driven method named Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), which is suitable for nonlinear and nonstationary signals. CEEMDAN is here applied to a monthly mean discharge record (1904–2010) of the Paraná River (South America). The results obtained in this way are interpreted by comparing them with CEEMDAN decompositions of other records such as climate index time series. It is found that Paraná flow modes consist of (i) annual and intraannual oscillations reflecting the rainfall seasonality of different Paraná Basin sectors, and (ii) interannual to interdecadal changes linked to climate cycles like El Niño/Southern Oscillation, the North Atlantic Oscillation, and the Interdecadal Pacific Oscillation. A nonlinear trend of Paraná discharge is found and reveals a monotonic increase that could be attributed to global warming and anthropogenic land-cover changes. The spectral separation of modes obtained using CEEMDAN is cleaner than that achieved by the Ensemble Empirical Mode Decomposition technique. This makes it easier to interpret CEEMDAN results. Hence, CEEMDAN is proposed as a powerful method for extracting physically meaningful information from hydroclimatic data.Fil: Antico, Andres. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; ArgentinaFil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaFil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentin