A convolutional neural network based approach to QRS detection (CROSBI ID 671161)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Šarlija, Marko ; Jurišić, Fran ; Popović, Siniša
engleski
A convolutional neural network based approach to QRS detection
In this paper we present a QRS detection algorithm based on pattern recognition as well as a new approach to ECG baseline wander removal and signal normalization. Each point of the zero-centred and normalized ECG signal is a QRS candidate, while a 1-D CNN classifier serves as a decision rule. Positive outputs from the CNN are clustered to form final QRS detections. The data is obtained from the 44 non-pacemaker recordings of the MIT-BIH arrhythmia database. Classifier was trained on 22 recordings and the remaining ones are used for performance evaluation. Our method achieves a sensitivity of 99.81% and 99.93% positive predictive value, which is comparable with most state-of-the-art solutions. This approach opens new possibilities for improvements in heartbeat classification as well as P and T wave detection problems.
electrocardiogram (ECG), QRS complex detection, convolutional neural networks (CNN), clustering
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
121-125.
2017.
objavljeno
10.1109/ISPA.2017.8073581
Podaci o matičnoj publikaciji
Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis
Kovačić, Stanislav ; Lončarić, Sven ; Kristian, Matej ; Štruc, Vitomir ; Vučić, Mladen
Zagreb: Sveučilište u Zagrebu
978-1-5090-4011-7
Podaci o skupu
10th International Symposium on Image and Signal Processing and Analysis
poster
18.09.2017-20.09.2017
Ljubljana, Slovenija
Povezanost rada
Računarstvo