Novel set of electrocardiographic left ventricular hypertrophy markers

La hipertrofia ventricular izquierda (HVI) induce cambios en las fases de despolarización y repolarización cardíaca. No obstante, pocos marcadores electrocardiográficos ncluyen parámetros de la repolarización en su diagnóstico. Objetivo de este estudio fue evaluar si la repolarización mejora el diagnóstico electrocardiográfico de la HVI. Material y Métodos: Se estudiamos los vectores QRS y T en el plano horizontal definido por las derivaciones precordiales V1 -V6 y el plano frontal definido por las derivaciones I-aVF en una serie de 40 pacientes con HVI y 34 sujetos control. Resultados: Los índices de HVI se construyeron como combinaciones de parámetros asociados a los vectores de despolarización y repolarización y su rendimiento se comparó con el rendimiento de los criterios de Sokolow-Lyon y Cornell. Los índices que incluyeron parámetros de repolarización mejoraron la sensibilidad de dichos criterios. Sobre los mismos, se construyeron estimativos a partir de medidas directas de amplitud del ECG, con el fin de obtener criterios de fácil medición en la práctica clínica. Para un 90% de especificidad, el mejor criterio, consistiendo en el voltaje de repolarización en la derivación V6 , produjo un 56% de sensibilidad, por encima del 30% de Sokolow-Lyon y el 40% de Cornell. Conclusiones: la repolarización contribuyó al diagnóstico electrocardiográfico de HVI y alentamos la búsqueda de nuevos índices en esta dirección.Left ventricular hypertrophy (LVH) induces changes in both depolarization and repolarization phases. However, little efforts were made to include repolarization features in its diagnosis. The aim of this study was to evaluate whether the repolarization improves ECG diagnosis of LVH. Methods: To do this, we studied the QRS and T vectors in the horizontal plane (HP) defined by the chest leads V1 -V6 and the frontal plane (PF) defined by the limb leads I-aVF in a series of 40 LVH patients and 34 control subjects. Results: LVH indices were constructed as combinations of parameters associated with depolarization and repolarization vectors and their performance was compared to the performance of Sokolow-Lyon and Cornell. Those indices, which included repolarization parameters, improved the sensitivity of these criteria. The latter were selected to construct estimates from direct measurements of ECG amplitudes, in order to obtain easy measurement criteria in the clinical practice. The best estimate, consisting of the repolarization voltage on lead V6 , produced a 56% sensitivity at 90% specificity, above Sokolow-Lyon (sensitivity 30%) and Cornell (sensitivity 40%). Conclusions: repolarization contributed to the electrocardiographic diagnosis of LVH and we encourage the search for new indices in this direction.Fil: Bonomini, Maria Paula. Universidad de Buenos Aires; ArgentinaFil: Ingallina, Fernando Juan. Universidad de Buenos Aires; ArgentinaFil: Barone, Valeria. Universidad de Buenos Aires; ArgentinaFil: Antonucci, Ricardo. Universidad de Buenos Aires; ArgentinaFil: Arini, Pedro David. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderon; Argentin

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

The effect of breath pacing on task switching and working memory

The cortical and subcortical circuit regulating both cognition and cardiac autonomic interactions are already well established. This circuit has mainly been analyzed from cortex to heart. Thus, the heart rate variability (HRV) is usually considered a reflection of cortical activity. In this paper, we investigate whether HRV changes affect cortical activity. Short-term local autonomic changes were induced by three breathing strategies: spontaneous (Control), normal (NB) and slow paced breathing (SB). We measured the performance in two cognition domains: executive functions and processing speed. Breathing maneuvres produced three clearly differentiated autonomic states, which preconditioned the cognitive tasks. We found that the SB significantly increased the HRV low frequency (LF) power and lowered the power spectral density (PSD) peak to 0.1Hz. Meanwhile, executive function was assessed by the working memory test, whose accuracy significantly improved after SB, with no significant changes in the response times. Processing speed was assessed by a multitasking test. Consistently, the proportion of correct answers (success rate) was the only dependent variable affected by short-term and long-term breath pacing. These findings suggest that accuracy, and not timing of these two cognitive domains would benefit from short-term SB in this study population.Fil: Bonomini, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaFil: Calvo, Mikel Val. Universidad Nacional de Educación a Distancia; España. Universidad Politécnica de Cartagena; EspañaFil: Morcillo, Alejandro Diaz. Universidad Politécnica de Cartagena; EspañaFil: Segovia, Maria Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Vicente, Jose Manuel Ferrandez. Universidad Politécnica de Cartagena; EspañaFil: Fernández Jover, Eduardo. Universidad de Miguel Hernández; Españ

Taxifolin stability: In silico prediction and in vitro degradation with HPLC-UV/UPLC–ESI-MS monitoring

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Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Modulation of spatial variance of ventricular repolarization after myocardial infarction

Myocardial infarction (MI) alters spatial features of thesurface electrocardiogram. Spatial variance of the T-wave (SVT )describes the interlead dispersion about a mean T-wave morphology. SVTwas linked to arrhythmiavulnerability and sudden cardiac death. In this work, we studied theevolution of SVT over the healing (MI7, up to 7 days after MI) and healed(MI60, from 60 days on post-MI) stages following MI. A control group(n=49) was compared to paired MI7 and MI60 groups (n=39). Fiverepresentative sets of frontal and precordial leads were analyzed: I-IIIII,V1-V2-V3, V4-V5-V6, aVF -V2-V5 and II-aVF -V5. SVT index signicantlyincreased at MI7 (p<0.05) in four out of ve sets and returned towardscontrol values at MI60 (p=NS) on patients without ventricular tachycardiaand/orventricular brillation (VT/VF). The preferential combination of ECG leadsresulted aVF -V2-V5, since it showed the strongest modulation over postMI time. In order totest whether such a modulation was maintained on the presence of VT=VF),recordings from VT=VF patients were also analyzed. SVT modulation waslost in the VT=VF group, signicantly increasing from controls at MI60(p<0.05) for all tested sets of leads. In conclusion, SVT modulation overMI7 and MI60 would signal a good recovery from MI, whereas lack of thismodulation could herald a VT=VF event.Fil: Bonomini, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderon; Argentina. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; ArgentinaFil: Arini, Pedro David. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderon; Argentina. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; Argentin

Spatial variance in the 12-lead ECG and mechanical dyssynchrony

Introduction: Electrical transmission disorders have a deleterious effect on cardiac depolarization, resulting in a disorganized ventricular contraction that reduces global mechanical efficiency; this mechanical dyssynchrony can be corrected by cardiac resynchronization therapy. However, despite adjustments in the electrical criteria selection of QRS for the recognition of mechanical dyssynchrony, a significant proportion of patients do not currently respond to this therapy. Purpose: To find if a new predictor of dyssynchrony, the electrocardiogram spatial variance, is a better marker of mechanical dyssynchrony than QRS duration. Methods: Forty-seven electrocardiograms and 47 strain (2D speckle tracking) echocardiograms were prospectively collected simultaneously in consecutive, non-selected patients; the left ventricular mechanical dispersion was measured in all the cases. The electrocardiographic analysis of variance was made with a digital superposition of the electrocardiographic leads and generates different indexes of variance of both QRS complex and repolarization phase. Results: ROC analysis probed that the best area under the curve (AUC) value correlated with left ventricular mechanical dispersion and was obtained combining several spatial variance markers (considering depolarization and repolarization spatial variance together; AUC = 0.97); the same analysis using the QRS duration versus mechanical dispersion showed a significantly lower AUC value (AUC = 0.64). Conclusion: Spatial variance combining depolarization and repolarization markers is a superior predictor of left ventricular mechanical dispersion than QRS duration.Fil: Bonomini, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaFil: Villaroel Abrego, Hugo. Universidad del Salvador. Facultad de Medicina; ArgentinaFil: Garillo, Raúl. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires"; Argentin

Intracardiac Pressure - Volume Diagrams and Their Links with Thermodynamics

The main objective of this column is to historically connect the pressure–volume diagram (PVD) of the heat mechanical engines and that of the heart—a natural chemical engine—both types being generators of useful work.Fil: Valentinuzzi, Max E.. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Tucumán; ArgentinaFil: Bonomini, Maria Paula. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; ArgentinaFil: Arini, Pedro David. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin