@article{fdi:010088280,
  title = {{A}utomatic digitization of paper electrocardiograms : a systematic review},
  author = {{L}ence, {A}. and {E}xtramiana, {F}. and {F}all, {A}hmad and {S}alem, {J}. {E}. and {Z}ucker, {J}ean-{D}aniel and {P}rifti, {E}di},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he digitization of electrocardiogram paper records is an essential step to preserve and analyze cardiac data. {T}his digitization process is not flawless as it involves several challenges, such as skew correction, binarization, and signal extraction. {V}arious approaches have been proposed to address these challenges and recent studies have introduced innovative solutions, such as deep learning models and automation processes. {A}lthough existing approaches have shown promising results, there is a lack of common databases and metrics where authors could evaluate and compare their methods. {F}urthermore, the limited accessibility of code or software hinders the comparison process. {O}verall, while digitization of paper {ECG} recordings is important in advancing cardiology research, additional efforts are needed to standardize the evaluation process while improving code accessibility. {T}his article provides a systematic review of this process.},
  keywords = {{E}lectrocardiogram ; {ECG} ; {D}igitization},
  booktitle = {},
  journal = {{J}ournal of {E}lectrocardiology},
  volume = {80},
  numero = {},
  pages = {125--132},
  ISSN = {0022-0736},
  year = {2023},
  DOI = {10.1016/j.jelectrocard.2023.05.009},
  URL = {https://www.documentation.ird.fr/hor/fdi:010088280},
}