This paper puts forward a 1-D convolutional neural network (CNN) that exploits a novel analysis of the correlation between the two leads of the noisy electrocardiogram (ECG) to classify heartbeats. The proposed method is one-dimensional, enabling complex structures while maintaining a reasonable computational complexity. It is based on the combination of elementary handcrafted time domain features, frequency domain features through spectrograms and the use of autoregressive modeling. On the MIT-BIH database, a 95.52% overall accuracy is obtained by classifying 15 types, whereas a 95.70% overall accuracy is reached when classifying 7 types from the INCART database.
Tanoh, I., & Napoletano, P. (2021). A novel 1-d ccanet for ecg classification. APPLIED SCIENCES, 11(6) [10.3390/app11062758].
Citazione: | Tanoh, I., & Napoletano, P. (2021). A novel 1-d ccanet for ecg classification. APPLIED SCIENCES, 11(6) [10.3390/app11062758]. | |
Tipo: | Articolo in rivista - Articolo scientifico | |
Carattere della pubblicazione: | Scientifica | |
Presenza di un coautore afferente ad Istituzioni straniere: | Si | |
Titolo: | A novel 1-d ccanet for ecg classification | |
Autori: | Tanoh, I; Napoletano, P | |
Autori: | NAPOLETANO, PAOLO (Corresponding) | |
Data di pubblicazione: | 2021 | |
Lingua: | English | |
Rivista: | APPLIED SCIENCES | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.3390/app11062758 | |
Appare nelle tipologie: | 01 - Articolo su rivista |