Narrow Results

In this work, the authors use computer modeling to theoretically investigate the mechanisms involved in figure-of-eight reentry during acute regional myocardial ischemia, a pattern of excitation which may lead to ventricular fibrillation and sudden cardiac death. For this purpose, a modified version of the Luo–Rudy dynamic model for the action potential and ionic currents has been used, together with a two-dimensional model of the regionally ischemic ventricle. The virtual tissue comprises several realistically dimensioned and located transitional border zones for hyperkalemia, hypoxia and acidosis, simulating the substrate heterogeneity created by acute ischemia. Different types of patterns of excitation following the delivery of a premature stimulus were obtained, including figure-of-eight reentry. Action potentials and selected ionic currents which explain the reentry process are analyzed. The effect of the degree of ATP-sensitive current activation in the vulnerability to reentry is also studied. The results are in accordance with experimental observations, and demonstrate the ability of second-generation mathematical models to analyze and explain the mechanisms involved in ischemic reentry.

EADs were produced by lowered g_K,s in Luo–Rudy cell models, and could be triggered by the addition of a small noise current. Endogenous EADs, produced in a fully deterministic, or a noise driven, one-dimensional virtual tissue could initiate ectopic (unidirectional or bidirectional) propagation, and retrograde, unidirectional propagation that is the analogue of reentry in a two-dimensional tissue. Such potentially reentrant activity occurred with a low probability, for a very narrow parameter range.

The model of the cardiac tissue as a conductive system with two interacting pacemakers and a refractory time is proposed. In the parametric space of the model the phase locking areas are investigated in detail. The obtained results make possible to predict the behavior of excitable systems with two pacemakers, depending on the type and intensity of their interaction and the initial phase. Comparison of the described phenomena with intrinsic pathologies of cardiac rhythms is given.

Sudden cardiac death is mainly caused by arrhythmogenesis. For a functional abnormal heart, such as an ischemic heart, the probability of arrhythmia occurring is greatly increased. During myocardial ischemia, re-entry is prone to degenerate into ventricular fibrillation (VF). Therefore it has important meaning to investigate the intricate mechanisms underlying VF under an ischemic condition in order to better facilitate therapeutic interventions. In this paper, to analyze the functional influence of acute global ischemia on cardiac electrical activity and subsequently on re-entrant arrhythmogenesis, we take into account three main pathophysiological consequences of ischemia: hyperkalaemia, acidosis, and anoxia, and develop a 3D human ventricular ischemic model that combines a detailed biophysical description of the excitation kinetics of human ventricular cells with an integrated geometry of human ventricular tissue which incorporates fiber direction anisotropy and the stimulation activation sequence. The results show that under acute global ischemia, the tissue excitability and the slope of ventricular cellular action potential duration restitution (APDR) are greatly decreased. As a result, the complexity of VF activation patterns is reduced. For the three components of ischemia, hyperkalaemia is the dominant contributor to the stability of re-entry under acute global ischemia. Increasing [K⁺]_o acts to prolong the cell refractory period, reduce the tissue excitability and slow the conduction velocity. Our results also show that VF can be eliminated by decreasing cellular excitability, primarily by elevating the concentration value of extracellular K⁺.

Healthcare is indeed an inevitable part of life for everyone. In recent days, most of the deaths have been happening because of noncommunicable diseases. Despite the significant advancements in medical diagnosis, cardiovascular diseases are still the most prominent cause of mortality worldwide. With recent innovations in Machine Learning (ML) and Deep Learning (DL) techniques, there has been an enormous surge in the clinical field, especially in cardiology. Several ML and DL algorithms are useful for predicting cardiovascular diseases. The predictive capability of these algorithms is promising for various cardiovascular diseases like coronary artery disease, arrhythmia, heart failure, and others. We also review the lung interactions during heart disease. After the study of various ML and DL models with different datasets, the performance of the various strategies is analyzed. In this study, we focused on the analysis of various ML and DL algorithms to diagnose cardiovascular disease. In this paper, we also presented a detailed analysis of heart failure detection and various risk factors. This paper may be helpful to researchers in studying various algorithms and finding an optimal algorithm for their dataset.

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Human heart is elegantly articulated to mechanically contract in response to electrical excitation. Cardiac electrical activity may be described as a multiscale process from sub-cellular to cellular to tissue level. Ion movement at the cellular level through ion channels results in an action potential that propagates as an electrical wave in tissue. A first-principles-based mathematical description of the cellular-level dynamics of cardiac electrophysiological behavior provides a better understanding of the functioning of the heart.

The mathematical models describing cellular dynamics often involve a coupled system of ordinary differential equations (ODEs) with variables including transmembrane voltage, ion concentrations and ion channel gating variables, whose evolution describes activation/inactivation of ion channels. In this study we discuss a mathematical model of the human ventricular myocyte (O’Hara–Rudy model), defined as a system of 41 ODEs, with variables involving membrane voltage, 29 gating variables describing activation of Na${}^{+}$, K${}^{+}$, Ca${}^{2+}$ channels, 11 variables describing ion concentrations and Ca${}^{2+}$ related flux. Runge–Kutta method with variable order and variable time step was adopted to solve the system numerically. We discuss the action potential (AP) profile of a healthy human ventricular myocyte and corresponding dominant ionic currents. We present a phase plot that describes the change in voltage and its rate as the system evolves over time. The phase plot seems to provide more details of the underlying events than the AP curve.

This work aims at presenting a methodology for electrocardiogram (ECG)-based arrhythmia disease detection using genetic algorithm (GA)-optimized k-means clustering. The open-source ECG data from MIT-BIH arrhythmia database and MIT-BIH normal sinus rhythm database are subjected to a sequence of steps including segmentation using R-point detection, extraction of features using principal component analysis (PCA), and pattern classification. Here, the classical classifiers viz., k-means clustering, error back propagation neural network (EBPNN), and support vector machine (SVM) have been initially attempted and subsequently m-fold (m = 3) cross validation is used to reduce the bias during training of the classifier. The average classification accuracy is computed as the average over all the three folds. It is observed that EBPNN and SVM with different order polynomial kernel provide significant accuracies in comparison with k-means one. In fact, the parameters (centroids) of k-means algorithm are locally optimized by minimizing its objective function. In order to overcome this limitation, a global optimization technique viz., GA is suggested here and implemented to find more robust parameters of k-means clustering. Finally, it is shown that GA-optimized k-means algorithm enhances its accuracy to those of other classifiers. The results are discussed and compared. It is concluded that the GA-optimized k-means algorithm is an alternate approach for classification whose accuracy will be near to that of supervised (viz., EBPNN and SVM) classifiers.

Heart rate variability (HRV) is the temporal variation between sequences of consecutive heartbeats. Chaos and fractal-based measurements have been widely used for quantifying the HRV for cardiac risk stratification purposes. In this paper, five different sets of HRVs, viz., normal sinus rhythm (NSR), congestive heart failure (CHF), cardiac arrhythmia suppression trial (CAST), supra ventricular tachyarrhythmia (SVTA) and atrial fibrillation (AF), have been analysed using nonlinear parameters to fix the ranges of each parameter. Data were downloaded from the PhysioNet database with 15 sets in each case. The parameters used for analysis were Poincare plot measures: SD1, SD2 and SD12, largest Lyapunov exponent (LLE), correlation dimension (CD); recurrence plot measures: recurrence rate (REC), determinism (DET), mean diagonal length (L_mean), maximal diagonal length (L_max) and entropy (ENTR); detrended fluctuation analysis measures: scaling exponent (α) and fractal dimension (FD); sample entropy (SampEn); and approximate entropy (ApEn). Analysis of variance (ANOVA) was done for confirming the differences in parameter values between various cases. All parameters except LLE showed a significant statistical difference for different cases.

A method for automatic classification of Arrhythmias from Electrocardiogram based on features generated from a new Continuous Wavelet Transform (CWT) is presented in this paper. The classification performance was studied using the most commonly available database, the MIT-BIH arrhythmia database. The new wavelet for classification was evolved using Genetic Algorithm (GA). The optimum wavelet for classification was obtained after several runs of the GA algorithm. The class labeling was followed according to the Association for the Advancement of Medical Instrumentation (AAMI). The wavelet scales corresponding to the different frequency levels giving maximum classification performance was identified. Probabilistic Neural Network (PNN) classifier was used for classification. The proposed classification system offered an overall sensitivity of 97% for Normal beats (N), 75% for Supraventricular beats (S_v) and 93% for Ventricular beats (V) which is better than existing results reported in literature. This technique could exclusively identify some of the isolated abnormalities compared to other results reported.

This study aims to present an efficient model for autodetection of cardiac arrhythmia by the diagnosis of self-affinity and identification of governing processes of a number of Electrocardiogram (ECG) signals taken from MIT-BIH database. In this work, the proposed model includes statistical methods to find the diagnosis pattern for detecting cardiac abnormalities which is useful for the computer aided system for arrhythmia detection. First, the Rescale Range (R/S) analysis has been employed for ECG signals to understand the scaling property of ECG signals. The value of Hurst exponent identifies the presence of abnormality in ECG signals taken for consideration with 92.58% accuracy. In this study, Higuchi method which deals with unifractality or monofractality of signals has been applied and it is found that unifractality is sufficient to detect arrhythmia with 91.61% accuracy. The Multifractal Detrended Fluctuation Analysis (MFDFA) has been used over the present signals to identify and confirm the multifractality. The nature of multifractality is different for arrhythmia patients and normal heart condition. The multifractal analysis is useful to detect abnormalities with 93.75% accuracy. Finally, the autocorrelation analysis has been used to identify the prevalent governing process in the present arrhythmic ECG signals and study confirms that all the signals are governed by stationary autoregressive methods of certain orders. In order to increase the overall efficiency, this present model deals with analyzing all the statistical features extracted from different statistical techniques for a large number of ECG signals of normal and abnormal heart condition. Finally, the result of present analysis altogether possibly indicates that the proposed model is efficient to detect cardiac arrhythmia with 99.3% accuracy.

Electrocardiogram (ECG) signals represent a useful information source about the rhythm and the functioning of the heart. Any disturbance in the heart's normal rhythmic contraction is called an arrhythmia. Analysis of Electrocardiogram signals is the most effective available method for diagnosing cardiac arrhythmias. Computer based classification of ECG provides higher accuracy and offer a potential of an affordable cardiac abnormality mass screening. The empirical mode decomposition is performed on various arrhythmia signals and different levels of intrinsic mode functions (IMF) are obtained. Singular value decomposition (SVD) is used to extract features from the IMF and classification is performed using support vector machine. This method is more efficient for classification of ECG signals and at the same time provides good generalization properties.

In biomedical signal processing, artificial intelligence techniques are used for identifying and extracting relevant information. However, it lacks effective solutions based on machine learning for the prediction of cardiac arrhythmias. The heart diseases diagnosis rests essentially on the analysis of various properties of ECG signal. The arrhythmia is one of the most common heart diseases. A cardiac arrhythmia is a disturbance of the heart rhythm. It occurs when the heart beats too slowly, too fast or anarchically, with no apparent cause. The diagnosis of cardiac arrhythmias is based on the analysis of the ECG properties, especially, the durations (P, QRS, T), the amplitudes (P, Q, R, S, T), the intervals (PQ, QT, RR), the cardiac frequency and the rhythm. In this paper we propose a system of arrhythmias diagnosis assistance based on the analysis of the temporal and frequential properties of the ECG signal. After the features extraction step, the ECG properties are then used as input for a convolutional neural network to detect and classify the arrhythmias. Finally, the classification results are used to perform a prediction of arrhythmias with nonlinear regression model. The method is illustrated using the MIT-BIH database.

Arrhythmia is one kind of diseases that gives rise to the death and possibly forms the immedicable danger. The most common cardiac arrhythmia is the ventricular premature beat. The main purpose of this study is to develop an efficient arrhythmia detection algorithm based on the morphology characteristics of arrhythmias using correlation coefficient in ECG signal. Subjects for experiments included normal subjects, patients with atrial premature contraction (APC), and patients with ventricular premature contraction (PVC). So and Chan's algorithm was used to find the locations of QRS complexes. When the QRS complexes were detected, the correlation coefficient and RR-interval were utilized to calculate the similarity of arrhythmias. The algorithm was tested using MIT-BIH arrhythmia database and every QRS complex was classified in the database. The total number of test data was 538, 9 and 24 for normal beats, APCs and PVCs, respectively. The results are presented in terms of, performance, positive predication and sensitivity. High overall performance (99.3%) for the classification of the different categories of arrhythmic beats was achieved. The positive prediction results of the system reach 99.44%, 100% and 95.35% for normal beats, APCs and PVCs, respectively. The sensitivity results of the system are 99.81%, 81.82% and 95.83% for normal beats, APCs and PVCs, respectively. Results revealed that the system is accurate and efficient to classify arrhythmias resulted from APC or PVC. The proposed arrhythmia detection algorithm is therefore helpful to the clinical diagnosis.

The paper proposes a gray relational analysis based learning algorithm, called gray relational algorithm, for the recognition of ECG beats. Without analyzing relations between the input ECG beat and every beat in the database for the recognition, several training beats are chosen for learning from an ECG waveform database with patient diagnosis information, and then the learning result is used to analyze the test ECGs. The resulting similarity measurement is further identified as the diagnosis of the test ECG. This algorithm is capable of reducing the computational procedure of gray relational analysis as it is directly used for the analysis. The experiment shows that the proposed method can achieve a good classification result.

Nurse Call systems are used to signal nurses for medical assistance. Present day technology uses push buttons and room lamps integrated with liquid crystal displays (LCDs) at the nurse station in most hospitals in India. Such systems are not automated and also have the risk of false alarms due to mishandling by caretakers. This prototype uses an automated technology, when implemented monitors patients in critical care units continuously and detects specific arrhythmia conditions with the help of thresholds based on pre-set standards and the information is passed on to the nurse station only in case of an emergency thus allowing continuous monitoring of the patient. Since the system is centralized, the CODE BLUE team (Dedicated team in every hospital to attend patients during a cardiac emergency) is also alerted at the same time minimizing the delay in medical assistance. This system would be most useful in emergency conditions such as Cardiac arrest, thus increasing the chances of survival of a patient. For this project, LabVIEW (Laboratory Virtual Instrument Engineering Workbench) 2014 version (software) and National Instruments MyRIO (NI Reconfigurable Input/Output) hardware are used. On comparison with some of the present day nurse call systems, the proposed system is also economical in most of the developing countries such as in India.

The implantable cardioverter-defibrillator is an effective therapeutic device for saving patients with cardiac diseases from death caused by life-threatening arrhythmias such as ventricular tachycardia and ventricular fibrillation. It is important to prevent the recurrence and treat these arrhythmias early and to accurately distinguish between a normal sinus rhythm, ventricular tachycardia, ventricular fibrillation, and supraventricular tachycardia. Therefore, in this study, we have proposed a multiple regression model based on information extracted from simultaneous intracardiac electrocardiograms in order to identify episodes of supraventricular tachycardia, ventricular tachycardia, and ventricular fibrillation. From the experimental results, we confirmed that life-threatening arrhythmias can be detected on the basis of indices obtained from simultaneous intracardiac electrocardiograms.

Heart Rate Turbulence (HRT) quantifies the autonomic response of the sinus node to singular ventricular premature complexes. It is composed of a short acceleration followed by a subsequent deceleration of heart rate. It is very likely that a baroreflex mechanism is the driving force of HRT triggered by the brief perturbation of arterial blood pressure following the premature beat. The present article briefly reviews the methodology of heart rate turbulence, its prognostic value as strong and independent risk predictor after acute myocardial infarction as well as several aspects of heart rate turbulence including the effects of acute ischemia on HRT, the significance of HRT in non-ischemic heart disease and the influence of physiological factors on HRT.

We examined the circadian variation and rate-dependence of the QT intervals (QT/RR relationship) in patients with heart failure (HF). In 108 patients with HF (male/female = 73/35, age 66±12 years), we analyzed RR, QT, rate-corrected QT (QTc) intervals and QT/RR relationship through 24-hour Holter ECG recorded at an admission (0±2 days) and before discharge (7±21 days) in the hospital and compared with age- and sex- matched controls (n = 55). The circadian rhythm of the RR interval was blunted in patients with HF at admission, but significant circadian rhythm restored at discharge from hospital. Circadian variation of the QT interval showed similar change as RR intervals. However, QTc intervals showed blunted circadian variations in these 3 groups. QTc intervals in patients with HF at admission (459±5 ms) was longest compared to control (434±4 ms) and HF at discharge (448±3 ms). QT/RR slope in control and HF at discharge exhibit significant circadian rhythm. The 24-hour mean slope at HF admission (0.27±0.01) was steeper than control (0.23±0.1) and HF at admission (0.24±0.01). There was no significant difference in QT/RR slope between control and HF at discharge. Total ventricular extrasystoles were decreased from 1152±122 beats/day at admission to 254±221 beats/day discharge from hospital. HF treatment reduces ventricular arrhythmia probably through the reduction of triggers of ventricular arrhythmias, whereas arrhythmogenic substrate (e.g. QT prolongation) is not improved by the HF treatment.

The rhythm of a scanned ECG paper can be interpreted by extracting the first derivative of the signal. This paper discusses a first derivative method to classify five different rhythms, namely: Normal Sinus Rhythm, Sinus Bradycardia, 1st Degree AV Block, Atrial Flutter and the Asystole. The PQRST Wave complex is mapped and the different intervals are identified. Unlike other methods where learning is needed and quite a number of data are required, the method described here used rule-based method. It is capable of interpreting data with minimal samples. Based on the studied data, the method was able to produce a 95% accuracy for samples with grids that do not perfectly lie in time axis.