Comparison of automated interval measurements by widely used algorithms in digital electrocardiographs

Background: Automated measurements of electrocardiographic (ECG) intervals by current-generation digital electrocardiographs are critical to computer-based ECG diagnostic statements, to serial comparison of ECGs, and to epidemiological studies of ECG findings in populations. A previous study demonstrated generally small but often significant systematic differences among 4 algorithms widely used for automated ECG in the United States and that measurement differences could be related to the degree of abnormality of the underlying tracing. Since that publication, some algorithms have been adjusted, whereas other large manufacturers of automated ECGs have asked to participate in an extension of this comparison. Methods: Seven widely used automated algorithms for computer-based interpretation participated in this blinded study of 800 digitized ECGs provided by the Cardiac Safety Research Consortium. All tracings were different from the study of 4 algorithms reported in 2014, and the selected population was heavily weighted toward groups with known effects on the QT interval: included were 200 normal subjects, 200 normal subjects receiving moxifloxacin as part of an active control arm of thorough QT studies, 200 subjects with genetically proved long QT syndrome type 1 (LQT1), and 200 subjects with genetically proved long QT syndrome Type 2 (LQT2). Results: For the entire population of 800 subjects, pairwise differences between algorithms for each mean interval value were clinically small, even where statistically significant, ranging from 0.2 to 3.6 milliseconds for the PR interval, 0.1 to 8.1 milliseconds for QRS duration, and 0.1 to 9.3 milliseconds for QT interval. The mean value of all paired differences among algorithms was higher in the long QT groups than in normals for both QRS duration and QT intervals. Differences in mean QRS duration ranged from 0.2 to 13.3 milliseconds in the LQT1 subjects and from 0.2 to 11.0 milliseconds in the LQT2 subjects. Differences in measured QT duration (not corrected for heart rate) ranged from 0.2 to 10.5 milliseconds in the LQT1 subjects and from 0.9 to 12.8 milliseconds in the LQT2 subjects. Conclusions: Among current-generation computer-based electrocardiographs, clinically small but statistically significant differences exist between ECG interval measurements by individual algorithms. Measurement differences between algorithms for QRS duration and for QT interval are larger in long QT interval subjects than in normal subjects. Comparisons of population study norms should be aware of small systematic differences in interval measurements due to different algorithm methodologies, within-individual interval measurement comparisons should use comparable methods, and further attempts to harmonize interval measurement methodologies are warranted

ICA-Derived Respiration Using an Adaptive R-peak Detector

Breathing Rate (BR) plays a key role in health deterioration monitoring. Despite that, it has been neglected due to inadequate nursing skills and insufficient equipment. ECG signal, which is always monitored in a hospital ward, is affected by respiration which makes it a highly appealing way for the BR estimation. In addition, the latter requires accurate R-peak detection, which is a continuing concern because current methods are still inaccurate and miss heart beats. This study proposes a frequency domain BR estimation method which uses a novel real-time R-peak detector based on Empirical Mode Decomposition (EMD) and a blind source ICA for separating the respiratory signal. The originality of the BR estimation method is that it takes place in the frequency domain as opposed to some of the current methods which rely on a time domain analysis, making the estimation more accurate. Moreover, our novel QRS detector uses an adaptive threshold over a sliding window and differentiates large Q-peaks from R-peaks, facilitating a more accurate BR estimation. The performance of our methods was tested on real data from Capnobase dataset. An average mean absolute error of less than 0.7 breath per minute was achieved using our frequency domain method, compared to 15 breaths per minute of the time domain analysis. Moreover, our modified QRS detector shows comparable results to other published methods, achieving a detection rate over 99.80%

Left Ventricular Hypertrophy Detection for Mason–likar Ecg Electrode Placement

[No abstract

Left Ventricular Hypertrophy Detection for Mason–likar Ecg Electrode Placement

[No abstract