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 from Amgen, during SPS Annual Meeting in Barcelona, is to leverage historical data to guide a novel pattern recognition algorithm in performing automated ECG and hemodynamic analyses.

Leveraging Machine Learning for Automated ECG and Hemodynamic Analyses 

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