Using Hilbert-Huang Transform to Assess EEG Slow Wave Activity During Anesthesia in Post-Cardiac Arrest Patients

Proceeding volume: 38Hypoxic ischemic encephalopathy (HIE) is a severe consequence of cardiac arrest (CA) representing a substantial diagnostic challenge. We have recently designed a novel method for the assessment of HIE after CA. The method is based on estimating the severity of the brain injury by analyzing changes in the electroencephalogram (EEG) slow wave activity while the patient is exposed to an anesthetic drug propofol in a controlled manner. In this paper, Hilbert-Huang Transform (HHT) was used to analyze EEG slow wave activity during anesthesia in ten post-CA patients. The recordings were made in the intensive care unit 36-48 hours after the CA in an experiment, during which the propofol infusion rate was incrementally decreased to determine the drug-induced changes in the EEG at different anesthetic levels. HHT was shown to successfully capture the changes in the slow wave activity to the behavior of intrinsic mode functions (IMFs). While, in patients with good neurological outcome defined after a six-month control period, propofol induced a significant increase in the amplitude of IMFs representing the slow wave activity, the patients with poor neurological outcome were unable to produce such a response. Consequently, the proposed method offer substantial prognostic potential by providing a novel approach for early estimation of HIE after CA.Peer reviewe

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

SLEEP-LIKE CORTICAL BISTABILITY IN VEGETATIVE STATE PATIENTS

The human brain is able to generate a wide repertoire of behavioral and psychological phenomena spanning from simple motor acts to cognition, from unimodal sensory perceptions to conscious experience. All these abilities are based on two key parameters of cortico-thalamic circuits functioning: the reactivity to a direct, local stimulation (cortical excitability) and the ability to causally interact (cortical effective connectivity). Indeed, alterations of these parameters have been suggested to underlie neurologic and psychiatric conditions. Over the last ten years, high-density electroencephalography combined with transcranial magnetic stimulation (TMS/hd-EEG) has been used to non-invasively probe cortical excitability and connectivity and to track over time pathological alterations, plastic changes and therapy-induced modifications in cortical circuits. A recently proposed theory suggests that consciousness depends on the brain\u2019s ability to engage in complex activity patterns that are, at once, distributed among interacting cortical areas (integrated) and differentiated in space and time (information-rich). In a recent series of experiments the electroencephalographic TMS-evoked brain response was recorded in healthy subjects during wakefulness, non-rapid eyes movement sleep (NREM), under pharmacological conditions (anesthesia), and pathological conditions (severely brain-injured, vegetative state patients). Indeed, TMS/hd-EEG measurements showed that during wakefulness the brain is able to sustain long-range specific patterns of activation, while when consciousness fades in NREM sleep, anesthesia and vegetative state, the thalamo-cortical system produces either a local or a global slow wave which underlies respectively a loss of differentiation or integration. We hypothesize that, like spontaneous sleep slow waves, the slow waves triggered by TMS are due to bistability between periods of neuronal activity (up-state) and silence (down-state) in cortical networks. Thalamo-cortical bistability could impair the ability of thalamo-cortical circuits to sustain long-range, differentiated patterns of activation, a key theoretical requisite for consciousness. Animal studies show that the extracellular signature of the down-state is a transient suppression of high frequency (>20Hz) power in the local field potential (LFP). More recently, intracranial recordings during NREM sleep in humans have shown that a intracranial stimulations induce a widespread suppression of high frequencies (i.e. cortical down-states) that impair the ability of thalamo-cortical circuits to engage in causal interactions. In the present thesis we use a TMS/hd-EEG approach in patients affected by disorders of consciousness such as vegetative state (VS) and minimally conscious state (MCS) to investigate whether bistability could underlie also pathological loss of consciousness. To verify this hypothesis, we recorded TMS-evoked potentials (TEPs) in awake VS and MCS patients as well as in healthy controls (HC) during wakefulness and NREM sleep. TEPs were analyzed by means of time-frequency analyses (power and phase-locking factor - PLF). We observed that TEPs recorded in VS patients were characterized by a large positive-negative deflection, closely resembling the one recorded in HC during NREM sleep. This sleep-like slow-wave was associated with a significant suppression of power in the high frequency band (>20 Hz) together with an early drop of PLF. Interestingly, in VS patients the power suppression slowly recovered to the baseline whereas in the NREM sleep of HC it was replaced by a late increase of power. Finally, the recovery of consciousness assessed in two patients evaluated longitudinally was paralleled by the resurgence of TEPs high frequency oscillations and by an increase of PLF duration. These results suggest that the slow waves evoked by TMS in VS patients possibly reflect a condition of cortical bistability that prevents the entrainment of thalamocortical modules in effective interactions and, hence, the emergence of consciousness. Intriguingly, the resumption of TEPs high frequency oscillations and a longer duration of phase-locked components (PLF) seem to be associated with the recovery of consciousness. Since bistability is, in principle, reversible and its mechanisms are well understood at the cellular and network level, it may represent a suitable target for novel therapeutic approaches in patients in whom consciousness is impaired, in spite of preserved cortical activity

Novel Cardiac Mapping Approaches and Multimodal Techniques to Unravel Multidomain Dynamics of Complex Arrhythmias Towards a Framework for Translational Mechanistic-Based Therapeutic Strategies

[ES] Las arritmias cardíacas son un problema importante para los sistemas de salud en el mundo desarrollado debido a su alta incidencia y prevalencia a medida que la población envejece. La fibrilación auricular (FA) y la fibrilación ventricular (FV) se encuentran entre las arritmias más complejas observadas en la práctica clínica. Las consecuencias clínicas de tales alteraciones arrítmicas incluyen el desarrollo de eventos cardioembólicos complejos en la FA, y repercusiones dramáticas debido a procesos fibrilatorios sostenidos que amenazan la vida infringiendo daño neurológico tras paro cardíaco por FV, y que pueden provocar la muerte súbita cardíaca (MSC). Sin embargo, a pesar de los avances tecnológicos de las últimas décadas, sus mecanismos intrínsecos se comprenden de forma incompleta y, hasta la fecha, las estrategias terapéuticas carecen de una base mecanicista suficiente y poseen bajas tasas de éxito. Entre los mecanismos implicados en la inducción y perpetuación de arritmias cardíacas, como la FA, se cree que las dinámicas de las fuentes focales y reentrantes de alta frecuencia, en sus diferentes modalidades, son las fuentes primarias que mantienen la arritmia. Sin embargo, se sabe poco sobre los atractores, así como, de la dinámica espacio-temporal de tales fuentes fibrilatorias primarias, específicamente, las fuentes focales o rotacionales dominantes que mantienen la arritmia. Por ello, se ha desarrollado una plataforma computacional, para comprender los factores (activos, pasivos y estructurales) determinantes, y moduladores de dicha dinámica. Esto ha permitido establecer un marco para comprender la compleja dinámica de los rotores con énfasis en sus propiedades deterministas para desarrollar herramientas basadas en los mecanismos para ayuda diagnóstica y terapéutica. Comprender los procesos fibrilatorios es clave para desarrollar marcadores y herramientas fisiológica- y clínicamente relevantes para la ayuda de diagnóstico temprano. Específicamente, las propiedades espectrales y de tiempo-frecuencia de los procesos fibrilatorios han demostrado resaltar el comportamiento determinista principal de los mecanismos intrínsecos subyacentes a las arritmias y el impacto de tales eventos arrítmicos. Esto es especialmente relevante para determinar el pronóstico temprano de los supervivientes comatosos después de un paro cardíaco debido a fibrilación ventricular (FV). Las técnicas de mapeo electrofisiológico, el mapeo eléctrico y óptico cardíaco, han demostrado ser recursos muy valiosos para dar forma a nuevas hipótesis y desarrollar nuevos enfoques mecanicistas y estrategias terapéuticas mejoradas. Esta tecnología permite además el trabajo multidisciplinar entre clínicos y bioingenieros, para el desarrollo y validación de dispositivos y metodologías para identificar biomarcadores multi-dominio que permitan rastrear con precisión la dinámica de las arritmias identificando fuentes dominantes y atractores con alta precisión para ser dianas de estrategias terapeúticas innovadoras. Es por ello que uno de los objetivos fundamentales ha sido la implantación y validación de nuevos sistemas de mapeo en distintas configuraciones que sirvan de plataforma de desarrollo de nuevas estrategias terapeúticas. Aunque el mapeo panorámico es el método principal y más completo para rastrear simultáneamente biomarcadores electrofisiológicos, su adopción por la comunidad científica es limitada principalmente debido al coste elevado de la tecnología. Aprovechando los avances tecnológicos recientes, nos hemos enfocado en desarrollar, y validar, sistemas de mapeo óptico de alta resolución para registro panorámico cardíaco, utilizando modelos clínicamente relevantes para la investigación básica y la bioingeniería.[CA] Les arítmies cardíaques són un problema important per als sistemes de salut del món desenvolupat a causa de la seva alta incidència i prevalença a mesura que la població envelleix. La fibril·lació auricular (FA) i la fibril·lació ventricular (FV), es troben entre les arítmies més complexes observades a la pràctica clínica. Les conseqüències clíniques d'aquests trastorns arítmics inclouen el desenvolupament d'esdeveniments cardioembòlics complexos en FA i repercussions dramàtiques a causa de processos fibril·latoris sostinguts que posen en perill la vida amb danys neurològics posteriors a la FV, que condueixen a una aturada cardíaca i a la mort cardíaca sobtada (SCD). Tanmateix, malgrat els avanços tecnològics de les darreres dècades, els seus mecanismes intrínsecs s'entenen de forma incompleta i, fins a la data, les estratègies terapèutiques no tenen una base mecanicista suficient i tenen baixes taxes d'èxit. La majoria dels avenços en el desenvolupament de biomarcadors òptims i noves estratègies terapèutiques en aquest camp provenen de tècniques valuoses en la investigació de mecanismes d'arítmia. Entre els mecanismes implicats en la inducció i perpetuació de les arítmies cardíaques, es creu que les fonts primàries subjacents a l'arítmia són les fonts focals reingressants d'alta freqüència dinàmica i AF, en les seves diferents modalitats. Tot i això, se sap poc sobre els atractors i la dinàmica espaciotemporal d'aquestes fonts primàries fibril·ladores, específicament les fonts rotacionals o focals dominants que mantenen l'arítmia. Per tant, s'ha desenvolupat una plataforma computacional per entendre determinants actius, passius, estructurals i moduladors d'aquestes dinàmiques. Això va permetre establir un marc per entendre la complexa dinàmica multidomini dels rotors amb ènfasi en les seves propietats deterministes per desenvolupar enfocaments mecanicistes per a l'ajuda i la teràpia diagnòstiques. La comprensió dels processos fibril·latoris és clau per desenvolupar puntuacions i eines rellevants fisiològicament i clínicament per ajudar al diagnòstic precoç. Concretament, les propietats espectrals i de temps-freqüència dels processos fibril·latoris han demostrat destacar un comportament determinista important dels mecanismes intrínsecs subjacents a les arítmies i l'impacte d'aquests esdeveniments arítmics. Mitjançant coneixements previs, processament de senyals, tècniques d'aprenentatge automàtic i anàlisi de dades, es va desenvolupar una puntuació de risc mecanicista a la aturada cardíaca per FV. Les tècniques de cartografia òptica cardíaca i electrofisiològica han demostrat ser recursos inestimables per donar forma a noves hipòtesis i desenvolupar nous enfocaments mecanicistes i estratègies terapèutiques. Aquesta tecnologia ha permès durant molts anys provar noves estratègies terapèutiques farmacològiques o ablatives i desenvolupar mètodes multidominis per fer un seguiment precís de la dinàmica d'arrímies que identifica fonts i atractors dominants. Tot i que el mapatge panoràmic és el mètode principal per al seguiment simultani de paràmetres electrofisiològics, la seva adopció per part de la comunitat multidisciplinària d'investigació cardiovascular està limitada principalment pel cost de la tecnologia. Aprofitant els avenços tecnològics recents, ens centrem en el desenvolupament i la validació de sistemes de mapes òptics de baix cost per a imatges panoràmiques mitjançant models clínicament rellevants per a la investigació bàsica i la bioenginyeria.[EN] Cardiac arrhythmias are a major problem for health systems in the developed world due to their high incidence and prevalence as the population ages. Atrial fibrillation (AF) and ventricular fibrillation (VF), are amongst the most complex arrhythmias seen in the clinical practice. Clinical consequences of such arrhythmic disturbances include developing complex cardio-embolic events in AF, and dramatic repercussions due to sustained life-threatening fibrillatory processes with subsequent neurological damage under VF, leading to cardiac arrest and sudden cardiac death (SCD). However, despite the technological advances in the last decades, their intrinsic mechanisms are incompletely understood, and, to date, therapeutic strategies lack of sufficient mechanistic basis and have low success rates. Most of the progress for developing optimal biomarkers and novel therapeutic strategies in this field has come from valuable techniques in the research of arrhythmia mechanisms. Amongst the mechanisms involved in the induction and perpetuation of cardiac arrhythmias such AF, dynamic high-frequency re-entrant and focal sources, in its different modalities, are thought to be the primary sources underlying the arrhythmia. However, little is known about the attractors and spatiotemporal dynamics of such fibrillatory primary sources, specifically dominant rotational or focal sources maintaining the arrhythmia. Therefore, a computational platform for understanding active, passive and structural determinants, and modulators of such dynamics was developed. This allowed stablishing a framework for understanding the complex multidomain dynamics of rotors with enphasis in their deterministic properties to develop mechanistic approaches for diagnostic aid and therapy. Understanding fibrillatory processes is key to develop physiologically and clinically relevant scores and tools for early diagnostic aid. Specifically, spectral and time-frequency properties of fibrillatory processes have shown to highlight major deterministic behaviour of intrinsic mechanisms underlying the arrhythmias and the impact of such arrhythmic events. Using prior knowledge, signal processing, machine learning techniques and data analytics, we aimed at developing a reliable mechanistic risk-score for comatose survivors of cardiac arrest due to VF. Cardiac optical mapping and electrophysiological mapping techniques have shown to be unvaluable resources to shape new hypotheses and develop novel mechanistic approaches and therapeutic strategies. This technology has allowed for many years testing new pharmacological or ablative therapeutic strategies, and developing multidomain methods to accurately track arrhymia dynamics identigying dominant sources and attractors. Even though, panoramic mapping is the primary method for simultaneously tracking electrophysiological parameters, its adoption by the multidisciplinary cardiovascular research community is limited mainly due to the cost of the technology. Taking advantage of recent technological advances, we focus on developing and validating low-cost optical mapping systems for panoramic imaging using clinically relevant models for basic research and bioengineering.Calvo Saiz, CJ. (2022). Novel Cardiac Mapping Approaches and Multimodal Techniques to Unravel Multidomain Dynamics of Complex Arrhythmias Towards a Framework for Translational Mechanistic-Based Therapeutic Strategies [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182329TESI

Artificial intelligence techniques for studying neural functions in coma and sleep disorders

The use of artificial intelligence in computational neuroscience has increased within the last years. In the field of electroencephalography (EEG) research machine and deep learning, models show huge potential. EEG data is high dimensional, and complex models are well suited for their analysis. However, the use of artificial intelligence in EEG research and clinical applications is not yet established, and multiple challenges remain to be addressed. This thesis is focused on analyzing neurological EEG signals for clinical applications with artificial intelligence and is split into three sub-projects. The first project is a methodological contribution, presenting a proof of concept that deep learning on EEG signals can be used as a multivariate pattern analysis technique for research. Even though the field of deep learning for EEG has produced many publications, the use of these algorithms in research for the analysis of EEG signals is not established. Therefore for my first project, I developed an analysis pipeline based on a deep learning architecture, data augmentation techniques, and feature extraction method that is class and trial-specific. In summary, I present a novel multivariate pattern analysis pipeline for EEG data based on deep learning that can extract in a data-driven way trial-by-trial discriminant activity. In the second part of this thesis, I present a clinical application of predicting the outcome of comatose patients after cardiac arrest. Outcome prediction of patients in a coma is today still an open challenge, that depends on subjective clinical evaluations. Importantly, current clinical markers can leave up to a third of patients without a clear prognosis. To address this challenge, I trained a convolutional neural network on EEG signals of coma patients that were exposed to standardized auditory stimulations. This work showed a high predictive power of the trained deep learning model, also on patients that were without a established prognosis based on existing clinical criteria. These results emphasize the potential of deep learning models for predicting outcome of coma and assisting clinicians. In the last part of my thesis, I focused on sleep-wake disorders and studied whether unsupervised machine learning techniques could improve diagnosis. The field of sleep-wake disorders is convoluted, as they can cooccur within patients, and only a few disorders have clear diagnostic biomarkers. Thus I developed a pipeline based on an unsupervised clustering algorithm to disentangle the full landscape of sleep-wake disorders. First I reproduced previous results in a sub-cohort of patients with central disorders of hypersomnolence. The verified pipeline was then used on the full landscape of sleep-wake disorders, where I identified clear clusters of disorders with clear diagnostic biomarkers. My results call for new biomarkers, to improve patient phenotyping

Abnormal network oscillations in patients with Dementia with Lewy bodies and a mouse model of alpha-synucleinopathy

Ph. D. Thesis.Electrophysiology can reveal changes in neuronal oscillatory activity in the brain in relation to neurodegenerative disorders, including dementia with Lewy bodies (DLB). DLB, characterised by abnormal α-synuclein (α-syn) aggregation within neurons, is the second most common cause of dementia after Alzheimer’s disease (AD). This thesis had two aims. Firstly, to identify resting-state EEG changes that reflect cognitive fluctuations, a DLB core symptom, and differentiate DLB from AD and Parkinson’s disease dementia (PDD) patients and healthy controls. Secondly, to detect electrophysiological alterations in young mice over-expressing human mutant α-syn while under urethane-induced anaesthesia, mimicking deep-sleep. The resulting slowoscillation (SO) composed of Up-states (neuronal firing) and Down-states (neuronal “silence”), was recorded intra-cortically, from the hippocampus and medial prefrontal cortex (mPFC). The human EEG analysis replicated reports of a shift of power and dominant frequency (DF) from alpha to theta frequencies, in DLB/PDD patients compared to AD patients and controls. Contrary to previous work, the DF variability (DFV) over time was increased in AD and not in DLB/PDD patients, although a DLBspecific correlation between the DFV and cognitive fluctuations persisted. The DFV and power/DF distribution could differentiate between AD and DLB patients with high sensitivity (92.26%) and specificity (83.3%). Analysis of sleep patterns in α-syn mice in both the mPFC and hippocampus revealed increased SO frequency, aberrant neuronal firing activity on Down-states, altered power distribution on Up-states and disturbed sleep spindle activity, compared to WTs. A novel infra-slow modulation (ISM) was described in WTs, presenting as bursts of power across frequencies every ~ 3.5 min. In α-syn mice, the ISM induced abnormally high levels of high frequency oscillatory power in the hippocampus. Our findings indicate altered neuronal oscillatory activity in DLB patients and α-syn mice during rest and sleep respectively, suggesting that α-syn affects the integrity of the networks underlying widespread, synchronous activity.Alzheimer’s Societ

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Motion Artifact Processing Techniques for Physiological Signals

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an ageing population and this is placing an ever-increasing burden on our healthcare systems. The urgent need to address this so called healthcare \time bomb" has led to a rapid growth in research into ubiquitous, pervasive and distributed healthcare technologies where recent advances in signal acquisition, data storage and communication are helping such systems become a reality. However, similar to recordings performed in the hospital environment, artifacts continue to be a major issue for these systems. The magnitude and frequency of artifacts can vary signicantly depending on the recording environment with one of the major contributions due to the motion of the subject or the recording transducer. As such, this thesis addresses the challenges of the removal of this motion artifact removal from various physiological signals. The preliminary investigations focus on artifact identication and the tagging of physiological signals streams with measures of signal quality. A new method for quantifying signal quality is developed based on the use of inexpensive accelerometers which facilitates the appropriate use of artifact processing methods as needed. These artifact processing methods are thoroughly examined as part of a comprehensive review of the most commonly applicable methods. This review forms the basis for the comparative studies subsequently presented. Then, a simple but novel experimental methodology for the comparison of artifact processing techniques is proposed, designed and tested for algorithm evaluation. The method is demonstrated to be highly eective for the type of artifact challenges common in a connected health setting, particularly those concerned with brain activity monitoring. This research primarily focuses on applying the techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) data due to their high susceptibility to contamination by subject motion related artifact. Using the novel experimental methodology, complemented with simulated data, a comprehensive comparison of a range of artifact processing methods is conducted, allowing the identication of the set of the best performing methods. A novel artifact removal technique is also developed, namely ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA), which provides the best results when applied on fNIRS data under particular conditions. Four of the best performing techniques were then tested on real ambulatory EEG data contaminated with movement artifacts comparable to those observed during in-home monitoring. It was determined that when analysing EEG data, the Wiener lter is consistently the best performing artifact removal technique. However, when employing the fNIRS data, the best technique depends on a number of factors including: 1) the availability of a reference signal and 2) whether or not the form of the artifact is known. It is envisaged that the use of physiological signal monitoring for patient healthcare will grow signicantly over the next number of decades and it is hoped that this thesis will aid in the progression and development of artifact removal techniques capable of supporting this growth

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