D. di Bernardo

Comparison of three measures of QT dispersion

QT dispersion quantifies the variability of QT intervals observed in the 12-lead ECG. The standard measure is the range of the QT intervals from the collection of the 12 ECG leads. The authors investigated the relationship between the standard measure, Range, and two alternative measures, standard deviation (SD) and interquartile range (IQR). The measures were assessed for their ability to differentiate between normal and two pathological groups. SD was highly correlated to Range in all subject groups. Weaker correlations were found between IQR and Range in the pathological groups and a very small correlation for the normal group. Of the measures considered, the current standard measure of QT dispersion was found to be the least effective in differentiating between normal and pathological subject groups. IQR appears to be a more discriminant measure of QT dispersion.

Can paroxysmal atrial fibrillation be predicted

Atrial fibrillation is an ECG rhythm with a significant mortality due to stroke. The objective of this study was to detect those patients most likely to develop atrial fibrillation, and to identify ECGs closest to the onset of fibrillation. Our hypothesis was that patients with atrial fibrillation would have atrial ectopy, and the frequency of this activity would increase prior to onset of fibrillation. From a learning set of 100 30-minute ECGs from 50 patients, 25 without atrial fibrillation (normal) and 25 who subsequently developed atrial fibrillation, an algorithm was developed to detect the presence of ectopic beats using R-R interval data. In the learning set, 37/50 abnormal and 34/50 normal patients were identified, giving a potential screening accuracy of 71%. As a prediction test to detect the ECGs closest to atrial fibrillation, 19/25 were correctly identified. For the test set, a total of 29/50 were correctly assigned to the normal and fibrillation groups, and a 39/50 score obtained in predicting the onset of atrial fibrillation.

Identification of regulatory pathways of the cell cycle in fission yeast

Abstract A novel identification method is presented, aimed at the reconstruction of genetic network structures. The iterative identification algorithm is based on least square linear regression, tailored to the case of scale free networks. The devised technique is assessed by comparing identification results with a well established in silico model of fission yeast cell cycle. Finally it is exploited for identifying the genetic network of fission yeast from experimental data available in the literature.

LMI-based Algorithm for the Reconstruction of Biological Networks

The general problem of reconstructing a biological network from temporal evolution data is tackled via an approach based on dynamical systems theory. In order to identify the dynamical model of the network an optimization algorithm, based on Linear Matrix Inequalities, is proposed. This approach allows to take into account, in the identification phase, both the experimental data and the a priori biological knowledge about the arcs of the network. Furthermore, the effectiveness of the proposed algorithm is improved by exploiting the assumption of scale-free structure, as usual in biological processes. The technique is validated against a well assessed case-study, that is the model of fission yeast cell cycle developed by Novak and Tyson.

Identification of Quadratic Nonlinear Models Oriented to Genetic Network Analysis

The goal of this paper is to provide a novel procedure for the identification of nonlinear models which exhibit a quadratic dependence on the state variables. These models turn out to be very useful for the description of a large class of biochemical processes with particular reference to the genetic networks regulating the cell cycle. The proposed approach is validated through extensive computer simulations on randomly generated systems

T wave shape changes with heart rate: a computer model analysis

The electrocardiogram T wave shape is known to change with the heart rate, but the cause is little understood. In this work, using a computer model of the left ventricle repolarisation, we investigated the T wave shape changes and proposed an explanation for this behaviour We used a computer model of left ventricle repolarisation to simulate the 12-lead ECG T waves. We defined a dispersion/duration ratio (Disp/APRD) as the maximum dispersion of repolarisation divided by the action potential repolarisation phase duration. The Disp/APRD is a measure of the heart rate in the model. We show that for increasing value of the Disp/APRD the T waves change in shape becoming more symmetrical while the T wave start-to-peak time interval shortens. We verified these results experimentally by recording the ECG on an healthy subject for two different heart rates (75 bpm and 130 bpm). We showed that our model predictions are confirmed by these preliminary experimental findings. The results from our model show, we believe for the first time, how the shape of the T wave changes with the heart rate and propose an explanation for this behaviour in terms of the Disp/APRD ratio. This work may have strong implications for improving the diagnosis of heart diseases using common clinical tests such as the exercise electrocardiography.

Modeling the cell cycle of fission yeast by means of piecewise linear systems

Biological process modeling requires the use of a non-LTI description. On the other hand, experimental data suggest that the behavior of many biological systems can be described with good approximation by means of piecewise linear time-invariant (LIT) models. On the basis of these considerations, the present work focuses on the problem of analyzing a set of experimental data and identifying the points where discontinuous phenomena occur. A validation of the technique is provided exploiting the well established cell cycle model of fission yeast by Novak and Tyson

Computer modelling of cardiac repolarisation

The origin of the T wave shape, the electrocardiographic manifestation of left ventricular repolarisation, is still unclear. No explanation has been given for its asymmetrical shape and upright orientation in normal subjects and its tall, symmetrical shape with inverted orientation commonly observed in heart pathologies. In this study a new and simple mathematical model of repolarisation of the left ventricle and of the associated body potentials is proposed. By choosing three different repolarisation sequences for the left ventricle, T waves with normal orientation and with pathologically inverted orientation were simulated. Using a normal experimental repolarisation sequence, the T waves in the simulated 12 lead ECG appeared as in normal subjects. Inverted, tall and symmetrical T waves were obtained using two abnormal repolarisation sequences (inverted epicardial repolarisation, inverted transmural repolarisation) increasing the value of dispersion towards 150 ms. Results from this work strongly suggest that the origin of tall, symmetrically: inverted T waves are an increased dispersion of repolarisation in the left ventricle and an abnormal repolarisation sequence.

Dispersion of repolarisation and the T wave: a computer model

A simple mathematical model of repolarisation of the left ventricle is presented. The T wave on the surface electrocardiogram is simulated. The model is applied to the study of increased dispersion of repolarisation that has been shown to be a substrate for fatal arrhythmias. Results from the model show that symmetrical and tall pathological T waves are a product of increased dispersion of repolarisation in the left ventricle. The end of the simulated T waves is the same in all 12 leads, suggesting that QT dispersion (defined as the differences of QT intervals in the 12 standard leads) is not a good measure of dispersion of repolarisation. The symmetry ratio, (SR), which measures the symmetry of the T wave, is shown to be a good candidate. The model predicts a value of SR close to 1.5 for small values of dispersion. This result is in agreement with the mean value of the symmetry ratio in normal subjects as reported in literature. For increasing values of dispersion, SR from. The model decreases towards value close to 1, demonstrating the source of the more symmetrical T waves in these patients.