A Phase Space Box-counting based Method for Arrhythmia Prediction from Electrocardiogram Time Series

Arrhythmia is one kind of cardiovascular diseases that give rise to the number of deaths and potentially yields immedicable danger. Arrhythmia is a life threatening condition originating from disorganized propagation of electrical signals in heart resulting in desynchronization among different chambers of the heart. Fundamentally, the synchronization process means that the phase relationship of electrical activities between the chambers remains coherent, maintaining a constant phase difference over time. If desynchronization occurs due to arrhythmia, the coherent phase relationship breaks down resulting in chaotic rhythm affecting the regular pumping mechanism of heart. This phenomenon was explored by using the phase space reconstruction technique which is a standard analysis technique of time series data generated from nonlinear dynamical system. In this project a novel index is presented for predicting the onset of ventricular arrhythmias. Analysis of continuously captured long-term ECG data recordings was conducted up to the onset of arrhythmia by the phase space reconstruction method, obtaining 2-dimensional images, analysed by the box counting method. The method was tested using the ECG data set of three different kinds including normal (NR), Ventricular Tachycardia (VT), Ventricular Fibrillation (VF), extracted from the Physionet ECG database. Statistical measures like mean (μ), standard deviation (σ) and coefficient of variation (σ/μ) for the box-counting in phase space diagrams are derived for a sliding window of 10 beats of ECG signal. From the results of these statistical analyses, a threshold was derived as an upper bound of Coefficient of Variation (CV) for box-counting of ECG phase portraits which is capable of reliably predicting the impeding arrhythmia long before its actual occurrence. As future work of research, it was planned to validate this prediction tool over a wider population of patients affected by different kind of arrhythmia, like atrial fibrillation, bundle and brunch block, and set different thresholds for them, in order to confirm its clinical applicability.

13C NMR of a single molecule magnet: analysis of pseudocontact shifts and residual dipolar couplings

Paramagnetic triple decker complexes of lanthanides are promising Single Molecule Magnets (SMMs), with many potential uses. Some of them show preferable relaxation behavior, which enables the recording of well resolved NMR spectra. These axially symmetric complexes are also strongly magnetically anisotropic, and this property can be described with the axial component of the magnetic susceptibility tensor, χa. For triple decker complexes with phthalocyanine based ligands, the Fermi˗contact contribution is small. Hence, together with the axial symmetry, the experimental chemical shifts in 1H and 13C NMR spectra can be modeled easily by considering pseudocontact and orbital shifts alone. This results in the determination of the χa value, which is also responsible for molecular alignment and consequently for the observation of residual dipolar couplings (RDCs). A detailed analysis of the experimental 1H-13C and 1H-1H couplings revealed that contributions from RDCs (positive and negative) and from dynamic frequency shifts (negative for all observed couplings) have to be considered. Whilst the pseudocontact shifts depend on the average positions of 1H and 13C nuclei relative to the lanthanide ions, the RDCs are related to the mobility of nuclei they correspond to. This phenomenon allows for the measurement of the internal mobility of the various groups in the SMMs.