Effect of high-pass filtering on ECG signal on the analysis of patients prone to atrial fibrillation

The aim of this study was to assess the effect of filtering techniques on the time-domain analysis of the ECG. Multi-lead ECG recordings obtained from chronic atrial fibrillation (AF) patients after successful external cardioversion have been acquired. Several high-pass filtering techniques and three cut-off frequency values were used: Bessel and Butterworth four-pole and two-pole bidirectional and unidirectional filters, at 0.01, 0.05 and 0.5 Hz low cut-off frequency. As a reference, a beat-by-beat linear piecewise interpolation was used to remove baseline wander, on each P-wave. Results show that ECG filtering affects the estimation of P-wave duration in a manner that depends upon the type of filter used: particularly, the bidirectional filters caused negligible variation of P-wave duration, while unidirectional ones provoked an increase higher than 8%

Quantification of Ventricular Repolarization Dispersion Using Digital Processing of the Surface ECG

Digital processing of electrocardiographic records was one of the first applications of signal processing on medicine. There are many ways to analyze and study electrical cardiac activity using the surface electrocardiogram (ECG) and nowadays a good clinical diagnostic and prevention of cardiac risk are the principal goal to be achieved. One aim of digital processing of ECG signals has been quantification of ventricular repolarization dispersion (VRD), phenomenon which mainly is determined by heterogeneity of action potential durations (APD) in different myocardial regions. The APD differs not only between myocytes of apex and the base of both ventricles, but those of endocardial and epicardial surfaces (transmural dispersion) and between both ventricles. Also, it was demonstrated that several electrophysiologically and functionally different myocardial cells, like epicardial, endocardial and mid-myocardial M cells. The APD inequalities develop global and/or local voltage gradients that play an important role in the inscription of ECG T-wave morphology. In this way, we can assume that T-wave is a direct expression of ventricular repolarization inhomogeneities on surface ECG. Experimental and clinical studies have demonstrated a relationship between VRD and severe ventricular arrhythmias. In addition, patients having increased VRD values have a higher risk of developing reentrant arrhythmias. Frequently the heart answer to several pathological states produced an increase of VRD; this phenomenon may develop into malignant ventricular arrhythmia (MVA) and/or sudden cardiac death (SCD). Moreover, it has been showed that the underlying mechanisms in MVA and/or SCD are cardiac re-entry, increased automation, influence of autonomic nervous system and arrhythmogenic substrates linked with cardiac pathologies. These cardiac alterations could presented ischemia, hypothermia, electrolyte imbalance, long QT syndrome, autonomic system effects and others. Digital processing of ECG has been proved to be useful for cardiac risk assessment, with additional advantages like of being non invasive treatments and applicable to the general population. With the aim to identify high cardiac risk patients, the researchers have been tried to quantify the VRD with different parameters obtained by mathematic-computational processing of the surface ECG. These parameters are based in detecting changes of T-wave intervals and T-wave morphology during cardiac pathologies, linking these changes with VRD. In this chapter, we have presented a review of VRD indexes based on digital processing of ECG signals to quantify cardiac risk. The chapter is organized as follows: Section 2 explains ECG preprocessing and delineation of fiducial points. In Section 3, indexes of VRD quantification, such as: QT interval dispersion, QT interval variability and T-wave duration, are described. In Section 4, different repolarization indexes describing T-wave morphology and energy are examined, including complexity of repolarization, T-wave residuum, angle between the depolarization and repolarization dominant vectors, micro T-wave alternans, T-wave area and amplitude and T-wave spectral variability. Finally, in Section 5 conclusions are presented.Fil: Vinzio Maggio, Ana Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaFil: Bonomini, Maria Paula. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Laciar Leber, Eric. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería; ArgentinaFil: Arini, Pedro David. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentin

Cardiac electrical defects in progeroid mice and Hutchinson-Gilford progeria syndrome patients with nuclear lamina alterations

Hutchinson–Gilford progeria syndrome (HGPS) is a rare genetic disease caused by defective prelamin A processing, leading to nuclear lamina alterations, severe cardiovascular pathology, and premature death. Prelamin A alterations also occur in physiological aging. It remains unknown how defective prelamin A processing affects the cardiac rhythm. We show age-dependent cardiac repolarization abnormalities in HGPS patients that are also present in the Zmpste24-/- mouse model of HGPS. Challenge of Zmpste24-/- mice with the ß-adrenergic agonist isoproterenol did not trigger ventricular arrhythmia but caused bradycardia-related premature ventricular complexes and slow-rate polymorphic ventricular rhythms during recovery. Patch-clamping in Zmpste24-/- cardiomyocytes revealed prolonged calcium-transient duration and reduced sarcoplasmic reticulum calcium loading and release, consistent with the absence of isoproterenol-induced ventricular arrhythmia. Zmpste24-/- progeroid mice also developed severe fibrosis-unrelated bradycardia and PQ interval and QRS complex prolongation. These conduction defects were accompanied by overt mislocalization of the gap junction protein connexin43 (Cx43). Remarkably, Cx43 mislocalization was also evident in autopsied left ventricle tissue from HGPS patients, suggesting intercellular connectivity alterations at late stages of the disease. The similarities between HGPS patients and progeroid mice reported here strongly suggest that defective cardiac repolarization and cardiomyocyte connectivity are important abnormalities in the HGPS pathogenesis that increase the risk of arrhythmia and premature death.Peer ReviewedPostprint (published version

ECG Noise Filtering Using Online Model-Based Bayesian Filtering Techniques

The electrocardiogram (ECG) is a time-varying electrical signal that interprets the electrical activity of the heart. It is obtained by a non-invasive technique known as surface electromyography (EMG), used widely in hospitals. There are many clinical contexts in which ECGs are used, such as medical diagnosis, physiological therapy and arrhythmia monitoring. In medical diagnosis, medical conditions are interpreted by examining information and features in ECGs. Physiological therapy involves the control of some aspect of the physiological effort of a patient, such as the use of a pacemaker to regulate the beating of the heart. Moreover, arrhythmia monitoring involves observing and detecting life-threatening conditions, such as myocardial infarction or heart attacks, in a patient. ECG signals are usually corrupted with various types of unwanted interference such as muscle artifacts, electrode artifacts, power line noise and respiration interference, and are distorted in such a way that it can be difficult to perform medical diagnosis, physiological therapy or arrhythmia monitoring. Consequently signal processing on ECGs is required to remove noise and interference signals for successful clinical applications. Existing signal processing techniques can remove some of the noise in an ECG signal, but are typically inadequate for extraction of the weak ECG components contaminated with background noise and for retention of various subtle features in the ECG. For example, the noise from the EMG usually overlaps the fundamental ECG cardiac components in the frequency domain, in the range of 0.01 Hz to 100 Hz. Simple filters are inadequate to remove noise which overlaps with ECG cardiac components. Sameni et al. have proposed a Bayesian filtering framework to resolve these problems, and this gives results which are clearly superior to the results obtained from application of conventional signal processing methods to ECG. However, a drawback of this Bayesian filtering framework is that it must run offline, and this of course is not desirable for clinical applications such as arrhythmia monitoring and physiological therapy, both of which re- quire online operation in near real-time. To resolve this problem, in this thesis we propose a dynamical model which permits the Bayesian filtering framework to function online. The framework with the proposed dynamical model has less than 4% loss in performance compared to the previous (offline) version of the framework. The proposed dynamical model is based on theory from fixed-lag smoothing

ASCNet-ECG: Deep Autoencoder based Attention aware Skip Connection network for ECG filtering

Currently, the telehealth monitoring field has gained huge attention due to its noteworthy use in day-to-day life. This advancement has led to an increase in the data collection of electrophysiological signals. Due to this advancement, electrocardiogram (ECG) signal monitoring has become a leading task in the medical field. ECG plays an important role in the medical field by analysing cardiac physiology and abnormalities. However, these signals are affected due to numerous varieties of noises, such as electrode motion, baseline wander and white noise etc., which affects the diagnosis accuracy. Therefore, filtering ECG signals became an important task. Currently, deep learning schemes are widely employed in signal-filtering tasks due to their efficient architecture of feature learning. This work presents a deep learning-based scheme for ECG signal filtering, which is based on the deep autoencoder module. According to this scheme, the data is processed through the encoder and decoder layer to reconstruct by eliminating noises. The proposed deep learning architecture uses a modified ReLU function to improve the learning of attributes because standard ReLU cannot adapt to huge variations. Further, a skip connection is also incorporated in the proposed architecture, which retains the key feature of the encoder layer while mapping these features to the decoder layer. Similarly, an attention model is also included, which performs channel and spatial attention, which generates the robust map by using channel and average pooling operations, resulting in improving the learning performance. The proposed approach is tested on a publicly available MIT-BIH dataset where different types of noise, such as electrode motion, baseline water and motion artifacts, are added to the original signal at varied SNR levels

Automatic detection of qt and related intervals

The objective of this thesis is divided into two main sections: The first section comprises of the development of the algorithm for the detection of the Q wave, Tmax afld the Tend to measure the intervals such as QT, QT corrected (QTc), RT, QTmax and RTmax respectively. The second section deals with the analysis of different variabilities including heart rate variability (HRV), QT, QTc, RT, QTmax and RTmax. Using the R wave points as reference points, the Q wave was detected by using the Differential Threshold Method (DTH). The Tmax was detected by using a search window on the derived signal of ECG starting from the R peak. The Tmax was detected by two different procedures. The first procedure was a combination of two different methods: the Least Squares Method (LSI) and the Threshold Method (TH) and the second procedure was based on the Differential Threshold Method (DTH). Once the points were detected, the relationship between the heart rate variability (HRV) and corrected QT variability along with other variabilities was studied in this research. The algorithm was validated on ten patients of five minute data segment of paced breathing at 6 breaths per minute and 12 breaths per minute respectively. The algorithm for the detection of the Q wave, T max and the Tend produced an overall success of 99.16% according to automatic verification of accuracy detection and 96.4% based on manual inspection. In this study, the duration of the QT interval was in the range of 450 to 500 milliseconds, which indicated normal duration of ventricular repolarization. The variability plots indicated similarity between HR variability and QT corrected variability

Simultaneous ambulatory cardial and oesophageal monitoring

A method for recording and analyzing patient signals is described. The electrocardiogram and the pH in the lower oesophagus are simultaneously monitored over a 24 hour period. Signals are recorded onto cassette tape for later analysis. The pH record is analyzed for reflux episodes which are correlated with patient symptoms. The corresponding ECG episodes are checked for ischemia which appears as depression of the ST segment

Investigation of Absolute Refractory Period Pacing to Prevent Lethal Arrhythmias in Humans

Sudden cardiac death (SCD) is a major health issue, being the commonest cause of natural death in the industrialised world. SCD frequently results from the development of erratic heart rhythms which are usually preceded by repolarisation alternans (RA). Previous studies suggest that the abolishment of RA may prevent the onset of arrhythmia. In a recent swine study, absolute refractory period pacing (ARPP) showed promising results in RA modulation. However, the cellular mechanisms underlying this therapy and its efficiency in human patients remains unclear. Single cell in silico modelling showed that ARPP might be used to both increase or decrease action potential duration (APD) with the degree of modulation depending mainly on stimulus duration, magnitude and coupling interval. ICaL, IKr and IK1 were the main currents involved, and conductance of Ito and ICaL strongly influenced results. APD alternans was successfully reduced in a population of alternating models. In vivo results obtained using an epicardial sock during cardiac surgery showed significant changes in repolarisation when applying ARPP. However, elevated morphological signal alterations led to question the results’ validity. The investigation of signal processing methodology led to the acknowledgement of high-pass filter interference in signal morphology due to the ARPP artefact, resulting in altered markers. Further in vivo data showed no significant effect of ARPP on local RT at the whole heart level. Small effects on RT, spectral method and Tend markers close to the pacing site were observed, suggesting a localised effect. One dimensional in silico modelling showed a rapid decline of the ARPP effect, being limited to around 10mm from the pacing site, correlating with the in vivo results. These results provide important new knowledge regarding the effects of ARPP in the human ventricle at the cellular and organ level. It also provides relevant information for further development, analysis and translation of pacing based therapies

Serum potassium concentration monitoring by ECG time warping analysis on the T wave

This doctoral thesis was developed within the joint Ph.D. program in biomedical engineering at Universitat Politècnica de Catalunya (Barcelona, Spain) and University of Zaragoza (Zaragoza, Spain) in the framework of Doctorats Industrials program co-financed by Laboratorios Rubió S.A. (Castellbisbal, Spain) and Agència de Gestió d’Ajuts Universitaris i de Recerca, Generalitat de Catalunya (Spain). This thesis was performed in partnership with the Nephrology ward from Hospital Clínico Universitario Lozano Blesa (Zaragoza, Spain) and in collaboration with Dr J. Ramírez from the William Harvey Research Institute, Queen Mary University of London (London, UK).End-stage renal disease (ESRD) patients demonstrate an increased incidence of sudden cardiac death (SCD) with declining kidney functioning as a consequence of blood potassium ([K+]) homeostasis impairment, which is restored by hemodialysis (HD) therapy. The clinically established method for the diagnosis of [K+] imbalance is blood tests, an invasive and costly procedure that limits continuous monitoring of ESRD patients. A non-invasive ambulatory index, able to quantify changes in [K+] level is an open issue. In this context, the electrocardiogram (ECG) and in particular, the T wave (TW) morphology, has been shown to be strongly correlated with [K+] imbalance. Therefore, the aim of this dissertation is to investigate and to propose TW-derived markers able to monitor changes in [K+] levels in ESRD patients undergoing HD. For that purpose, the time warping analysis, a technique that allows the comparison and quantification of differences between two different TW shapes, was investigated. The application of TW time warping based markers in monitoring [K+ ] variations (Δ [K+]) and the derivation of a heart-rate corrected marker is proposed and compared with respect to two well-established Δ [K+]-related TW-based indexes. All the markers are evaluated in a single lead approach and after having emphasised the TW energy content through spatial transformation by Principal Component Analysis (PCA). Results demonstrate that the proposed biomarkers outperform the already proposed indexes, also proving that the use of PCA transformed lead generates markers with a higher correlation with Δ [K+] than the single lead approach. The possibility to improve markers robustness in the case of low signal-to-noise ratio ECGs, by spatially transforming the signal maximising the beat-to-beat TW periodicity criteria through the so-called Periodic Component Analysis (pCA), is then explored. pCA-based markers show superior performance during and after the HD than those obtained by PCA suggesting improved stability for continuous Δ [K+] tracking. The thesis studies also the application of regressions models to quantify Δ [K+] from pCA-based time warping markers. The accuracy of the regression models is evaluated by correlation and estimation error between the actual and the corresponding model-estimated Δ [K+] values, and the smallest estimation error is found for quadratic regression models. Being the time warping derived markers sensitive to TW boundary delineation errors, which may endanger their prognostic power, the advantages of using a weighting stage is investigated for their robust computation. The performance of two weighting functions (WF)s is tested and compared with respect to the control no weighting case, in simulated scenarios and in real scenarios (i.e. for [K+] monitoring and SCD risk stratification). No improvements in [K+] monitoring are found, probably due to the considerable marked [K+]-induced TW morphological changes. On the contrary, both simulation tests and SCD risk stratification analysis show that the proposed WFs can enhance the robustness of TW time warping analysis against TW delineation errors. In conclusion, this Doctoral Thesis confirms the hypothesis that enhanced perforce in Δ [K+] tracking and quantification can be achieved by analysing the overall TW morphology by time warping analysis. The simplicity of the technology, together with its low cost and ease of acquisition, should provide a new opportunity for TW analysis to reach standard clinical practice. Moreover, the use of WFs to minimise the undesired effects of TW delineation errors on the computation of time warping markers revealed a noticeable improvement of the SCD risk stratification power of time warping derived indexes.Los pacientes con enfermedad renal en etapa terminal (ESRD) demuestran una mayor incidencia de muerte cardíaca súbita (SCD) tras el deterioro del funcionamiento renal como consecuencia del desequilibrio del potasio ([K+]) en sangre. Este último se restablece mediante la terapia de hemodiálisis (HD). El desequilibrio de [K+] se diagnostica a través del análisis de sangre, un procedimiento invasivo y costoso que limita la monitorización de los pacientes con ESRD. Se necesita un índice ambulatorio no invasivo, capaz de cuantificar los cambios en el nivel de [K+] (Δ [K+]). En este contexto, se ha demostrado que el electrocardiograma (ECG) y en particular la onda T (TW), están correlacionados con Δ [K+]. El objetivo de esta tesis es evaluar marcadores derivados de la TW capaces de monitorizar ¿[K+] en pacientes con ESRD sometidos a HD. Para ello, se aplicó el análisis time warping, una técnica que permite la comparación de dos formas diferentes de TW. En primer lugar, se evalúa la aplicación de marcadores basados en el time warping para el seguimiento de Δ [K+] así como la derivación de un marcador corregido por la frecuencia cardíaca, comparando los marcadores con respecto a dos índices basados en TW bien establecidos y relacionados con Δ [K+]. Todos los marcadores se evalúan en las derivaciones independientes y después de haber enfatizado el contenido de energía de TW a través del Análisis de Componentes Principales (PCA). Los resultados demuestran mejores prestaciones de los marcadores time warping respecto a los ya propuestos y que el uso de PCA genera marcadores con una correlación más alta con Δ [K+] respecto a las derivaciones independientes. A continuación, se explora la posibilidad de mejorar la robustez de los marcadores en el caso de ECG con una relación señal/ruido baja, maximizando la periodicidad de TW latido a latido mediante el Análisisde Componentes Periódicos (pCA). Los marcadores basados en pCA muestran un rendimiento superior durante y después de la HD que los obtenidos por PCA, lo que sugiere una estabilidad mejorada para el seguimiento continuo de Δ [K+]. Luego, se evalúan modelos de regresión para cuantificar [K+] a partir de marcadores basados en pCA. La precisión de los modelos de regresión se evalúa mediante el error de estimación entre valores reales de Δ [K+] y los correspondientes estimados por el modelo. Con el error de estimación más pequeño, el modelo cuadrático es el más adecuado para la cuantificación de [K+].Siendo el análisis time warping sensible a los errores de delineación de los límites de TW, lo que supone un riesgo para su poder pronóstico, se investigan las ventajas de usar una etapa de ponderación para el cálculo de marcadores time warping. El rendimiento de dos funciones de ponderación (WF) se prueba y se compara con respecto al caso de control sin ponderación, en escenarios simulados y en escenarios reales (para el seguimiento de [K+] y la estratificación del riesgo de SCD). No se encontraron mejoras en la monitorización de [K+] debido a los considerables cambios morfológicos de TW inducidos por Δ [K+]. Por otro lado, tanto las pruebas de simulación como el análisis de estratificación de riesgo de SCD muestran que los WF propuestos pueden mejorar la robustez del análisis time warping de TW contra los errores dedelineación de TW. En conclusión, esta tesis doctoral confirma la hipótesis de que se puede lograr un mejor seguimiento y cuantificación de Δ [K+] mediante el análisis de la morfología de TW mediante el análisis time warping. La simplicidad de la tecnología, junto con su bajo costo y facilidad de adquisición del ECG, debería brindar una nueva oportunidad para que el análisis de TW en la práctica clínica rutinaria. Además, el uso de WF para minimizar los efectos no deseados de errores de delineación de TW en el cálculo de los marcadores time warping reveló una mejora del poder de estratificación del riesgoEnginyeria biomèdic