Novel frequency analysis of signal-averaged electrocardiograms is predictive of adverse outcomes in implantable cardioverter defibrillator patients.

BACKGROUND: Current noninvasive risk stratification methods offer limited prediction of arrhythmic events when selecting patients for ICD implantation. Our laboratory has recently developed a signal processing metric called Layered Symbolic Decomposition frequency (LSDf) that quantifies the percentage of hidden QRS wave frequency components in signal-averaged ECG (SAECG) recordings. The purpose of this pilot study was to determine whether LSDf can be predictive of ventricular arrhythmia or death in an ICD patient cohort. METHODS AND RESULTS: Fifty-two ICD patients were recruited from 2008 to 2009. These were followed for a mean of 8.5 ± 0.4 years for the primary outcome of first appropriately treated ventricular arrhythmia (VT/VF) or death. Thirty-four subjects met the primary outcome. LSDf was significantly lower, and 12-lead QRS duration was significantly greater in patients meeting the primary outcome (12.14 ± 3.97% vs. 16.45 ± 3.73%; p = 0.001) and (111.59 ± 14.96 ms vs. 97.69 ± 13.51 ms; p = 0.012) respectively. A 13.25% LSDf threshold (0.74 sensitivity and 0.85 specificity) was selected based on an ROC curve. Kaplan-Meier survival analysis was conducted; patients above the 13.25% threshold demonstrated significantly better survival outcomes (log-rank p < 0.001). In Cox multivariate regression analysis, the LSDf threshold (13.25%) was compared to LVEF (28.5%), 12-lead QRSd (100 ms), age, % male sex, NYHA classification, and antiarrhythmic usage. LSDf was a predictor of the primary outcome (p = 0.005) and an independent predictor for solely ventricular arrhythmia (p = 0.002). CONCLUSION: Layered Symbolic Decomposition frequency analysis in SAECG recordings may be a viable predictor of negative ICD survival outcomes.

Moving average and standard deviation thresholding (MAST): a novel algorithm for accurate R-wave detection in the murine electrocardiogram.

Advances in implantable radio-telemetry or diverse biologging devices capable of acquiring high-resolution ambulatory electrocardiogram (ECG) or heart rate recordings facilitate comparative physiological investigations by enabling detailed analysis of cardiopulmonary phenotypes and responses in vivo. Two priorities guiding the meaningful adoption of such technologies are: (1) automation, to streamline and standardize large dataset analysis, and (2) flexibility in quality-control. The latter is especially relevant when considering the tendency of some fully automated software solutions to significantly underestimate heart rate when raw signals contain high-amplitude noise. We present herein moving average and standard deviation thresholding (MAST), a novel, open-access algorithm developed to perform automated, accurate, and noise-robust single-channel R-wave detection from ECG obtained in chronically instrumented mice. MAST additionally and automatically excludes and annotates segments where R-wave detection is not possible due to artefact levels exceeding signal levels. Customizable settings (e.g. window width of moving average) allow for MAST to be scaled for use in non-murine species. Two expert reviewers compared MAST's performance (true/false positive and false negative detections) with that of a commercial ECG analysis program. Both approaches were applied blindly to the same random selection of 270 3-min ECG recordings from a dataset containing varying amounts of signal artefact. MAST exhibited roughly one quarter the error rate of the commercial software and accurately detected R-waves with greater consistency and virtually no false positives (sensitivity, Se: 98.48% ± 4.32% vs. 94.59% ± 17.52%, positive predictivity, +P: 99.99% ± 0.06% vs. 99.57% ± 3.91%, P < 0.001 and P = 0.0274 respectively, Wilcoxon signed rank; values are mean ± SD). Our novel, open-access approach for automated single-channel R-wave detection enables investigators to study murine heart rate indices with greater accuracy and less effort. It also provides a foundational code for translation to other mammals, ectothermic vertebrates, and birds.

Multi-lead QRS detection using window pairs.

We designed a novel approach for multi-lead QRS detection. The algorithm uses one equation with two different window widths to generate a feature signal and a detection threshold. This enables it to adapt to various changes in QRS morphology and noise levels, resulting in a detection error rate of just 0.29% on the MIT-BIH Arrhythmia Database. The algorithm is also computationally efficient and capable of resolving differences between multiple leads by automatically attaching a confidence value to each QRS detection.