Szczegóły publikacji
Opis bibliograficzny
An embedded system using Convolutional Neural Network model for online and real-time ECG signal classification and prediction / Wahyu Caesarendra, Taufiq Aiman Hishamuddin, Daphne Teck Ching Lai, Asmah Husaini, Lisa Nurhasanah, Adam GŁOWACZ, Gusti Ahmad Fanshuri Alfarisy // Diagnostics [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2075-4418 . — 2022 — vol. 12 iss. 4 art. no. 795, s. 1–19. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 18–19, Abstr. — Publikacja dostępna online od: 2022-03-24
Autorzy (7)
- Caesarendra Wahyu
- Hishamuddin Taufiq Aiman
- Teck Ching Lai Daphne
- Husaini Asmah
- Nurhasanah Lisa
- AGHGłowacz Adam
- Alfarisy Gusti Ahmad Fanshur
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 139647 |
|---|---|
| Data dodania do BaDAP | 2022-03-25 |
| Tekst źródłowy | URL |
| DOI | 10.3390/diagnostics12040795 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Diagnostics |
Abstract
This paper presents an automatic ECG signal classification system that applied the Deep Learning (DL) model to classify four types of ECG signals. In the first part of our work, we present the model development. Four different classes of ECG signals from the PhysioNet open-source database were selected and used. This preliminary study used a Deep Learning (DL) technique namely Convolutional Neural Network (CNN) to classify and predict the ECG signals from four different classes: normal, sudden death, arrhythmia, and supraventricular arrhythmia. The classification and prediction process includes pulse extraction, image reshaping, training dataset, and testing process. In general, the training accuracy achieved up to 95% after 100 epochs. However, the prediction of each ECG single type shows a differentiation. Among the four classes, the results show that the predictions for sudden death ECG waveforms are the highest, i.e., 80 out of 80 samples are correct (100% accuracy). In contrast, the lowest is the prediction for normal sinus ECG waveforms, i.e., 74 out of 80 samples are correct (92.5% accuracy). This is due to the image features of normal sinus ECG waveforms being almost similar to the image features of supraventricular arrhythmia ECG waveforms. However, the model has been tuned to achieve an optimal prediction. In the second part, we presented the hardware implementation with the predictive model embedded in an NVIDIA Jetson Nanoprocessor for the online and real-time classification of ECG waveforms.
