Open-source software for generating electrocardiogram signals

ECGSYN, a dynamical model that faithfully reproduces the main features of the human electrocardiogram (ECG), including heart rate variability, RR intervals and QT intervals is presented. Details of the underlying algorithm and an open-source software implementation in Matlab, C and Java are described. An example of how this model will facilitate comparisons of signal processing techniques is provided.Comment: 10 pages, 5 figure

Review of "System Modeling in Cellular Biology: From Concepts to Nuts and Bolts" by Szallasi, Stelling and Periwal

"System Modeling in Cellular Biology: From Concepts to Nuts and Bolts" by Szallasi, Stelling and Periwal introduces the relevant concepts, terminology, and techniques of this field of science. It emphasises the modelling and computational challenges of taking a multidisciplinary approach to biology. This book provides a comprehensive introduction to systems biology and will form a valuable resource for students, teachers and researchers from both experimental and theoretical disciplines

A Comparative Study of the Magnitude, Frequency and Distribution of Intense Rainfall in the United Kingdom

During the 1960s, a study was made of the magnitude, frequency and distribution of intense rainfall over the UK, employing data from more than 120 daily-read rain gauges covering the period 1911 to 1960. Using the same methodology, that study was recently updated utilizing data for the period 1961 to 2006 for the same gauges, or from those nearby. This paper describes the techniques applied to ensure consistency of data and statistical modelling. It presents a comparison of patterns of extreme rainfalls for the two periods and discusses the changes that have taken place. Most noticeably, increases up to 20% have occurred in the north west of the country and in parts of East Anglia. There have also been changes in other areas, including decreases of the same magnitude over central England. The implications of these changes are considered

Can index based insurance reduce the vulnerability of farmers to weather?

An IGC study reveals that index insurance has the potential to reduce the vulnerability of farmers to weather. This is dependent on data quality and model accuracy, with the highest predictive capacity involving a combination of satellite datasets. Even so, variation in agricultural production remains a challenge. Furthermore, the insurance needs to be credible and reliable, and accompanied by substantial training, to ensure farmers have adequate knowledge to make informed decisions

A Simple Filter Benchmark for Feature Selection

Abstract A new correlation-based filter approach for simple, fast, and effective feature selection (FS) is proposed. The association strength between each feature and the response variable (relevance) and between pairs of features (redundancy) is quantified via a simple nonlinear transformation of correlation coefficients inspired by information theoretic concepts. Furthermore, the association strength between a set of features and the response variable (feature complementarity) is explicitly addressed using a similar nonlinear transformation of partial correlation coefficients, where a feature is selected conditionally upon its additional information content when combined with the features already selected in the forward sequential process. The new filter scheme overcomes several major issues associated with competing FS algorithms, including computational complexity and difficulty in implementation, and can be used on both multi-class classification and regression problems. Experiments on five synthetic and twelve real datasets demonstrate that the proposed filter outperforms popular alternative filter approaches in terms of recovering the correct features. We envisage the proposed scheme setting a competitive benchmark against which more sophisticated FS algorithms can be compared. Documented Matlab source code is available on the first author's website

Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection

Background: Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness.

Methods: This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a "hoarseness" diagram. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices.

Results: On a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8% plus or minus 2.0%. The true positive classification performance is 95.4% plus or minus 3.2%, and the true negative performance is 91.5% plus or minus 2.3% (95% confidence). This is shown to outperform all combinations of the most popular classical tools.

Conclusions: Given the very large number of arbitrary parameters and computational complexity of existing techniques, these new techniques are far simpler and yet achieve clinically useful classification performance using only a basic classification technique. They do so by exploiting the inherent nonlinearity and turbulent randomness in disordered voice signals. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.
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A Graphical User Interface for a Method to Infer Kinetics and Network Architecture (MIKANA)

One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis–Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1)

Correlates of depression in bipolar disorder

We analyse time series from 100 patients with bipolar disorder for correlates of depression symptoms. As the sampling interval is non-uniform, we quantify the extent of missing and irregular data using new measures of compliance and continuity. We find that uniformity of response is negatively correlated with the standard deviation of sleep ratings (ρ = -0.26, p = 0.01). To investigate the correlation structure of the time series themselves, we apply the Edelson-Krolik method for correlation estimation. We examine the correlation between depression symptoms for a subset of patients and find that self-reported measures of sleep and appetite/weight show a lower average correlation than other symptoms. Using surrogate time series as a reference dataset, we find no evidence that depression is correlated between patients, though we note a possible loss of information from sparse sampling