An Algorithm for Automated Representation of Dynamic Correlation Rhythmograms for Long-lasting Signal Recordings

• Published in: Biomedical engineering Vol. 54; no. 5; pp. 357 - 360
• Main Authors: Timofeeva, P. Yu, Alekseev, B. E., Manilo, L. A., Nemirko, A. P.
• Format: Journal Article
• Language: English
• Published: New York Springer US 2021; Springer; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Automation; Biomaterials; Biomedical and Life Sciences; Biomedicine; Cardiac arrhythmia; Classification; Correlation; EKG; Electrocardiogram; Electrocardiography; Engineering; Heart; Heart failure; Information processing; Intervals; Methods; Monitoring systems; Morphology; Representations; Sinuses
• ISSN: 0006-3398; 1573-8256
• DOI: 10.1007/s10527-021-10039-5

Investigation of the cardiac rhythm in clinical practice makes wide use of 24-hour ECG monitoring. One method for analysis of long-term ECG signal recordings involves representation of the signal as a correlation rhythmogram. Analysis of scattergrams yields statistical data whose processing gives parameters with little information value. These indicators cannot describe dynamic changes in R-R intervals, which contain additional information on the nature of the heartbeat. Our study developed a system for representing signals from long-term ECG traces in the form of dynamic correlation rhythmograms. The algorithm allows changes in heartbeat dynamics over 24 h (or more) to be visualized in less than 1 min. Using the developed system as a tool for visualizing dynamic information on R-R intervals, specialists can extract unique information on the nature of the heartbeat and classify cardiac disorders with greater accuracy.