Naslov
A convolutional neural network based approach to QRS detection

Autori
Šarlija, Marko ; Jurišić, Fran ; Popović, Siniša

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
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, 2017, 121-125

ISBN
978-1-5090-4011-7

Skup
10th International Symposium on Image and Signal Processing and Analysis

Mjesto i datum
Ljubljana, Slovenija, 18.09.2017. - 20.09.2017

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
electrocardiogram (ECG), QRS complex detection, convolutional neural networks (CNN), clustering

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA