Source: Jia Z, Bien H, Entcheva E. Detecting space-time alternating biological signals close to the bifurcation point. IEEE Trans Biomed Eng. 2010 Feb;57(2):316-24.
Abstract: Time-alternating biological signals, i.e., alternans, arise in variety of physiological states marked by dynamic instabilities, e.g., period doubling. Normally, a sequence of large-small-large transients, they can exhibit variable patterns over time and space, including spatial discordance. Capture of the early formation of such alternating regions is challenging because of the spatiotemporal similarities between noise and the small-amplitude alternating signals close to the bifurcation point. We present a new approach for automatic detection of alternating signals in large noisy spatiotemporal datasets by exploiting quantitative measures of alternans evolution, e.g., temporal persistence, and by preserving phase information. The technique specifically targets low amplitude, relatively short alternating sequences and is validated by combinatorics-derived probabilities and empirical datasets with white noise. Using high-resolution optical mapping in live cardiomyocyte networks, exhibiting calcium alternans, we reveal for the first time early fine-scale alternans, close to the noise level, which are linked to the later formation of larger regions and evolution of spatially discordant alternans. This robust method aims at quantification and better understanding of the onset of cardiac arrhythmias and can be applied to general analysis of space-time alternating signals, including the vicinity of the bifurcation point.
Brief description of the algorithm:
The algorithm is designed to quantify the temporal persistence of period-2 rhythms (alternans) in traces of biological signals, exhibiting such instabilities (ECG, action potentials, calcium transients etc), recorded from multiple spatial locations. The advanced version (calfSDA) analyses data traces from multiple locations, obtained under dynamic pacing - successively increasing/decreasing pacing frequencies. It performs analysis and statistics on the identified alternans over space and time, revealing evolution patterns as pacing frequency changes, and keeping track of spatial concordance/discordance.
Both functions accept pre-processed data, after beat detection and extraction of a parameter of interest - peak height, duration etc. For the custom-developed pre-processing software please contact: emilia.entcheva@sunysb.edu
PMID: 19695992 | EndNote Citation
MATLAB code: calAltPer.m, calfSDA.m
function [newimage]=calAltPer(phMap,beat,varargin) - simple version
function [SDA Alt]=calfSDA(phMap,varargin) - advanced version with analysis and statistics
Please read detailed comments within the MATLAB files for function usage.