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.

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.

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.

Bidimensional unconstrained optimization approach to EMD: An algorithm revealing skin perfusion alterations in pseudoxanthoma elasticum patients.

BACKGROUND AND OBJECTIVE: Pseudoxanthoma elasticum (PXE) is an inherited and systemic metabolic disorder that affects the skin, leading among other things to a peau d'orange appearance. Unfortunately, PXE is still poorly understood and there is no existing therapy to treat the disease. Because the skin is the first organ to be affected in PXE, we propose herein a study of skin microvascular perfusion. By means of this analysis, our goal is to increase knowledge of PXE. METHODS: For this purpose, microvascular data from patients suffering from PXE and from healthy control subjects were recorded using the laser speckle contrast imaging (LSCI) modality. These data were processed using the recent 2D version of the unconstrained optimization approach to empirical mode decomposition (UOA-EMD). Our work therefore corresponds to the first time this algorithm has been applied to biomedical data. RESULTS: Our study shows that the 2D-UOA-EMD is able to reveal spatial patterns on local textures of LSCI data. Moreover, these spatial patterns differ between PXE patients and control subjects. Quantification measure of these spatial patterns reveals statistical significant differences between PXE and control subjects, in the neck (p=0.0004) and in the back (p=0.0052). CONCLUSIONS: For the first time, alterations in microvascular perfusion in PXE patients have been revealed. Our findings open new avenues for our understanding of pathophysiologic skin changes in PXE.

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 (K_{2}), 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 (D_{m}^{U}), the correlation entropy (K_{m}^{U}), and the noise level (σ_{m}^{U}). 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 D_{m}^{U} and σ_{m}^{U} behave in a similar manner to those based on the GCI. However, for the calculation of K_{2}, the estimator K_{m}^{U} outperforms its GCI-based counterpart. On the basis of the behavior of these estimators, we have proposed an automatic algorithm to find D,K_{2}, 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.

Orientation-Independent Empirical Mode Decomposition for Images Based on Unconstrained Optimization.

This paper introduces a 2D extension of the empirical mode decomposition (EMD), through a novel approach based on unconstrained optimization. EMD is a fully data-driven method that locally separates, in a completely data-driven and unsupervised manner, signals into fast and slow oscillations. The present proposal implements the method in a very simple and fast way, and it is compared with the state-of-the-art methods evidencing the advantages of being computationally efficient, orientation-independent, and leads to better performances for the decomposition of amplitude modulated-frequency modulated (AM-FM) images. The resulting genuine 2D method is successfully tested on artificial AM-FM images and its capabilities are illustrated on a biomedical example. The proposed framework leaves room for an nD extension (n > 2 ).

State-Space Approach to Structural Representation of Perturbed Pitch Period Sequences in Voice Signals.

OBJECTIVES: The aim of this study was to propose a state space-based approach to model perturbed pitch period sequences (PPSs), extracted from real sustained vowels, combining the principal features of disturbed real PPSs with structural analysis of stochastic time series and state space methods. METHODS: The PPSs were obtained from a database composed of 53 healthy subjects. State space models were developed taking into account different structures and complexity levels. PPS features such as trend, cycle, and irregular structures were considered. Model parameters were calculated using optimization procedures. For each PPS, state estimates were obtained combining the developed models and diffuse initialization with filtering and smoothing methods. Statistical tests were applied to objectively evaluate the performance of this method. RESULTS: Statistical tests demonstrated that the proposed approach correctly represented more than the 75% of the database with a significance value of 0.05. In the analysis, structural estimates suitably characterized the dynamics of the PPSs. Trend estimates proved to properly represent slow long-term dynamics, whereas cycle estimates captured short-term autoregressive dependencies. CONCLUSIONS: The present study demonstrated that the proposed approach is suitable for representing and analyzing real perturbed PPSs, also allowing to extract further information related to the phonation process.

Screening of obstructive sleep apnea with empirical mode decomposition of pulse oximetry.

Detection of desaturations on the pulse oximetry signal is of great importance for the diagnosis of sleep apneas. Using the counting of desaturations, an index can be built to help in the diagnosis of severe cases of obstructive sleep apnea-hypopnea syndrome. It is important to have automatic detection methods that allows the screening for this syndrome, reducing the need of the expensive polysomnography based studies. In this paper a novel recognition method based on the empirical mode decomposition of the pulse oximetry signal is proposed. The desaturations produce a very specific wave pattern that is extracted in the modes of the decomposition. Using this information, a detector based on properly selected thresholds and a set of simple rules is built. The oxygen desaturation index constructed from these detections produces a detector for obstructive sleep apnea-hypopnea syndrome with high sensitivity (0.838) and specificity (0.855) and yields better results than standard desaturation detection approaches.

The natural history of the sleep and respiratory engineering track at EMBC 1988 to 2010.

Sleep science and respiratory engineering as medical subspecialties and research areas grew up side-by-side with biomedical engineering. The formation of EMBS in the 1950's and the discovery of REM sleep in the 1950's led to parallel development and interaction of sleep and biomedical engineering in diagnostics and therapeutics.

Wavelet leader based multifractal analysis of heart rate variability during myocardial ischaemia.

Heart rate variability is a non invasive and indirect measure of the autonomic control of the heart. Therefore, alterations to this control system caused by myocardial ischaemia are reflected in changes in the complex and irregular fluctuations of this signal. Multifractal analysis is a well suited tool for the analysis of this kind of fluctuations, since it gives a description of the singular behavior of a signal. Recently, a new approach for multifractal analysis was proposed, the wavelet leader based multifractal formalism, which shows remarkable improvements over previous methods. In order to characterize and detect ischaemic episodes, in this work we propose to perform a short-time windowed wavelet leader based multifractal analysis. Our results suggest that this new method provides appropriate indexes that could be used as a tool for the detection of myocardial ischaemia.

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.

Modeling, identification and nonlinear model predictive control of type I diabetic patient.

Patients with type I diabetes nearly always need therapy with insulin. The most desirable treatment would be to mimic the operation of a normal pancreas. In this work a patient affected with this pathology is modeled and identified with a neural network, and a control strategy known as Nonlinear Model Predictive Control is evaluated as an approach to command an insulin pump using the subcutaneous route. A method for dealing with the problems related with the multiple insulin injections simulation and a multilayer neural network identification of the patient model is presented. The controller performance of the proposed strategy is tested under charge and reference disturbances (setpoint). Simulating an initial blood glucose concentration of 250 mg/dl a stable value of 97.0 mg/dl was reached, with a minimum level of 76.1 mg/dl. The results of a simulated 50 g oral glucose tolerance test show a maximum glucose concentration of 142.6 mg/dl with an undershoot of 76.0 mg/dl. According to the simulation results, stable close-loop control is achieved and physiological levels are reached with reasonable delays, avoiding the undesirable low glucose levels. Further studies are needed in order to deal with noise and robustness aspects, issues which are out of the scope of this work.