Leveraging Machine Learning for Automated ECG and Hemodynamic Analyses

In recent years, cardiovascular safety scientists have adopted a number of analysis software platforms designed to streamline the evaluation of ECG and hemodynamic endpoints.

Post-processing with technologies such as pattern recognition can improve speed and accuracy of analysis using user-defined waveform libraries. While these technologies increase efficiency, the need to evaluate larger datasets (beat to beat), with increased depth (e.g. arrhythmia analysis), presents some conundrums: the need for significant level of human intervention, processing time, and most importantly, can be prone to high inter-user variability, a potential consequence of varying experience and analysis fatigue.

The goal of this study, presented by Mr Baublits Senior Associate Scientist at Amgen, during SPS 2019 Annual Meeting* in Barcelona, is to leverage historical data to guide a novel pattern recognition algorithm in performing automated ECG and hemodynamic analyses.

*This was a Sponsored Presentation. Although not an official part of the SPS Annual Meeting scientific program, its presentation was permitted by the Society

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