Deep learning in the classification and recognition of cardiac activity patterns

• Autorzy: Jeleń Łukasz , Ciskowski Piotr , Kluwak Konrad

Identyfikatory

DOI

10.24425/ijet.2024.149517

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

ECG signal; deep learning; arrhythmia; signal processing; ECG classification

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2024
• Tom: Vol. 70, No. 1
• Strony: 79--85
• Opis fizyczny: Bibliogr. 22 poz., rys., tab., wykr.

Twórcy

autor

Jeleń Łukasz

• Department of Computer Engineering, Wrocław University of Science and Technology, Wrocław, Poland

autor

Ciskowski Piotr

• Department of Computer Engineering, Wrocław University of Science and Technology, Wrocław, Poland

autor

Kluwak Konrad

• Department of Control Systems and Mechatronics, Wrocław University of Science and Technology, Wrocław, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).