The PhysioNet 2020 Challenge focused on the automatic classification of 27 cardiac abnormalities (CAs) from 12-lead ECG signals. We investigated on a hybrid approach, combining average-template-based algorithms with deep neural networks (DNNs), to build an ensemble classification model. We calibrated the model on the available 40,000+ ECGs, while organizers tested the model on a private test set. Standard ECG preprocessing was applied. For ECGs related to CAs altering the ECG morphology, multi-lead average P, QRS, and T segments were computed. For signals associated with irregular rhythms, time dependent features were computed. The ensemble model comprised of: i) three DNNs to classify morphology-related CAs. ii) a fully connected neural network to classify irregular rhythm; and iii) a threshold-based classifier for premature ventricular beat detection. The organizers designed a score for ranking the models. The ensemble model proposed by our team 'BiSP Lab' reached the 40th position, and obtained a score of -0.179 on the private test set. Despite the low performance obtained on the private test set, our ensemble model showed potential for classification of CAs from ECGs.
Classification of 12-lead ECG with an Ensemble Machine Learning Approach / M. Bodini, M.W. Rivolta, R. Sassi - In: Computing in Cardiology[s.l] : IEEE Computer Society, 2020. - ISBN 9781728173825. - pp. 1-4 (( convegno CinC tenutosi a Rimini nel 2020.
Classification of 12-lead ECG with an Ensemble Machine Learning Approach
M. Bodini
Primo
;M.W. Rivolta;R. SassiUltimo
2020
Abstract
The PhysioNet 2020 Challenge focused on the automatic classification of 27 cardiac abnormalities (CAs) from 12-lead ECG signals. We investigated on a hybrid approach, combining average-template-based algorithms with deep neural networks (DNNs), to build an ensemble classification model. We calibrated the model on the available 40,000+ ECGs, while organizers tested the model on a private test set. Standard ECG preprocessing was applied. For ECGs related to CAs altering the ECG morphology, multi-lead average P, QRS, and T segments were computed. For signals associated with irregular rhythms, time dependent features were computed. The ensemble model comprised of: i) three DNNs to classify morphology-related CAs. ii) a fully connected neural network to classify irregular rhythm; and iii) a threshold-based classifier for premature ventricular beat detection. The organizers designed a score for ranking the models. The ensemble model proposed by our team 'BiSP Lab' reached the 40th position, and obtained a score of -0.179 on the private test set. Despite the low performance obtained on the private test set, our ensemble model showed potential for classification of CAs from ECGs.File | Dimensione | Formato | |
---|---|---|---|
CinC2020-406.pdf
accesso aperto
Tipologia:
Publisher's version/PDF
Dimensione
442.21 kB
Formato
Adobe PDF
|
442.21 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.