A framework for comparative study of databases and computational methods for arrhythmia detection from single-lead ECG.

ABSTRACT: Arrhythmia detection from ECG is an important area of computational ECG analysis. However, although a large number of public ECG recordings are available, most research uses only few datasets, making it difficult to estimate the generalizability of the plethora of ECG classification methods. Furthermore, there is a large variability in the evaluation procedures, as well as lack of insight into whether they could successfully perform in a real-world setup. To address these problems, we propose an open-source, flexible and configurable ECG classification codebase-ECGDL, as one of the first efforts that includes 9 arrhythmia datasets, covering a large number of both morphological and rhythmic arrhythmias, as well as 4 deep neural networks, 4 segmentation techniques and 4 evaluation schemes. W

SUBMITTER: Merdjanovska E 

PROVIDER: S-EPMC10356811 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature