"This paper presents an application of Neural. Networks (NNs) and Support Vector Machines (SVMs) for the. detection and classification of heartbeats in electrocardiogram. (ECG) signals. The preprocessing algorithm for the beats. detection is based on well-known Pan-Tompkins’ algorithm. The. proposed approach is robust to different types of noise and. shows good performances both in beat analysis and QRS. morphology extraction. The proposed method in combination. with radial basis function SVM and adaptive NNs, brought. remarkable results on the classification of different kind of. cardiac arrhythmia as shown by suitable numerical simulations. presented at the end of the paper."
Conforto, S., Laudani, A., Oliva, F., RIGANTI FULGINEI, F., Schmid, M. (2013). Classification of ECG patterns for diagnostic purposes by means of neural networks and support vector machines. In Proceedings of the 36th International Conference on Telecommunications and Signal Processing, TSP 2013 (pp.591-595) [10.1109/TSP.2013.6614003].
Classification of ECG patterns for diagnostic purposes by means of neural networks and support vector machines
CONFORTO, SILVIA;LAUDANI, ANTONINO;RIGANTI FULGINEI, Francesco;SCHMID, Maurizio
2013-01-01
Abstract
"This paper presents an application of Neural. Networks (NNs) and Support Vector Machines (SVMs) for the. detection and classification of heartbeats in electrocardiogram. (ECG) signals. The preprocessing algorithm for the beats. detection is based on well-known Pan-Tompkins’ algorithm. The. proposed approach is robust to different types of noise and. shows good performances both in beat analysis and QRS. morphology extraction. The proposed method in combination. with radial basis function SVM and adaptive NNs, brought. remarkable results on the classification of different kind of. cardiac arrhythmia as shown by suitable numerical simulations. presented at the end of the paper."I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
