Power spectrum analysis of fetal heart rate variability using the abdominal maternal electrocardiogram

A signal analysis procedure is described for obtaining the power spectrum of the fetal and maternal heart rates as recorded from the abdomen. This technique, which includes the subtraction of an averaged maternal ECG waveform using a cross-correlation function and the fast Fourier transform algorithm, enables the detection of all the fetal QRS complexes in spite of their coincidence with the maternal ECGs. The power spectrum of the fetal heart rates (FHR) obtained from 15 women at 32–41 weeks gestation were studied and two indices were measured which are related to the long term and short term variabilities in the FHR signal. It was found that quantitative evaluation of the FHR variability can be obtained using power spectrum analysis.     

The study of autowave mechanisms of electrocardiogram variability during high frequency arrhythmias: mathematical modeling data

High-frequency cardiac arrhythmias are very dangerous, as they quite often lead to sudden death. These high-frequency arrhythmias are frequently produced by rotating autowaves. In the given work, the dynamics of a rotating three-dimensional excitation scroll wave and the influence of this dynamics on the variability of model electrocardiograms (ECGs) were simulated with the use of the Aliev--Panfilov model for both homogeneous and heterogeneous excitable media. Model ECGs were obtained by summing up local membrane potentials, while ECG variability was estimated numerically through the normalized variability analysis. In the homogeneous medium, the stability of the scroll wave to its filament perturbations was shown to be dependent both on the excitability of the medium and tension of the filament, while in the heterogeneous medium, the scroll was shown to be unstable. It was shown that the scroll wave dynamics affects essentially the variability of the model ECGs, and the ECG variability increases as the excitation threshold value grows. It was found that, at some parameters of the excitable medium, the variability of ECGs in the homogeneous medium is higher than in the heterogeneous medium.     

Predicting the QRS complex and detecting small changes using principal component analysis.

In this paper, a new method for QRS complex analysis and estimation based on principal component analysis (PCA) and polynomial fitting techniques is presented. Multi-channel ECG signals were recorded and QRS complexes were obtained from every channel and aligned perfectly in matrices. For every channel, the covariance matrix was calculated from the QRS complex data matrix of many heartbeats. Then the corresponding eigenvectors and eigenvalues were calculated and reconstruction parameter vectors were computed by expansion of every beat in terms of the principal eigenvectors. These parameter vectors show short-term fluctuations that have to be discriminated from abrupt changes or long-term trends that might indicate diseases. For this purpose, first-order poly-fit methods were applied to the elements of the reconstruction parameter vectors. In healthy volunteers, subsequent QRS complexes were estimated by calculating the corresponding reconstruction parameter vectors derived from these functions. The similarity, absolute error and RMS error between the original and predicted QRS complexes were measured. Based on this work, thresholds can be defined for changes in the parameter vectors that indicate diseases.     

Machine Learning Approach to Detect Cardiac Arrhythmias in ECG Signals: A Survey

Cardiac arrhythmia is a condition when the heart rate is irregular either the beat is too slow or too fast. It occurs due to improper electrical impulses that coordinates the heart beats. Sudden cardiac death may occurs due to some dangerous arrhythmias conditions. Hence the main objective of the electrocardiogram (ECG) analysis is to detect the life-threatening arrhythmias accurately for appropriate treatment in order to save life. Since the last decades, several methods were reported for automatic ECG beat classifications. In this work, we present a systematic review of the current state-of-the-art methods used to detect cardiac arrhythmia using on ECG signals. It includes the signal decomposition, feature extraction and machine learning approaches used for automatic detection and decision making process. The articles covers the pre-processing, detection of QRS complex, feature extraction and classification of ECG beats. Based on the past studies, it is understood that the automated approach using computer-aided decision making process is highly required for real-time detection of cardiac arrhythmias. The advantages and limitations of different methods are discussed and also the future scopes is highlighted in the process of effective detection of cardiac arrhythmias. This study could be beneficial for researchers to analyze the existing state-of-art techniques used in detection of arrhythmia conditions.     

SNPs Identified as Modulators of ECG Traits in the General Population Do Not Markedly Affect ECG Traits during Acute Myocardial Infarction nor Ventricular Fibrillation Risk in This Condition

Background

Ventricular fibrillation (VF) in the setting of acute ST elevation myocardial infarction (STEMI) is a leading cause of mortality. Although the risk of VF has a genetic component, the underlying genetic factors are largely unknown. Since heart rate and ECG intervals of conduction and repolarization during acute STEMI differ between patients who do and patients who do not develop VF, we investigated whether SNPs known to modulate these ECG indices in the general population also impact on the respective ECG indices during STEMI and on the risk of VF.

Methods and Results

The study population consisted of participants of the Arrhythmia Genetics in the NEtherlandS (AGNES) study, which enrols patients with a first STEMI that develop VF (cases) and patients that do not develop VF (controls). SNPs known to impact on RR interval, PR interval, QRS duration or QTc interval in the general population were tested for effects on the respective STEMI ECG indices (stage 1). Only those showing a (suggestive) significant association were tested for association with VF (stage 2). On average, VF cases had a shorter RR and a longer QTc interval compared to non-VF controls. Eight SNPs showed a trend for association with the respective STEMI ECG indices. Of these, three were also suggestively associated with VF.

Conclusions

RR interval and ECG indices of conduction and repolarization during acute STEMI differ between patients who develop VF and patients who do not. Although the effects of the SNPs on ECG indices during an acute STEMI seem to be similar in magnitude and direction as those found in the general population, the effects, at least in isolation, are too small to explain the differences in ECGs between cases and controls and to determine risk of VF.     

Grammatic representation of beat sequences for fuzzy arrhythmia diagnosis

The final stage of a system for automatic monitoring of cardiac arrhythmias is the diagnosis of the rhythm or arrhythmia present in the patient during the monitoring process. In this paper we approach the detection process by means of the analysis of the electrocardiographic signal (ECG) on a surface lead produced by those arrhythmias which can be recognized by identifying specific beat sequences and taking into account contextual information, mainly rhythm information. We have developed a diagnosis process for arrhythmias which uses a fuzzy classification of beats according to their etiology or focus of origin. The process we describe permits a more adequate consideration by the user of the arrhythmias diagnosed by the system, mainly in those cases in which the information derived from ECG analysis is not determinant.     

Electrocardiograms in five bipolar leads recorded from the body surface of three fish species (Cyprinus carpio, Oreochromis niloticus and Pagrus major) in fresh or sea-water

1. ECGs were recorded using five bipolar leads from the body surface of porgy Pagrus major and tilapia Oreochromis niloticus in fresh or sea-water, or held on a dry towel. 2. The maximum QRS amplitude was detected in lead V in both species being 54 +/- 25 microV in porgy and 241 +/- 78 microV in tilapia, respectively, when the fish were held on the dry towel. 3. Clear ECG waves could not be obtained from porgy in sea-water because of the porgy's small cardiac potential and leakage of the potential. However, clear ECG waves could be obtained in sea-water from the body surface of tilapia, with the average QRS amplitude being 35 +/- 8 microV. 4. The mean electrical P and QRS axes in tilapia were directed toward almost the same direction (frontal downward), whereas both P and QRS axes in most carp were in opposite directions. In porgy, the relationship between the P and QRS axes could not be identified due to the smaller amplitude of the P wave.     

Variations in heart rate and rhythm of harbor seal pups during rehabilitation

Heart rate and rhythm is regulated by the autonomic nervous system, which matures during the first months of life. Little is known about heart rate and rhythm development and potential arrhythmias in seal pups during rehabilitation in seal centers. Using an iPhone ECG device, 1 min ECGs were obtained from harbor seal pups admitted to a seal rehabilitation facility. ECGs were taken from 55 seals after admission, 53 seals after 14 d, and 52 seals prior to release. From 24 seal pups additional ECGs were taken daily for the first week of rehabilitation. At admission sinus rhythm with a median heart rate of 148 complexes per minute was detected, prior to release sinus bradycardia or sinus arrhythmia with a median heart rate of 104 complexes minute was present. P wave morphology was highly variable and single supra‐ and ventricular premature complexes were recorded in individual animals. The first 14 d were characterized by highly variable heart rates and rhythms, including episodes of sinus tachycardia and 2nd degree atrioventricular blocks. The reduction in heart rates and development of a regular heart rhythm during rehabilitation suggest adaptation to the unfamiliar environment, resolution of disease, and/or maturation of the autonomic nervous system.     

Real time electrocardiogram QRS detection using combined adaptive threshold

Background  

QRS and ventricular beat detection is a basic procedure for electrocardiogram (ECG) processing and analysis. Large variety of methods have been proposed and used, featuring high percentages of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGs     

Radiofrequency catheter ablation of atrioventricular nodal reentrant tachycardia: it is not always as it is expected

Observation of Coincident arrhythmias is not uncommon but the co-existence of idiopathic verapamil sensitive left ventricular tachycardia (ILVT) with other arrhythmias is very rare. We hereby presented a 30 year old male patient with a history of frequent episodes of palpitations and sustained narrow complex tachycardia. During electrophysiologic study two arrhythmias, one with narrow complexes which was shown to be typical atrioventricular nodal re-entrant tachycardia and the other with wide QRS complexes and right bundle branch block and left axis morphology, compatible with ILVT, were inducible. Radiofrequency catheter ablation of both arrhythmias was done at two consecutive sessions. The patient has remained asymptomatic without antiarrhythmic therapy for the past six months.     

The Effect of Aging on the Specialized Conducting System: A Telemetry ECG Study in Rats over a 6 Month Period

Advanced age alone appears to be a risk factor for increased susceptibility to cardiac arrhythmias. We previously observed in the aged rat heart that sinus rhythm ventricular activation is delayed and characterized by abnormal epicardial patterns although conduction velocity is normal. While these findings relate to an advanced stage of aging, it is not yet known when and how ventricular electrical impairment originates and which is the underlying substrate. To address these points, we performed continuous telemetry ECG recordings in freely moving rats over a six-month period to monitor ECG waveform changes, heart rate variability and the incidence of cardiac arrhythmias. At the end of the study, we performed in-vivo multiple lead epicardial recordings and histopathology of cardiac tissue. We found that the duration of ECG waves and intervals gradually increased and heart rate variability gradually decreased with age. Moreover, the incidence of cardiac arrhythmias gradually increased, with atrial arrhythmias exceeding ventricular arrhythmias. Epicardial multiple lead recordings confirmed abnormalities in ventricular activation patterns, likely attributable to distal conducting system dysfunctions. Microscopic analysis of aged heart specimens revealed multifocal connective tissue deposition and perinuclear myocytolysis in the atria. Our results demonstrate that aging gradually modifies the terminal part of the specialized cardiac conducting system, creating a substrate for increased arrhythmogenesis. These findings may open new therapeutic options in the management of cardiac arrhythmias in the elderly population.     

正常人体表心电图形态的逐拍变化

心电图的各种波形是众多心肌细胞动作电位在体表的综合效应。建立了对体表心电图的形态进行逐拍分析的硬件及软件系统。对５８例健康人体表心电图进行了采集和分析,得到了正常人体表心电图中Ｐ波,ＱＲＳ波,及Ｔ波的形态差异图,并进行了频谱及时域瞬态分析,从而展示了心肌电活动在体表心电图中的逐拍变化现象,体表心电图逐拍形态变化反映了心房肌和心室肌电活动的时变特征。     

An electrocardiographic series of flecainide toxicity

Anti-arrhythmic drugs (AADs) uniquely affect the various electrolyte channels in the heart and can slow conduction, increase refractoriness, and/or decrease automaticity with the goal of preventing tachyarrhythmias. Due to these properties, these same drugs are by nature pro-arrhythmic. Vaughan-Williams classification Ic AADs belong to a class of medications that inhibit sodium channels, leading to decreased conduction velocity of myocytes and Purkinje fibers as well as to decreased automaticity of pacemaker cells. When present in toxic amounts, this leads to classic changes on the electrocardiogram (ECG) that are harbingers of potentially lethal arrhythmias. Presented is a clinical series of ECGs that occurred in a patient who presented with flecainide toxicity.     

基于单个细胞动作电位计算心电:若干异常仿真心电图

根据构造的心脏电生理模型及提出的基于单细胞动作电位计算心电图的算法,介绍异常心电活动的描述方式及对若干异常心电图的仿真结果,包括心肌缺血、心肌梗死、房室传导阻滞、束支传导阻滞、以及房室旁路,并对这些心电图的 产生机制进行说明,算法及仿真结果表明,细胞间的跨缝隙连结电位差是产生场点电热进而产生各种心电图波形的原因。     

Modeling of IK1 mutations in human left ventricular myocytes and tissue

Elucidation of the cellular basis of arrhythmias in ion channelopathy disorders is complicated by the inherent difficulties in studying human cardiac tissue. Thus we used a computer modeling approach to study the mechanisms of cellular dysfunction induced by mutations in inward rectifier potassium channel (K(ir))2.1 that cause Andersen-Tawil syndrome (ATS). ATS is an autosomal dominant disorder associated with ventricular arrhythmias that uncommonly degenerate into the lethal arrhythmia torsade de pointes. We simulated the cellular and tissue effects of a potent disease-causing mutation D71V K(ir)2.1 with mathematical models of human ventricular myocytes and a bidomain model of transmural conduction. The D71V K(ir)2.1 mutation caused significant action potential duration prolongation in subendocardial, midmyocardial, and subepicardial myocytes but did not significantly increase transmural dispersion of repolarization. Simulations of the D71V mutation at shorter cycle lengths induced stable action potential alternans in midmyocardial, but not subendocardial or subepicardial cells. The action potential alternans was manifested as an abbreviated QRS complex in the transmural ECG, the result of action potential propagation failure in the midmyocardial tissue. In addition, our simulations of D71V mutation recapitulate several key ECG features of ATS, including QT prolongation, T-wave flattening, and QRS widening. Thus our modeling approach faithfully recapitulates several features of ATS and provides a mechanistic explanation for the low frequency of torsade de pointes arrhythmia in ATS.     

Differential effects of anesthetics on electrical properties of the rainbow trout (Oncorhynchus mykiss) heart

We compared electrocardiographic signals in hatchery-reared, non-spinally-transected, immature rainbow trout (Oncorhynchus mykiss Walbaum) under clove oil (25 ppm), tricaine methanesulfonate (tricaine, 60 ppm), and benzocaine (108 ppm) general anesthesia (35 min, 14 degrees C). For all 3 anesthetics, the mean heart rate (HR) and QRS amplitude did not differ, and QRS duration and QT interval were independent of HR. Heart rate variability (HRV) was significantly (4-fold, P=0.032) higher under benzocaine than under clove oil and tricaine, but did not differ between clove oil and tricaine. QRS duration differed between groups (P<0.001, F=121); benzocaine anesthesia resulted in longer QRS complexes compared to clove oil (P<0.001) and tricaine (P<0.001) anesthesia, and QRS complexes under clove oil were longer than those under tricaine (P<0.001). High HRV and QRS amplitude variation with benzocaine were associated with HR oscillations as anesthetic exposure time increased, and suggest benzocaine toxicity which may influence cardiac function studies. Similar clove oil and tricaine ECG patterns suggest comparable autonomic effects, and maintenance of myocardial excitability. Given its low cost, ease of use, and similar ECG profiles to tricaine, clove oil is a viable alternative for studies of cardiac function in anesthetized rainbow trout.     

Improved ECG detection of presence and severity of right ventricular pressure load validated with cardiac magnetic resonance imaging

The study aimed to assess whether the 12-lead ECG-derived ventricular gradient, a vectorial representation of ventricular action potential duration heterogeneity directed toward the area of shortest action potential duration, can improve ECG diagnosis of chronic right ventricular (RV) pressure load. ECGs from 72 pulmonary arterial hypertension patients recorded <30 days before onset of therapy were compared with ECGs from matched healthy control subjects (n = 144). Conventional ECG criteria for increased RV pressure load were compared with the ventricular gradient. In 38 patients a cardiac magnetic resonance (CMR) study had been performed within 24 h of the ECG. By multivariable analysis, combined use of conventional ECG parameters (rsr' or rsR' in V1, R/S > 1 with R > 0.5 mV in V1, and QRS axis >90 degrees ) had a sensitivity of 89% and a specificity of 93% for presence of chronic RV pressure load. However, the ventricular gradient not only had a higher diagnostic accuracy for chronic RV pressure load by receiver operating characteristic analysis [areas under the curve (AUC) = 0.993, SE 0.004 vs. AUC = 0.945, SE 0.021, P < 0.05], but also discriminated between mild-to-moderate and severe RV pressure load. CMR identified an inverse relation between the ventricular gradient and RV mass, and a trend toward a similar relation with RV volume. In conclusion, chronically increased RV pressure load is electrocardiographically reflected by an altered ventricular gradient associated with RV remodeling-related changes in ventricular action potential duration heterogeneity. The use of the ventricular gradient allows ECG detection of even mildly increased RV pressure load.     

Influence of recognition errors of computerised analysis of 24-hour electrocardiograms on the measurement of spectral components of heart rate variability

Spectral methods for the assessment of heart rate variability (HRV) in 24-h electrocardiogram (ECG) are believed to require visual verification and manual editing of the computerised recognition of the ECG. This study investigated the effect of the recognition errors of computerised ECG recognition on two methods providing spectral HRV indices: (a) Fast Fourier Transformation (FFT); and (b) peak-to-trough analysis (PTA). Both methods were used to measure HRV spectra in 24-h ECGs recorded in 557 survivors of acute myocardial infarction. Each ECG was analysed using the Marquette 8000 Holter system and spectral HRV analyses were performed both prior to and after manual verification of the automatic ECG analysis. The FFT and PTA methods were used to calculate the low (0.04-0.15 Hz), medium (0.15-0.40 Hz) and high (0.40-1.00 Hz) HRV spectral components. For each method and for each spectral component, the rank correlations between the results obtained from unedited and edited ECG recognition were calculated. The correlations between the corresponding spectral components provided by the FFT and PTA methods applied to the edited recognitions were also calculated. Both methods were substantially affected by recognition errors. The FFT method was more sensitive to the misrecognition than the PTA method. The inter-method correlations were higher for the high and medium spectral components than for the low spectral component. The study suggests that spectral HRV analysis should be performed only on carefully verified and manually corrected recognitions of long-term electrocardiograms.     

Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks

BackgroundElectrocardiogram (ECG) is a method of recording the electrical activity of the heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any irregularity of the heartbeat that causes an abnormality in the heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG recordings is not practical for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to develop computer-aided-diagnosis (CAD) systems to automatically identify arrhythmias.MethodsThis paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The proposed approach identifies arrhythmia classes using Convolutional Neural Network (CNN) trained by two-dimensional (2D) ECG beat images. Firstly, ECG signals, which consist of 5 different arrhythmias, are segmented into heartbeats which are transformed into 2D grayscale images. Afterward, the images are used as input for training a new CNN architecture to classify heartbeats.ResultsThe experimental results show that the classification performance of the proposed approach reaches an overall accuracy of 99.7%, sensitivity of 99.7%, and specificity of 99.22% in the classification of five different ECG arrhythmias. Further, the proposed CNN architecture is compared to other popular CNN architectures such as LeNet and ResNet-50 to evaluate the performance of the study.ConclusionsTest results demonstrate that the deep network trained by ECG images provides outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Moreover, the proposed method has lower computational costs compared to existing methods and is more suitable for mobile device-based diagnosis systems as it does not involve any complex preprocessing process. Hence, the proposed approach provides a simple and robust automatic cardiac arrhythmia detection scheme for the classification of ECG arrhythmias.