Interatrial conduction block with retrograde activation of the left atrium and paroxysmal supraventricular tachyarrhythmias: influence of preventive antiarrhythmic treatment.

Patients with advanced interatrial conduction block with retrograde activation to the left atrium present a high incidence of supraventricular tachyarrhythmias. We report the value of preventive antiarrhythmic treatment in these patients

P-Wave Data Augmentation for Bayès Syndrome Detection

Resulta interesante detectar en una etapa temprana el Síndrome de Bayés debido a sus asociaciones con múltiples afecciones médicas. En el ámbi-to de esta investigación se presenta una estrategia de aumentado de datos de muestras de ECGs brindadas por el equipo del Dr Antonio Bayes. Sobre estos datos se aplicaron dos técnicas de clustering: K-Means++ (dos implementacio-nes diferentes) y FAUM. El método se aplicó mediante la herramienta Matlab y también mediante la provista por FAUM. Además, se utilizó FAUM estableciendo una cantidad fija de clusters. Tanto K-Means++ como FAUM se aplica-ron sobre las muestras de cada señal. Inicialmente se contaba con 49 muestras de señales y aplicando las técnicas de aumentado de datos se lograron obtener 2113 señales. Se destaca de los métodos mencionados, la implementación de K-Means++ en el análisis de los agrupamientos. Se logró un F1-Score de 94% en una de sus implementaciones. Los resultados alcanzados son alentadores, ya que el incremento en el conjunto de datos logrado debido al aumentado, hace posible continuar atacando este problema con la aplicación de métodos supervisados que requieran gran cantidad de muestras, como por ejemplo las de aprendizaje profundo.Sociedad Argentina de Informática e Investigación Operativ

Digitalización de imágenes de ECG para la detección del síndrome de Bayés

Bayes Syndrome is a recently recognized by medical community sickness. This desease has been studied in the last decades by its discoverer, MD. Antonio Bayés de Luna. Since several works shows that this desease is related to multiple symptoms, an early detection is considered relevant. Given that digital support of the EKG signal is mandatory for its analysis by a computer algorithm and considering that even with the technological advances, a big number of health institutions rely on paper or image digitalized support por EKGs. Hence, an image digitalization method that preserves the signal features that are relevant to diagnose the Bayes Syndrome is needed. In this paper, some alternatives of digitalization are analyzed for a representative dataset. Results are promising and shows that developed digitalization algorithm could be used for the further project stages that involves signal processing and classification.IX Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Digitalización de imágenes de ECG para la detección del síndrome de Bayés

Bayes Syndrome is a recently recognized by medical community sickness. This desease has been studied in the last decades by its discoverer, MD. Antonio Bayés de Luna. Since several works shows that this desease is related to multiple symptoms, an early detection is considered relevant. Given that digital support of the EKG signal is mandatory for its analysis by a computer algorithm and considering that even with the technological advances, a big number of health institutions rely on paper or image digitalized support por EKGs. Hence, an image digitalization method that preserves the signal features that are relevant to diagnose the Bayes Syndrome is needed. In this paper, some alternatives of digitalization are analyzed for a representative dataset. Results are promising and shows that developed digitalization algorithm could be used for the further project stages that involves signal processing and classification.IX Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Digitalización de imágenes de ECG para la detección del síndrome de Bayés

Bayes Syndrome is a recently recognized by medical community sickness. This desease has been studied in the last decades by its discoverer, MD. Antonio Bayés de Luna. Since several works shows that this desease is related to multiple symptoms, an early detection is considered relevant. Given that digital support of the EKG signal is mandatory for its analysis by a computer algorithm and considering that even with the technological advances, a big number of health institutions rely on paper or image digitalized support por EKGs. Hence, an image digitalization method that preserves the signal features that are relevant to diagnose the Bayes Syndrome is needed. In this paper, some alternatives of digitalization are analyzed for a representative dataset. Results are promising and shows that developed digitalization algorithm could be used for the further project stages that involves signal processing and classification.IX Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Detrended Fluctuation Analysis of Heart Rate by Means of Symbolic Series

Abstract Detrended fluctuation analysis (DFA) Introduction Fluctuations of time intervals between consecutive heartbeats exhibit a complex dynamics, which is influenced by the activity of many regulatory systems interacting over a wide range of time or space scales The study of heart rate time series using detrended fluctuation analysis (DFA) has been shown to be a useful tool for diagnostic in patients with cardiac diseases Previous studies Variability of heart rate fluctuations has been also studied by using RR increment series ( RR series) and, particularly, by using series constructed with the magnitude and the sign of RR series In this work, an alternative methodology was introduced in order to improve the statistical differentiation between risk groups of suffering cardiac death, by applying DFA over RR series. The methodology considered a symbolic transformation of RR series by means of an alphabet with four symbols. Results were compared with those calculated over original RR series and, series corresponding with magnitude and sign of RR series. Methods Analyzed databas

Genome-wide association study of primary tooth eruption identifies pleiotropic loci associated with height and craniofacial distances

Twin and family studies indicate that the timing of primary tooth eruption is highly heritable, with estimates typically exceeding 80%. To identify variants involved in primary tooth eruption we performed a population based genome-wide association study of ‘age at first tooth’ and ‘number of teeth’ using 5998 and 6609 individuals respectively from the Avon Longitudinal Study of Parents and Children (ALSPAC) and 5403 individuals from the 1966 Northern Finland Birth Cohort (NFBC1966). We tested 2,446,724 SNPs imputed in both studies. Analyses were controlled for the effect of gestational age, sex and age of measurement. Results from the two studies were combined using fixed effects inverse variance meta-analysis. We identified a total of fifteen independent loci, with ten loci reaching genome-wide significance (p<5x10−8) for ‘age at first tooth’ and eleven loci for ‘number of teeth’. Together these associations explain 6.06% of the variation in ‘age of first tooth’ and 4.76% of the variation in ‘number of teeth’. The identified loci included eight previously unidentified loci, some containing genes known to play a role in tooth and other developmental pathways, including a SNP in the protein-coding region of BMP4 (rs17563, P= 9.080x10−17). Three of these loci, containing the genes HMGA2, AJUBA and ADK, also showed evidence of association with craniofacial distances, particularly those indexing facial width. Our results suggest that the genome-wide association approach is a powerful strategy for detecting variants involved in tooth eruption, and potentially craniofacial growth and more generally organ development

International criteria for electrocardiographic interpretation in athletes: Consensus statement.

Sudden cardiac death (SCD) is the leading cause of mortality in athletes during sport. A variety of mostly hereditary, structural or electrical cardiac disorders are associated with SCD in young athletes, the majority of which can be identified or suggested by abnormalities on a resting 12-lead electrocardiogram (ECG). Whether used for diagnostic or screening purposes, physicians responsible for the cardiovascular care of athletes should be knowledgeable and competent in ECG interpretation in athletes. However, in most countries a shortage of physician expertise limits wider application of the ECG in the care of the athlete. A critical need exists for physician education in modern ECG interpretation that distinguishes normal physiological adaptations in athletes from distinctly abnormal findings suggestive of underlying pathology. Since the original 2010 European Society of Cardiology recommendations for ECG interpretation in athletes, ECG standards have evolved quickly, advanced by a growing body of scientific data and investigations that both examine proposed criteria sets and establish new evidence to guide refinements. On 26-27 February 2015, an international group of experts in sports cardiology, inherited cardiac disease, and sports medicine convened in Seattle, Washington (USA), to update contemporary standards for ECG interpretation in athletes. The objective of the meeting was to define and revise ECG interpretation standards based on new and emerging research and to develop a clear guide to the proper evaluation of ECG abnormalities in athletes. This statement represents an international consensus for ECG interpretation in athletes and provides expert opinion-based recommendations linking specific ECG abnormalities and the secondary evaluation for conditions associated with SCD

Sudden cardiac death and pump failure death prediction in chronic heart failure by combining ECG and clinical markers in an integrated risk model

BACKGROUND: Sudden cardiac death (SCD) and pump failure death (PFD) are common endpoints in chronic heart failure (CHF) patients, but prevention strategies are different. Currently used tools to specifically predict these endpoints are limited. We developed risk models to specifically assess SCD and PFD risk in CHF by combining ECG markers and clinical variables. METHODS: The relation of clinical and ECG markers with SCD and PFD risk was assessed in 597 patients enrolled in the MUSIC (MUerte Súbita en Insuficiencia Cardiaca) study. ECG indices included: turbulence slope (TS), reflecting autonomic dysfunction; T-wave alternans (TWA), reflecting ventricular repolarization instability; and T-peak-to-end restitution (ΔαTpe) and T-wave morphology restitution (TMR), both reflecting changes in dispersion of repolarization due to heart rate changes. Standard clinical indices were also included. RESULTS: The indices with the greatest SCD prognostic impact were gender, New York Heart Association (NYHA) class, left ventricular ejection fraction, TWA, ΔαTpe and TMR. For PFD, the indices were diabetes, NYHA class, ΔαTpe and TS. Using a model with only clinical variables, the hazard ratios (HRs) for SCD and PFD for patients in the high-risk group (fifth quintile of risk score) with respect to patients in the low-risk group (first and second quintiles of risk score) were both greater than 4. HRs for SCD and PFD increased to 9 and 11 when using a model including only ECG markers, and to 14 and 13, when combining clinical and ECG markers. CONCLUSION: The inclusion of ECG markers capturing complementary pro-arrhythmic and pump failure mechanisms into risk models based only on standard clinical variables substantially improves prediction of SCD and PFD in CHF patients