Extraction of the atrial activity from the ECG based on independent component analysis with prior knowledge of the source kurtosis signs

In this work it will be shown that a contrast for independent component analysis based on prior knowledge of the source kurtosis signs (ica-sks) is able to extract atrial activity from the electrocardiogram when a constrained updating is introduced. A spectral concentration measure is used, only allowing signal pair updates when spectral concentration augments. This strategy proves to be valid for independent source extraction with priors on the spectral concentration. Moreover, the method is computationally attractive with a very low complexity compared to the recently proposed methods based on spatiotemporal extraction of the atrial fibrillation signal

Novel approaches for quantitative electrogram analysis for rotor identification: Implications for ablation in patients with atrial fibrillation

University of Minnesota Ph.D. dissertation. May 2017. Major: Biomedical Engineering. Advisor: Elena Tolkacheva. 1 computer file (PDF); xxviii, 349 pages + 4 audio/video filesAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia that causes stroke affecting more than 2.3 million people in the US. Catheter ablation with pulmonary vein isolation (PVI) to terminate AF is successful for paroxysmal AF but suffers limitations with persistent AF patients as current mapping methods cannot identify AF active substrates outside of PVI region. Recent evidences in the mechanistic understating of AF pathophysiology suggest that ectopic activity, localized re-entrant circuit with fibrillatory propagation and multiple circuit re-entries may all be involved in human AF. Accordingly, the hypothesis that rotor is an underlying AF mechanism is compatible with both the presence of focal discharges and multiple wavelets. Rotors are stable electrical sources which have characteristic spiral waves like appearance with a pivot point surrounded by peripheral region. Targeted ablation at the rotor pivot points in several animal studies have demonstrated efficacy in terminating AF. The objective of this dissertation was to develop robust spatiotemporal mapping techniques that can fully capture the intrinsic dynamics of the non-stationary time series intracardiac electrogram signal to accurately identify the rotor pivot zones that may cause and maintain AF. In this thesis, four time domain approaches namely multiscale entropy (MSE) recurrence period density entropy (RPDE), kurtosis and intrinsic mode function (IMF) complexity index and one frequency domain approach namely multiscale frequency (MSF) was proposed and developed for accurate identification of rotor pivot points. The novel approaches were validated using optical mapping data with induced ventricular arrhythmia in ex-vivo isolated rabbit heart with single, double and meandering rotors (including numerically simulated data). The results demonstrated the efficacy of the novel approaches in accurate identification of rotor pivot point. The chaotic nature of rotor pivot point resulted in higher complexity measured by MSE, RPDE, kurtosis, IMF and MSF compared to the stable rotor periphery that enabled its accurate identification. Additionally, the feasibility of using conventional catheter mapping system to generate patient specific 3D maps for intraprocedural guidance for catheter ablation using these novel approaches was demonstrated with 1055 intracardiac electrograms obtained from both atria’s in a persistent AF patient. Notably, the 3D maps did not provide any clinically significant information on rotor pivot point identification or the presence of rotors themselves. Validation of these novel approaches is required in large datasets with paroxysmal and persistent AF patients to evaluate their clinical utility in rotor identification as potential targets for AF ablation

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Unsupervised methods in multilingual and multimodal semantic modeling

In the first part of this project, independent component analysis has been applied to extract word clusters from two Farsi corpora. Both word-document and word-context matrices have been considered to extract such clusters. The application of ICA on the word-document matrices extracted from these two corpora led to the detection of syntagmatic word clusters, while the utilization of word-context matrix resulted in the extraction of both syntagmatic and paradigmatic word clusters. Furthermore, we have discussed some potential benefits of this automatically extracted thesaurus. In such a thesaurus, a word is defined by some other words without being connected to the outer physical objects. In order to fill such a gap, symbol grounding has been proposed by philosophers as a mechanism which might connect words to their physical referents. From their point of view, if words are properly connected to their referents, their meaning might be realized. Once this objective is achieved, a new promising horizon would open in the realm of artificial intelligence. In the second part of the project, we have offered a simple but novel method for grounding words based on the features coming from the visual modality. Firstly, indexical grounding is implemented. In this naïve symbol grounding method, a word is characterized using video indexes as its context. Secondly, such indexical word vectors have been normalized according to the features calculated for motion videos. This multimodal fusion has been referred to as the pattern grounding. In addition, the indexical word vectors have been normalized using some randomly generated data instead of the original motion features. This third case was called randomized grounding. These three cases of symbol grounding have been compared in terms of the performance of translation. Besides that, word clusters have been excerpted by comparing the vector distances and from the dendrograms generated using an agglomerative hierarchical clustering method. We have observed that pattern grounding exceled the indexical grounding in the translation of the motion annotated words, while randomized grounding has deteriorated the translation significantly. Moreover, pattern grounding culminated in the formation of clusters in which a word fit semantically to the other members, while using the indexical grounding, some of the closely related words dispersed into arbitrary clusters