Extraction and Digitization of ECG Signals from Standard Clinical Portable Document Format Files for the Principal Component Analysis of T-wave Morphology.

INTRODUCTION: T-wave analysis from standard electrocardiogram (ECG) remains one of the most available clinical and research methods for evaluating myocardial repolarization. T-wave morphology was recently evaluated to aid with diagnosis and characterization of diastolic dysfunction. Unfortunately, PDF stored ECG datasets limit additional numerical post-processing of ECG waveforms. In this study, we apply a simple custom process pipeline to extract and re-digitize T-wave signals and subject them to principal component analysis (PCA) to define primary T-wave shape variations. METHODS: We propose simple pre-processing and digitization algorithms programmable as a MATLAB tool using standard thresholding functions without the need for advanced signal analysis. To validate digitized datasets, we compared clinically standard measurements in 20 different ECGs with the original ECG machine interpreted values as a gold standard. Afterwards, we analyzed 212 individual ECGs for T-wave shape analysis using PCA. RESULTS: The re-digitized signal was shown to preserve the original information as evidenced by excellent agreement between original - machine interpreted and re-digitized clinical variables including heart rate: bias ~ 1 bpm (95% CI: -1.0 to 3.5), QT interval: bias ~ 0.000 ms (95% CI: -0.012 to 0.012), PR interval: bias = -0.015 ms (95% CI: -0.015 to 0.003), and QRS duration: bias = -0.001 ms (95% CI: -0.007 to 0.006). PCA revealed that the first principal component universally modulates the T-wave height or amount of repolarization voltage regardless of the investigated ECG lead. The second and third principal components described variation in the T-wave peak onset and the T-wave peak morphology, respectively. CONCLUSION: This study presents a straightforward method for re-digitizing ECGs stored in the PDF format utilized in many academic electronic medical record systems. This process can yield re-digitized lead specific signals which can be retrospectively analyzed using advanced custom post-processing numerical analysis independent of commercially available platforms.

Coronavirus Disease 2019 Infection in Children with Pre-Existing Heart Disease.

We present 7 children with congenital heart disease and coronavirus disease 2019. Of these, 5 were younger than 1 year of age and 3 had atrioventricular canal defect and trisomy 21. All 7 developed acute decompensation, with 1 death in an 18-year-old with hypertrophic cardiomyopathy and other comorbidities.