Threshold calculation for R wave detection in complex cardiac

La señal electrocardiográfica es una señal eléctrica con una amplitud de 1 mV aproximadamente y componentes espectrales entre 0,7 y 100 Hz. El electrocardiograma representa el comportamiento eléctrico del corazón humano y está conformado principalmente por un grupo de ondas denominado el complejo cardiaco. Las ondas que componen el complejo cardiaco son: P, Q, R, S y T. La onda R corresponde a la onda positiva de mayor amplitud de la señal electrocardiográfica y el tiempo entre cada onda permite el cálculo de la frecuencia cardiaca instantánea. Para el cálculo del tiempo entre cada onda R es necesario la implementación de un sistema de filtrado que permita una atenuación de las componentes espectrales que no pertenecen a esta forma de onda. Posteriormente se procede a un proceso de umbralización que consiste en generar una señal binaria que toma el valor de uno en la muestra que registra la existencia de una onda R y cero en las demás muestras. El objetivo de este trabajo es presentar los resultados obtenidos al implementar un algoritmo para el establecer del umbral basado en el cálculo del histograma de la señal electrocardiográfica que ha sido previamente tratada a través de un sistema basado en bancos de filtros.The electrocardiographic signal is an electrical signal and its amplitude is 1 mV approximately and spectral components between 0.7 and 100 Hz. The electrocardiographic signal represents the electrical behavior of the human heart and it has a group of waves called the cardiac complex. Waves comprising the cardiac complex are: P, Q, R, S and T. The R-wave corresponds to the positive wave of greater amplitude of the electrocardiographic signal and the time between each wave allows the calculation of instantaneous heart rate. The calculation of the time between R wave requires implementation of a filtering system that allows an attenuation of the spectral components that do not belong to this waveform. Then proceed to a thresholding process that consists of generating a binary signal which takes the value of one in the sample that records the wave R and zero in the other samples. The principal goal of this paper is to present the results to implement an algorithm for setting the threshold based on the calculation of the histogram of the electrocardiographic signal that has been previously addressed through a system based on filter banks

Electrocardiography in horses, part 2: how to read the equine ECG

The equine practitioner is faced with a wide variety of dysrhythmias, of which some are physiological. The recording of an exercise electrocardiogram (ECG) can help distinguish between physiological and pathological dysrhythmias, underlining the importance of exercise recordings. The evaluation of an ECG recording should be performed in a highly methodical manner in order to avoid errors. Each P wave should be followed by a QRS complex, and each QRS complex should be preceded by a P wave. The classification of dysrhythmias according to their origin helps to understand the associated changes on the ECG. In this respect, sinoatrial nodal (SA nodal), atrial myocardial, atrioventricular nodal (AV nodal) and ventricular myocardial dysrhythmias can be distinguished. Artefacts on the ECG can lead to misinterpretations. Recording an ECG of good quality is a prerequisite to prevent misinterpretations, but artefacts are almost impossible to avoid when recording during exercise. Changes in P or T waves during exercise also often lead to misinterpretations, however they have no clinical significance

Effects of early afterdepolarizations on excitation patterns in an accurate model of the human ventricles

Early Afterdepolarizations, EADs, are defined as the reversal of the action potential before completion of the repolarization phase, which can result in ectopic beats. However, the series of mechanisms of EADs leading to these ectopic beats and related cardiac arrhythmias are not well understood. Therefore, we aimed to investigate the influence of this single cell behavior on the whole heart level. For this study we used a modified version of the Ten Tusscher-Panfilov model of human ventricular cells (TP06) which we implemented in a 3D ventricle model including realistic fiber orientations. To increase the likelihood of EAD formation at the single cell level, we reduced the repolarization reserve (RR) by reducing the rapid delayed rectifier Potassium current and raising the L-type Calcium current. Varying these parameters defined a 2D parametric space where different excitation patterns could be classified. Depending on the initial conditions, by either exciting the ventricles with a spiral formation or burst pacing protocol, we found multiple different spatio-temporal excitation patterns. The spiral formation protocol resulted in the categorization of a stable spiral (S), a meandering spiral (MS), a spiral break-up regime (SB), spiral fibrillation type B (B), spiral fibrillation type A (A) and an oscillatory excitation type (O). The last three patterns are a 3D generalization of previously found patterns in 2D. First, the spiral fibrillation type B showed waves determined by a chaotic bi-excitable regime, i.e. mediated by both Sodium and Calcium waves at the same time and in same tissue settings. In the parameter region governed by the B pattern, single cells were able to repolarize completely and different (spiral) waves chaotically burst into each other without finishing a 360 degree rotation. Second, spiral fibrillation type A patterns consisted of multiple small rotating spirals. Single cells failed to repolarize to the resting membrane potential hence prohibiting the Sodium channel gates to recover. Accordingly, we found that Calcium waves mediated these patterns. Third, a further reduction of the RR resulted in a more exotic parameter regime whereby the individual cells behaved independently as oscillators. The patterns arose due to a phase-shift of different oscillators as disconnection of the cells resulted in continuation of the patterns. For all patterns, we computed realistic 9 lead ECGs by including a torso model. The B and A type pattern exposed the behavior of Ventricular Tachycardia (VT). We conclude that EADs at the single cell level can result in different types of cardiac fibrillation at the tissue and 3D ventricle level

Methods of Assessment and Clinical Relevance of QT Dynamics

The dependence on heart rate of the QT interval has been investigated for many years and several mathematical formulae have been proposed to describe the QT interval/heart rate (or QT interval/RR interval) relationship. While the most popular is Bazett’s formula, it overcorrects the QT interval at high heart rates and under-corrects it at slow heart rates. This formulae and many others similar ones, do not accurately describe the natural behaviour of the QT interval. The QT interval/RR interval relationship is generally described as QT dynamics. In recent years, several methods of its assessment have been proposed, the most popular of which is linear regression. An increased steepness of the linear QT/RR slope correlates with the risk of arrhythmic death following myocardial infarction. It has also been demonstrated that the QT interval adapts to heart rate changes with a delay (QT hysteresis) and that QT dynamics parameters vary over time. New methods of QT dynamics assessment that take into account these phenomena have been proposed. Using these methods, changes in QT dynamics have been observed in patients with advanced heart failure, and during morning hours in patients with ischemic heart disease and history of cardiac arrest. The assessment of QT dynamics is a new and promising tool for identifying patients at increased risk of arrhythmic events and for studying the effect of drugs on ventricular repolarisation

Extracting fetal heart beats from maternal abdominal recordings: Selection of the optimal principal components

This study presents a systematic comparison of different approaches to the automated selection of the principal components (PC) which optimise the detection of maternal and fetal heart beats from non-invasive maternal abdominal recordings. A public database of 75 4-channel non-invasive maternal abdominal recordings was used for training the algorithm. Four methods were developed and assessed to determine the optimal PC: (1) power spectral distribution, (2) root mean square, (3) sample entropy, and (4) QRS template. The sensitivity of the performance of the algorithm to large-amplitude noise removal (by wavelet de-noising) and maternal beat cancellation methods were also assessed. The accuracy of maternal and fetal beat detection was assessed against reference annotations and quantified using the detection accuracy score F1 [2*PPV*Se / (PPV + Se)], sensitivity (Se), and positive predictive value (PPV). The best performing implementation was assessed on a test dataset of 100 recordings and the agreement between the computed and the reference fetal heart rate (fHR) and fetal RR (fRR) time series quantified. The best performance for detecting maternal beats (F1 99.3%, Se 99.0%, PPV 99.7%) was obtained when using the QRS template method to select the optimal maternal PC and applying wavelet de-noising. The best performance for detecting fetal beats (F1 89.8%, Se 89.3%, PPV 90.5%) was obtained when the optimal fetal PC was selected using the sample entropy method and utilising a fixed-length time window for the cancellation of the maternal beats. The performance on the test dataset was 142.7 beats2/min2 for fHR and 19.9 ms for fRR, ranking respectively 14 and 17 (out of 29) when compared to the other algorithms presented at the Physionet Challenge 2013

Analysis of the high-frequency content in human qrs complexes by the continuous wavelet transform: An automatized analysis for the prediction of sudden cardiac death

Background: Fragmentation and delayed potentials in the QRS signal of patients have been postulated as risk markers for Sudden Cardiac Death (SCD). The analysis of the high-frequency spectral content may be useful for quantification. Methods: Forty-two consecutive patients with prior history of SCD or malignant arrhythmias (patients) where compared with 120 healthy individuals (controls). The QRS complexes were extracted with a modified Pan-Tompkins algorithm and processed with the Continuous Wavelet Transform to analyze the high-frequency content (85–130 Hz). Results: Overall, the power of the high-frequency content was higher in patients compared with controls (170.9 vs. 47.3 103nV2Hz−1; p = 0.007), with a prolonged time to reach the maximal power (68.9 vs. 64.8 ms; p = 0.002). An analysis of the signal intensity (instantaneous average of cumulative power), revealed a distinct function between patients and controls. The total intensity was higher in patients compared with controls (137.1 vs. 39 103nV2Hz−1s−1; p = 0.001) and the time to reach the maximal intensity was also prolonged (88.7 vs. 82.1 ms; p < 0.001). Discussion: The high-frequency content of the QRS complexes was distinct between patients at risk of SCD and healthy controls. The wavelet transform is an efficient tool for spectral analysis of the QRS complexes that may contribute to stratification of risk

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 128, May 1974

This special bibliography lists 282 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1974

Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis

Recently, different algorithms have been suggested to improve Sample Entropy (SE) performance. Although new methods for calculating SE have been proposed, so far improving the efficiency (computational time) of SE calculation methods has not been considered. This research shows such an analysis of calculating a correlation between Electroencephalogram(EEG) and Heart Rate Variability(HRV) based on their SE values. Our results indicate that the parsimonious outcome of SE calculation can be achieved by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain.Peer reviewe