@ARTICLE{Jeleń_Łukasz_Deep_2024,
 author={Jeleń, Łukasz and Ciskowski, Piotr and Kluwak, Konrad},
 volume={vol. 70},
 number={No 1},
 journal={International Journal of Electronics and Telecommunications},
 pages={79–85},
 howpublished={online},
 year={2024},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={Electrocardiography is an examination performed frequently in patients experiencing symptoms of heart disease. Upon a detailed analysis, it has shown potential to detect and identify various activities. In this article, we present a deep learning approach that can be used to analyze ECG signals. Our research shows promising results in recognizing activity and disease patterns with nearly 90% accuracy. In this paper, we present the early results of our analysis, indicating the potential of using deep learning algorithms in the analysis of both onedimensional and two–dimensional data. The methodology we present can be utilized for ECG data classification and can be extended to wearable devices. Conclusions of our study pave the way for exploring live data analysis through wearable devices in order to not only predict specific cardiac conditions, but also a possibility of using them in alternative and augmented communication frameworks.},
 type={Article},
 title={Deep learning in the classification and recognition of cardiac activity patterns},
 URL={http://ochroma.man.poznan.pl/Content/130696/10_4457_Jelen_L_sk.pdf},
 doi={10.24425/ijet.2024.149517},
 keywords={ECG signal, deep learning, arrhythmia, signal processing, ECG classification},
}