Review of Processing Pathological Vectorcardiographic Records for the Detection of Heart Disease

Intelligent assessment of atrial fibrillation gradation based on sinus rhythm electrocardiogram and baseline information

BACKGROUND

Atrial fibrillation (AF) is a progressive arrhythmia that significantly affects a patient's quality of life. The 4S-AF scheme is clinically recommended for AF management; however, the evaluation process is complex and time-consuming. This renders its promotion in primary medical institutions challenging. This retrospective study aimed to simplify the evaluation process and present an objective assessment model for AF gradation.

METHODS

In total, 189 12-lead electrocardiogram (ECG) recordings from 64 patients were included in this study. The data were annotated into two groups (mild and severe) according to the 4S-AF scheme. Using a preprocessed ECG during the sinus rhythm (SR), we obtained a synthesized vectorcardiogram (VCG). Subsequently, various features were calculated from both signals, and age, sex, and medical history were included as baseline characteristics. Different machine learning models, including support vector machines, random forests (RF), and logistic regression, were finally tested with a combination of feature selection techniques.

RESULTS

The proposed method demonstrated excellent performance in the classification of AF gradation. With an optimized feature set of VCG and baseline features, the RF model achieved accuracy, sensitivity, and specificity of 83.02 %, 80.56 %, and 88.24 %, respectively, under the inter-patient paradigm.

CONCLUSION

Our results demonstrate the value of physiological signals in AF gradation evaluation, and VCG signals were effective in identifying mild and severe AF. Considering its low computational complexity and high assessment performance, the proposed model is expected to serve as a useful prognostic tool for clinical AF management.

BRAVEHEART: Open-source software for automated electrocardiographic and vectorcardiographic analysis

BACKGROUND AND OBJECTIVES

Electrocardiographic (ECG) and vectorcardiographic (VCG) analyses are used to diagnose current cardiovascular disease and for risk stratification for future adverse cardiovascular events. With increasing use of digital ECGs, research into novel ECG/VCG parameters has increased, but widespread computer-based ECG/VCG analysis is limited because there are no currently available, open-source, and easily customizable software packages designed for automated and reproducible analysis.

METHODS AND RESULTS

We present BRAVEHEART, an open-source, modular, customizable, and easy to use software package implemented in the MATLAB programming language, for scientific analysis of standard 12-lead ECGs acquired in a digital format. BRAVEHEART accepts a wide variety of digital ECG formats and provides complete and automatic ECG/VCG processing with signal denoising to remove high- and low-frequency artifact, non-dominant beat identification and removal, accurate fiducial point annotation, VCG construction, median beat construction, customizable measurements on median beats, and output of measurements and results in numeric and graphical formats.

CONCLUSIONS

The BRAVEHEART software package provides easily customizable scientific analysis of ECGs and VCGs. We hope that making BRAVEHART available will allow other researchers to further the field of ECG/VCG analysis without having to spend significant time and resources developing their own ECG/VCG analysis software and will improve the reproducibility of future studies. Source code, compiled executables, and a detailed user guide can be found at http://github.com/BIVectors/BRAVEHEART. The source code is distributed under the GNU General Public License version 3.

Limits of the spatial ventricular gradient and QRST angles in patients with normal electrocardiograms and no known cardiovascular disease stratified by age, sex, and race

INTRODUCTION

Measurement of the spatial ventricular gradient (SVG), spatial QRST angles, and other vectorcardiographic measures of myocardial electrical heterogeneity have emerged as novel risk stratification methods for sudden cardiac death and other adverse cardiovascular events. Prior studies of normal limits of these measurements included primarily young, healthy, White volunteers, but normal limits in older patients are unknown. The influence of race and body mass index (BMI) on these measurements is also unclear.

METHODS

Normal 12-lead electrocardiograms (ECGs) from a single center were identified. Patients with abnormal cardiovascular, pulmonary, or renal history (assessed by International Classification of Disease [ICD-9/ICD-10] codes) or abnormal cardiovascular imaging were excluded. The SVG and QRST angles were measured and stratified by age, sex, and race. Multivariable linear regression was used to assess the influence of age, BMI, and heart rate (HR) on these measurements.

RESULTS

Among 3292 patients, observed ranges of SVG and QRST angles (peak and mean) differed significantly based on sex, age, and race. Sex differences attenuated with increasing age. Men tended to have larger SVG magnitude (60.4 [46.1-77.8] vs. 52.5 [41.3-65.8] mv*ms, p < .0001) and elevation, and more anterior/negative SVG azimuth (-14.8 [-25.1 to -4.3] vs. 1.3 [-9.8 to 10.5] deg, p < .0001) compared to women. Men also had wider QRST angles. Observed ranges varied significantly with BMI and HR. SVG and QRST angle measurements were robust to different filtering bandwidths and moderate fiducial point annotation errors, but were heavily affected by changes in baseline correction.

CONCLUSIONS

Age, sex, race, BMI, and HR significantly affect the range of SVG and QRST angles in patients with normal ECGs and no known cardiovascular disease, and should be accounted for in future studies. An online calculator for prediction of these "normal limits" given demographics is provided at https://bivectors.github.io/gehcalc/.

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs

The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector's coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology.