Efficient methodology for electrocardiogram beat classification

The long-term outcome of athletes with frequent ventricular premature complexes (VPCs) and apparently normal heart has not been fully clarified. To evaluate the clinical and prognostic significance of VPCs and the influence of continuing sports activity during follow-up, we studied 120 healthy athletes (96 men; median age 16 years) in whom frequent VPCs (>100 VPCs/24 hours) were discovered by chance during preparticipation screening. All athletes were followed up for a median of 84 months. During follow-up, 96 underwent serial 24-hour Holter recording and 62 underwent serial echocardiography. The median number of VPCs/24 hours on basal Holter was 3,760. During follow-up, 81 athletes continued sports activity, whereas 39 did not. No athlete died or developed overt heart disease. The median number of VPCs/24 hours decreased in both athletes who continued sports activity and those who did not (from 3,805 to 1,124, p <0.0001 and from 5,787 to 1,298, p <0.0001, respectively). During follow-up, left ventricular ejection fraction slightly decreased to <55% in 9 of 62 athletes who, in respect to the remaining 53, had more VPCs/24 hours both in the basal state (12,000 vs 3,880) and during follow-up (10,702 vs 1,368), and a longer follow-up (95 vs 36 months). In conclusion, (1) frequent VPCs in athletes without heart disease have a long-term benign prognostic significance, (2) sporting activity does not modify this benign outcome, (3) during follow-up, the burden of VPCs decreases whether or not subjects continue sports activity, and (4) in 14.5% of athletes, ejection fraction slightly decreases over time.

심전도 신호 분석 및 부정맥 분류는 환자를 진단하고 치료하는데 중요한 역할을 한다. 부정맥은 맥박이 불규칙한 상태로 심실빈맥(VT)이나 심실세동(VF) 환자에게 심각한 위협이 될 수 있다. 심방조기수축(APC)과 상심실성빈맥(SVT), 심실조기수축(PVC)은 심실빈맥(VT)만큼 치명적이지는 않지만 심장질환을 진단하는데 중요한 부정맥이다. 본 논문은 2~3개의 부정맥 분류만을 고려한 기존의 방법을 극복하고 다양한 부정맥을 분류하기 위한 새로운 방법을 제시한다. 심전도 신호의 특징 추출을 위해서 EMD 방법으로 신호를 분해하여 IMFs를 얻는다. 입력 데이터의 양은 분류기 성능에 영향을 미치므로 신호 데이터의 차원을 감소시키기 위해 Burg 알고리즘을 IMFs에 적용하여 AR 계수를 구하고 여러 개의 이진 분류기를 결합한 다중 클래스 SVM의 입력으로 사용한다. 최적의 SVM 성능 파라미터를 선택하고 부정맥 분류에 적용한 결과 검출의 정확성은 96.8%~99.5%였다. 실험 결과는 제안한 EMD 방법에 의한 전처리 및 특징 추출과 다중 클래스 SVM에 의한 부정맥 분류의 유용성을 보여준다. Electrocardiogram(ECG) analysis and arrhythmia recognition are critical for diagnosis and treatment of ill patients. Cardiac arrhythmia is a condition in which heart beat may be irregular and presents a serious threat to the patient recovering from ventricular tachycardia (VT) and ventricular fibrillation (VF). Other arrhythmias like atrial premature contraction (APC), Premature ventricular contraction (PVC) and superventricular tachycardia (SVT) are important in diagnosing the heart diseases. This paper presented new method to classify various arrhythmias contrary to other techniques which are limited to only two or three arrhythmias. ECG is decomposed into Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD). Burg algorithm was performed on IMFs to obtain AR coefficients which can reduce the dimension of feature vector and utilized as Multi-class SVM inputs which is basically extended from binary SVM. We chose optimal parameters for SVM classifier, applied to arrhythmias classification and achieved the accuracies of detecting NSR, APC, PVC, SVT, VT and VP were 96.8% to 99.5%. The results showed that EMD was useful for the preprocessing and feature extraction and multi-class SVM for classification of cardiac arrhythmias, with high usefulness.

Detection and treatment of arrhythmias has become one of the main goals in cardiac care diagnosis provided by general practitioners. Electrocardiogram (ECG) analysis is one of the most commonly used tools to test and diagnose heart problems. Classification of ECG heartbeats enables the identification of specific arrhythmia or other heart conditions. This paper presents and contrasts the results from two effective ECG arrhythmia classification schemes. The first scheme consists of a principal component analysis (PCA) step for feature reduction at the input vector to the classifier, combined with soft independent modelling of class analogy (SIMCA). The second method uses a multi-class support vector machine (MSVM) classifier to differentiate between four different types of arrhythmia from ECG beats. The four types of beats include Normal (N), Premature Ventricular Contraction (PVC), and Atrial premature contraction (APC) and Right Bundle Branch Block Beat (RBBB). The time domain features were obtained from the St Petersburg INCART 12-lead Arrhythmia Database (incartdb). Between 10 and 30 Principal Components (PCs) were selected for reconstructing individual ECG beats and create the input vector to the classifier. The average classification accuracy of the proposed scheme is 76.83% and 98.33% using MSVM and SIMCA classifier respectively. The SIMCA classification algorithm provided better performance than the MSVM classifiers.

R-Wave Peak Time at Lead II in Adults With Ventricular Premature Beats, Bundle Branch Block and Left Anterior Fascicular Block

Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals

Arrhythmia is caused by improper and irregular sinus rhythm or heartbeats. In order to diagnose cardiac arrhyth- mia, electrocardiogram (ECG) beat classification and analysis is very necessary. The efficiency and accuracy of any classification model highly depends on selecting the most relevant features. The aim of this study is to classify different arrhythmic beats with a reduced set of relevant-only ECG features. To optimize the ECG feature selection process and increase the classification accuracy, a Mahalanobis-Taguchi System (MTS) based classifica- tion and analysis scheme is proposed. MTS is a multi-dimensional pattern recognition system which dynamically selects important features for further analysis. Arrhythmia can occur at any time and thus requires proper and continuous monitoring of the patient to reduce sudden heart attacks. The proposed MTS- based classification scheme is integrated with a Wireless Body Area Network (WBAN) for pervasive monitoring. The proposed scheme is analyzed and compared with a state-of-the-art scheme in terms of sensitivity, specificity, and accuracy. The results show that the proposed scheme performs significantly better than the other scheme by achieving high sensitivity, specificity, and classification accuracy for different arrhythmic heartbeats i.e., Left Bundle Branch Block (LBBB), Premature Ventricular Contraction (PVC), Right Bundle Branch Block (RBBB), and Atrial Premature Contraction (APC).

Premature ventricular contraction (PVC) is one of the most serious arrhythmias. Without early diagnosis and proper treatment, PVC can result in significant complications. In this paper, a novel feature extraction method based on a sparse auto-encoder (SAE) and softmax regression (SR) classifier was used to differentiate PVCs from other common Non-PVC rhythms, including normal sinus (N), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contraction (APC), and paced beat (PB) rhythms. The proposed method was analyzed using 40 ECG records obtained from the MIT-BIH Arrhythmia Database. The proposed method exhibited an overall accuracy of 99.4%, with a PVC recognition sensitivity and positive predictability of 97.9% and 91.8%, respectively.

Complete bundle branch block as an independent predictor of all-cause mortality: report of 7,073 patients referred for nuclear exercise testing

This work proposes an efficient method of Electrocardiogram (ECG) beats classification. ECG is an important biological signal indicating muscular activities of heart. Abnormalities in ECG beats reflect heart malfunctions. Four types of ECG beats including normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), and premature ventricular contractions (PVC) are intended to be classified. We take two-level wavelet decomposition to decompose ECG beats and extract appropriate features from sub bands of the decomposed signals. According to previous studies, wavelet coefficients seem to be sufficient features for solving the classification problems. However, they do not have suitable performance in high White Gaussian noise (WGN) added to ECG signals. Therefore, we compute fractal dimension using Katz' algorithm besides wavelet coefficients to complement our feature sets. Feature sets are classified by employing probabilistic neural network (PNN) which shows satisfactory results. Final results are indication of highly stable accuracy and high efficiency in different values of added WGN.

Cardiac Arrhythmia is an abnormal heart rhythm that develops when the electrical impulses control the heart’s contraction which does not function properly. The heart can beat too fast (tachycardia), too slow (bradycardia), or in an irregular pattern. Observing ECG signal peaks and channels freehand is difficult due to their ingenious modification. Automated detection of cardiovascular abnormalities is preferred for the early diagnosis of cardiac disorders. This paper used machine learning approaches for detecting ECG abnormality utilizing a Support Vector Machine (SVM) and Cost-Sensitive Decision-Tree (CS-DT) classifier. The Empirical Mode Decomposition approach was utilized to examine the properties of R-peaks and QRS complexes in ECG signs. Various morphological characteristics are analyzed from the signal penetrated by the classifier to diagnose the irregular beats. A set of twenty-two clinically feasible features comprising temporal, morphological, and statistical were extracted from the processed ECG signals and applied to the classifier to categorize cardiovascular irregularities like Normal (N), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Beats (APB), and Premature Ventricular Contraction (PVC). The Beth Israel Hospital at Massachusetts Institute of Technology (MIT-BIH) dataset has been used for this work, where feature datasets are split into training and evaluation subsets. The training set is used to train machine learning models on the extracted features, while the evaluation set is used to assess the performance of the trained models. The evaluation metrics such as Accuracy (Acc), Sensitivity (Se), Specificity (Sp), and Positive Predictivity (Pp), are frequently used to evaluate the model’s performance in Arrhythmia detection along with classification. The simulation has been conducted using SVM and CS-DT classifier with performance for all individual class labels at a Confidence Factor (CF) of 0.5. The performance of the time and frequency domain features is merged resulting in higher classification of Sensitivity, Specificity, Positive Predictivity, and Accuracy of 89.5%, 98.11%, 87.76%, and 96.8% in SVM, 97.71%, 99.58%, 97.66%, 99.32% in CS-DT classifier in identifying the irregular heartbeats.

The features of ventricular arrhythmia in coronary spasm induced by ergonovine were examined to elucidate the characteristics of ventricular arrhythmia in acute myocardial ischemia. The coupling intervals and configurations of the QRS wave of the premature ventricular beats (PVBs) were extremely variable, even in the same individual and at the closer time phases. The prematurity index (R-R'/QT) of PVBs with the right bundle branch block (RBBB) pattern was significantly smaller than with the left bundle branch block (LBBB) pattern. The vulnerability index (R-RX QT/R-R') of PVBs with the RBBB pattern was significantly larger than with the LBBB pattern. The incidence of PVBs with the RBBB pattern (50.4%) was slightly larger than with the LBBB pattern (40.5%). PVBs deteriorated into ventricular tachycardias (VTs) in 29 (24.8%) of 117 cases with the RBBB pattern and in 11 (11.7%) out of 94 cases with the LBBB pattern. Thus, PVBs with the RBBB pattern were more likely to progress to VT than with the LBBB pattern. But it is difficult to assess the clinical significance of PVBs from their QRS morphology alone, because 11 (27.5%) of 40 cases of VT were initiated by PVBs with the LBBB pattern.

Background/Objectives: COVID-19 is associated with multiple systemic effects, including cardiovascular complications. However, its interplay with cardiac conduction abnormalities remains underexplored. We compared the clinical profile of COVID-19 patients with pre-existing left bundle branch block (LBBB) or right bundle branch block (RBBB) at hospital admission. Methods: This study included 100 COVID-19 patients with antecedent BBB (50 LBBB, 50 RBBB). Critical cardiometabolic, renal, hematological, and inflammatory markers were measured. Logistic regression was used to identify key predictors differentiating COVID-19 patients with LBBB and RBBB. Spearman's correlations were applied to assess intra-strata associations for these variables. Results: COVID-19 patients with LBBB patients were significantly more likely to display lower systolic blood pressure (p = 0.012) but greater left atrial size (p = 0.008), left ventricular diameter (p = 0.001), and interventricular septal thickness (p = 0.023). Hematological and inflammatory markers differed, with LBBB patients being prone to exhibit higher red cell distribution width (p = 0.005), lymphocyte count (p < 0.001), neutrophil count (p = 0.045), and C-reactive protein (p < 0.001). This group also tended to show lower erythrocyte sedimentation rate (p = 0.013) and glycated hemoglobin (p = 0.045) but higher random glucose (p = 0.014). Absolute lymphocyte count, C-reactive protein, and left ventricular diameter were the most robust predictors distinguishing LBBB from RBBB. Significant associations were found exclusively for LBBB, all of them being weak. These predominantly negative relationships indicated an inflammatory origin, and most of them occurred for lymphocyte count. Conclusions: COVID-19 patients with LBBB and RBBB present distinct clinical profiles at hospital admission. The former group demonstrates a more adverse baseline clinical profile, particularly in terms of cardiac and inflammatory markers. These findings suggest that pre-existing BBB type may influence disease progression, potentially helping in risk stratification for COVID-19 patients.

Studies in intracardiac electrography in man: I. The potential variations in the right atrium

Low-complexity sinusoidal-assisted EMD (SAEMD) algorithms for solving mode-mixing problems in HHT

Comparative features of newly acquired left and right bundle branch block in the general population: The framingham study