Automatic detection of wakefulness and rest intervals in actigraphic signals: a data-driven approach.

Actigraphy is an useful tool for evaluating the activity pattern of a subject; activity registries are usually processed by first splitting the signal into its wakefulness and rest intervals and then analyzing each one in isolation. Consequently, a preprocessing stage for such a splitting is needed. Several methods have been reported to this end but they rely on parameters and thresholds which are manually set based on previous knowledge of the signals or learned from training. This compromises the general applicability of this methods. In this paper we propose a new method in which thresholds are automatically set based solely on the specific registry to be analyzed. The method consists of two stages: (1) estimation of an initial classification mask by means of the expectation maximization algorithm and (2) estimation of a final refined mask through an iterative method which re-estimates both the mask and the classifier parameters at each iteration step. Results on real data show that our methodology outperforms those so far proposed and can be more effectively used to obtain derived sleep quality parameters from actigraphy registries.

Nonlinear analysis of actigraphic signals for the assessment of the attention-deficit/hyperactivity disorder (ADHD).

Attention-deficit/hyperactivity disorder (ADHD) is the most common neurobehavioral disorder in children and adolescents; however, its etiology is still unknown, which hinders the existence of reliable, fast and inexpensive standard diagnostic methods. In this paper, we propose a novel methodology for automatic diagnosis of the combined type of ADHD based on nonlinear signal processing of 24h-long actigraphic registries. Since it relies on actigraphy measurements, it constitutes an inexpensive and non-invasive objective diagnostic method. Our results on real data reach 96.77% sensitivity and 84.38% specificity by means of multidimensional classifiers driven by combined features from different time intervals. Our analysis also reveals that, if features from a single time interval are used, the whole 24-h interval is the only one that yields classification figures with practical diagnostic capabilities. Overall, our figures overcome those obtained by actigraphy-based methods reported and are comparable with others based on more expensive (and not so convenient) adquisition methods.

Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model.

A stochastic deformable model is proposed for the segmentation of the myocardium in Magnetic Resonance Imaging. The segmentation is posed as a probabilistic optimization problem in which the optimal time-dependent surface is obtained for the myocardium of the heart in a discrete space of locations built upon simple geometric assumptions. For this purpose, first, the left ventricle is detected by a set of image analysis tools gathered from the literature. Then, the segmentation solution is obtained by the Maximization of the Posterior Marginals for the myocardium location in a Markov Random Field framework which optimally integrates temporal-spatial smoothness with intensity and gradient related features in an unsupervised way by the Maximum Likelihood estimation of the parameters of the field. This scheme provides a flexible and robust segmentation method which has been able to generate results comparable to manually segmented images for some derived cardiac function parameters in a set of 43 patients affected in different degrees by an Acute Myocardial Infarction.

Sequential anisotropic multichannel Wiener filtering with Rician bias correction applied to 3D regularization of DWI data.

It has been shown that the tensor calculation is very sensitive to the presence of noise in the acquired images, yielding to very low quality Diffusion Tensor Images (DTI) data. Recent investigations have shown that the noise present in the Diffusion Weighted Images (DWI) causes bias effects on the DTI data which cannot be corrected if the noise characteristic is not taken into account. One possible solution is to increase the minimum number of acquired measurements (which is 7) to several tens (or even several hundreds). This has the disadvantage of increasing the acquisition time by one (or two) orders of magnitude, making the process inconvenient for a clinical setting. We here proposed a turn-around procedure for which the number of acquisitions is maintained but, the DWI data are filtered prior to determining the DTI. We show a significant reduction on the DTI bias by means of a simple and fast procedure which is based on linear filtering; well-known drawbacks of such filters are circumvented by means of anisotropic neighborhoods and sequential application of the filter itself. Information of the first order probability density function of the raw data, namely, the Rice distribution, is also included. Results are shown both for synthetic and real datasets. Some error measurements are determined in the synthetic experiments, showing how the proposed scheme is able to reduce them. It is worth noting a 50% increase in the linear component for real DTI data, meaning that the bias in the DTI is considerably reduced. A novel fiber smoothness measure is defined to evaluate the resulting tractography for real DWI data. Our findings show that after filtering, fibers are considerably smoother on the average. Execution times are very low as compared to other reported approaches which allows for a real-time implementation.

Signal LMMSE estimation from multiple samples in MRI and DT-MRI.

A method to estimate the magnitude MR data from several noisy samples is presented. It is based on the Linear Minimum Mean Squared Error (LMMSE) estimator for the Rician noise model when several scanning repetitions are available. This method gives a closed-form analytical solution that takes into account the probability distribution of the data as well as the existing level of noise, showing a better performance than methods such as the average or the median.

La edad como factor modificador del ritmo circadiano del infarto agudo de miocardio / Age as modifying factor of circadian rhythm of acute myocardial infarction

Objetivo. Estudiar la presencia y características del ritmo circadiano en la hora de inicio de los síntomas del infarto de miocardio (IAM) en un grupo de población geriátrica española.Ámbito. Unidades Coronarias adscritas al proyecto ARIAM. Diseño. Estudio de cohorte retrospectivo. Pacientes registrados en el proyecto multicéntrico ARIAM. Se seleccionaron los pacientes con diagnóstico de IAM al alta de la Unidad Coronaria (54.249 pacientes), registrados entre mayo de 1994 y octubre de 2003. Intervención. Ninguna. Variables de interés principales. Se analiza la hora de inicio del IAM, características generales de la población y del IAM (sexo, edad, extensión y localización del IAM y mortalidad dentro de la Unidad Coronaria). Se estratifica la población por edad (menores de 65 años, 65-74 años, 75-84 años y mayores de 85 años) y se comparan entre sí los subgrupos resultantes. Se aplica la prueba del χ2 sobre el porcentaje de pacientes agrupados en períodos de 6 horas. Resultados. La hora de inicio del dolor en el IAM muestra ritmo circadiano en todos los subgrupos de edad (p < 0,001). La frecuencia máxima se localiza en el período horario entre las 6 y 12 de la mañana; mientras que la frecuencia mínima se observa entre las 0 y 6 de la mañana, salvo en la población mayor de 85 años, en la que la frecuencia mínima se localiza entre las 6 de la tarde y la medianoche. La comparación entre los distintos subgrupos de edad muestra diferencias estadísticamente significativas, salvo en la comparación entre los subgrupos de 75-84 años y 85-100 años (p = 0,13). Conclusiones. El IAM en la población geriátrica, al igual que en la no geriátrica, muestra ritmo circadiano en la hora de presentación de sus síntomas. El pico de máxima incidencia matinal se acentúa con la edad. La incidencia mínima es nocturna, salvo en los muy ancianos, que es vespertina

Objective. Study the presence and characteristics of circadian rhythm when myocardial infarction (AMI) symptoms initiate in a group of Spanish geriatric population. Scope. Coronary Units included in ARIAM project. Design. Retrospective cohort study. Patients registered in the multicenter project ARIAM. Patients diagnosed of AMI on discharge from the Coronary Unit (54,249 patients), registered between May 1994 and October 2003, were selected. Intervention. None. Variables of principal interest. Time of onset of AMI, general characteristics of the population and AMI (gender, age, extension and location of AMI and mortality within the coronary unit) were analyzed. The population was stratified by age (younger than 65 years, 65-74 years, 75-84 years and older than 85 years) and the resulting subgroups were compared. The χ2 test was applied on the percentage of patients grouped in 6 hour periods. Results. Time of onset of pain in AMI showed circadian rhythm in all the age subgroups (p < 0.001). Maximum frequency was located in the time period between 6 a.m. and 12 p.m., while minimum frequency was observed between 12 a.m and 6 a.m., except in the population over 85 years, in which the minimum frequency was located between 6 p.m. and midnight a.m. Comparison between the different age subgroups showed statistically significant differences, except in the comparison between the subgroups of 75-84 years and 85-100 years (p = 0.13). Conclusions. Those with AMI in the geriatric population, as in the non-geriatric one, show circadian rhythm when their symptoms occur. Peak of maximum morning incidence increase with age. Minimum incidence is at night, except in the very elderly, where it is in the evening