FlexPoints: Efficient electrocardiogram signal compression for machine learning

Abstract

The electrocardiogram (ECG) stands out as one of the most frequently used medical tests, playing a crucial role in the accurate diagnosis and treatment of patients. While ECG devices generate a huge amount of data, only a fraction of it holds valuable medical information. To deal with this problem, many compression algorithms and filters have been developed over the years. However, the rapid development of new machine-learning techniques introduces new challenges. To address this class of problems, we have introduced a FlexPoints algorithm. This innovative algorithm searches for characteristic points on the ECG signal and ignores all other points that lack pertinent medical information. The conducted experiments have demonstrated that our proposed algorithm can significantly reduce the number of data points representing ECG signals without losing valuable medical insights. These sparse but essential characteristic points, referred to as flex points, serve as well-fitted input for modern machine learning models. Such models exhibit enhanced performance when using flex points as input, as opposed to raw data or data compressed by other popular algorithms.