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Many physiological systems are paradigmatic examples of complex networks, displaying behaviors best studied by means of tools derived from nonlinear dynamics and fractal geometry. Furthermore, while conventional wisdom considers health as an "orderly" situation (and diseases are often called "disorders"), truth is that health is characterized by a remarkable (pseudo)-randomness, and the loss of this pseudo-randomness (i.e., the "decomplexification" of the system's output) is one of the earliest sign of the system's dysfunction. The potential clinical uses of this information are evident. However, the instruments used to assess complexity are still under debate, and these tools are just beginning to find their place at the bedside. We present a brief overview of the potential uses of complexity analysis in several areas of clinical medicine. We comment on the metrics most frequently used, and we review specifically their application on certain neurologic diseases, aging, diabetes, febrile diseases and the critically ill patient.

Atrial fibrillation (AF) is the most commonly clinically-encountered arrhythmia. Catheter ablation of AF is mainly based on trigger elimination and modification of the AF substrate. Substrate mapping ablation of complex fractionated atrial electrograms (CFAEs) has emerged to be a promising technique. To improve substrate mapping based on CFAE analysis, automatic detection algorithms need to be developed in order to simplify and accelerate the ablation procedures. According to the latest studies, the level of fractionation has been shown to be promisingly well estimated from CFAE measured during radio frequency (RF) ablation of AF. The nature of CFAE is generally nonlinear and nonstationary, so the use of complexity measures is considered to be the appropriate technique for the analysis of AF records. This work proposes the use of sample entropy (SampEn), not only as a way to discern between non-fractionated and fractionated atrial electrograms (A-EGM), but also as a tool for characterizing the degree of A-EGM regularity, which is linked to changes in the AF substrate and to heart tissue damage. The use of SampEn combined with a blind parameter estimation optimization process enables the classification between CFAE and non-CFAE with statistical significance (p < 0:001), 0.89 area under the ROC, 86% specificity and 77% sensitivity over a mixed database of A-EGM combined from two independent CFAE signal databases, recorded during RF ablation of AF in two EU countries (542 signals in total). On the basis of the results obtained in this study, it can be suggested that the use of SampEn is suitable for real-time support during navigation of RF ablation of AF, as only 1.5 seconds of signal segments need to be analyzed.

Entropy estimation metrics have become a widely used method to identify subtle changes or hidden features in biomedical records. These methods have been more effective than conventional linear techniques in a number of signal classification applications, specially the healthy pathological segmentation dichotomy. Nevertheless, a thorough characterization of these measures, namely, how to match metric and signal features, is still lacking. This paper studies a specific characterization problem: the influence of missing samples in biomedical records. The assessment is conducted using four of the most popular entropy metrics: Approximate Entropy, Sample Entropy, Fuzzy Entropy, and Detrended Fluctuation Analysis. The rationale of this study is that missing samples are a signal disturbance that can arise in many cases: signal compression, non-uniform sampling, or data transmission stages. It is of great interest to determine if these real situations can impair the capability of segmenting signal classes using such metrics. The experiments employed several biosignals: electroencephalograms, gait records, and RR time series. Samples of these signals were systematically removed, and the entropy computed for each case. The results showed that these metrics are robust against missing samples: With a data loss percentage of 50% or even higher, the methods were still able to distinguish among signal classes.

[EN] This paper describes a new method to optimize the computation of the quadratic sample entropy (QSE) metric. The objective is to enhance its segmentation capability between pathological and healthy subjects for short and unevenly sampled biomedical records, like those obtained using ambulatory blood pressure monitoring (ABPM). In ABPM, blood pressure is measured every 20-30 min during 24h while patients undergo normal daily activities. ABPM is indicated for a number of applications such as white-coat, suspected, borderline, or masked hypertension. Hypertension is a very important clinical issue that can lead to serious health implications, and therefore its identification and characterization is of paramount importance. Nonlinear processing of signals by means of entropy calculation algorithms has been used in many medical applications to distinguish among signal classes. However, most of these methods do not perform well if the records are not long enough and/or not uniformly sampled. That is the case for ABPM records. These signals are extremely short and scattered with outliers or missing/resampled data. This is why ABPM Blood pressure signal screening using nonlinear methods is a quite unexplored field. We propose an additional stage for the computation of QSE independently of its parameter r and the input signal length. This enabled us to apply a segmentation process to ABPM records successfully. The experimental dataset consisted of 61 blood pressure data records of control and pathological subjects with only 52 samples per time series. The entropy estimation values obtained led to the segmentation of the two groups, while other standard nonlinear methods failed. (C) 2014 Elsevier Ireland Ltd. All rights reserved.

Las medidas de complejidad son un conjunto de métodos estadísticos que permiten estimar la regularidad de un sistema. Estos métodos se basan en técnicas de análisis no lineal de forma que se pueda caracterizar un señal sin hacer asunciones implícitas de estacionariedad o ergocidad de la misma. Estos métodos se están aplicando ampliamente sobre señales biológicas debido a la naturaleza de las mismas. Las señales biológicas se caracterizan por ser irregulares, no lineales y variables en el tiempo, de forma que los métodos tradicionales de análisis lineal no consiguen caracterizar su comportamiento completamente. Estas medidas funcionan muy bien en la práctica, ya que consiguen extraer información de las señales que de otra forma no es posible. Entre otras capacidades, consiguen diferenciar estados patológicos, precedir la aparición de un ataque epiléptico o distinguir entre estados del sueño. Pero su aplicación presenta cierta controversia, ya que carecen de una caracterización que indique al usuario qué medida aplicar en función de las características del registro, cómo debe ser aplicada o incluso c´omo interpretar los resultados obtenidos. En este trabajo se ha propuesto abordar una caracterización de algunas de las medidas de complejidad de uso más común. Se muestra una caracterización de la entropía aproximada (ApEn), la entropía muestral (SampEn), la entropía en multiples escalas o multiescala (MSE), el análisis de fluctuaciones sin tendencias (DFA), la entropía cuadrada de R´enyi (RSE) y el coeficiente de entropía muestral (CosEn), ante situaciones en las que las señales han perdido muestras o cuya longitud es limitada. La pérdida de muestras es algo muy común en la actualidad, dónde la mayoría de los registros se hacen de forma ambulatoria y el espacio de almacenamiento es limitado (compresión de datos) o la transmisión se hace de forma inalámbrica, donde el canal puede presentar condiciones inestables o interferencias que causen la pérdida de muestras, bien de