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Titolo:
Hierarchical state space partitioning with a network self-organising map for the recognition of ST-T segment changes
Autore:
Bezerianos, A; Vladutu, L; Papadimitriou, S;
Indirizzi:
Univ Patras, Sch Med, Dept Med Phys, GR-26110 Patras, Greece Univ Patras Patras Greece GR-26110 ept Med Phys, GR-26110 Patras, Greece Univ Patras, Dept Comp Engn & Informat, GR-26110 Patras, Greece Univ Patras Patras Greece GR-26110 n & Informat, GR-26110 Patras, Greece
Titolo Testata:
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
fascicolo: 4, volume: 38, anno: 2000,
pagine: 406 - 415
SICI:
0140-0118(200007)38:4<406:HSSPWA>2.0.ZU;2-M
Fonte:
ISI
Lingua:
ENG
Soggetto:
SUDDEN CARDIAC DEATH; WAVELET TRANSFORM; NEURAL NETWORKS; ECG ANALYSIS; ELECTROCARDIOGRAM; ALTERNANS; ISCHEMIA;
Keywords:
neural networks; automatic ischaemia detection; self-organising maps; learning vector quantisation; multilayer perceptrons; radial basis functions;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Engineering, Computing & Technology
Citazioni:
40
Recensione:
Indirizzi per estratti:
Indirizzo: Bezerianos, A Univ Patras, Sch Med, Dept Med Phys, GR-26110 Patras, GreeceUniv Patras Patras Greece GR-26110 GR-26110 Patras, Greece
Citazione:
A. Bezerianos et al., "Hierarchical state space partitioning with a network self-organising map for the recognition of ST-T segment changes", MED BIO E C, 38(4), 2000, pp. 406-415

Abstract

The problem of maximising the performance of ST-T segment automatic recognition for ischaemia detection is a difficult pattern classification problem. The paper proposes the network self-organising map (NetSOM) model as an enhancement to the Kohonen self-organised map (SOM) model. This model is capable of effectively decomposing complex large-scale pattern classification problems into a number of partitions, each of which is more manageable witha local classification device. The NetSOM attempts to generalise the regularisation and ordering potential of the basic SOM from the space of vectorsto the space of approximating functions. It becomes a device for the ordering of local experts (i.e, independent neural networks) over its lattice ofneurons and for their selection and co-ordination, Each local expert is anindependent neural network that is trained and activated under the controlof the NetSOM. This method is evaluated with examples from the European ST-T database. The first results obtained after the application of NetSOM to ST-T segment change recognition show a significant improvement in the performance compared with that obtained with monolithic approaches, i.e. with single network types. The basic SOM model has attained an average ischaemic beat sensitivity of 73.6% and an average ischaemic bear predictivity of 68.3%. The work reports and discusses the improvements that have been obtained from the implementation of a NetSOM classification system with both multilayer perceptrons and radial basis function (RBF) networks as local experts for the ST-T segment change problem. Specifically, the NetSOM with multilayer perceptrons (radial basis functions) as local experts has improved the results over the basic SOM to an average ischaemic beat sensitivity of 75.9% (77.7%) and an average ischaemic beat predictivity of 72.5% (74.1%).

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 15/07/20 alle ore 04:00:35