BMC Med Inform Decis Mak - Using n-gram analysis to cluster heartbeat signals.

Tópicos

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Resumo

CKGROUND: Biological signals may carry specific characteristics that reflect basic dynamics of the body. In particular, heart beat signals carry specific signatures that are related to human physiologic mechanisms. In recent years, many researchers have shown that representations which used non-linear symbolic sequences can often reveal much hidden dynamic information. This kind of symbolization proved to be useful for predicting life-threatening cardiac diseases.METHODS: This paper presents an improved method called the "Adaptive Interbeat Interval Analysis (AIIA) method". The AIIA method uses the Simple K-Means algorithm for symbolization, which offers a new way to represent subtle variations between two interbeat intervals without human intervention. After symbolization, it uses the n-gram algorithm to generate different kinds of symbolic sequences. Each symbolic sequence stands for a variation phase. Finally, the symbolic sequences are categorized by classic classifiers.RESULTS: In the experiments presented in this paper, AIIA method achieved 91% (3-gram, 26 clusters) accuracy in successfully classifying between the patients with Atrial Fibrillation (AF), Congestive Heart Failure (CHF) and healthy people. It also achieved 87% (3-gram, 26 clusters) accuracy in classifying the patients with apnea.CONCLUSIONS: The two experiments presented in this paper demonstrate that AIIA method can categorize different heart diseases. Both experiments acquired the best category results when using the Bayesian Network. For future work, the concept of the AIIA method can be extended to the categorization of other physiological signals. More features can be added to improve the accuracy.

Resumo Limpo

ckground biolog signal may carri specif characterist reflect basic dynam bodi particular heart beat signal carri specif signatur relat human physiolog mechan recent year mani research shown represent use nonlinear symbol sequenc can often reveal much hidden dynam inform kind symbol prove use predict lifethreaten cardiac diseasesmethod paper present improv method call adapt interbeat interv analysi aiia method aiia method use simpl kmean algorithm symbol offer new way repres subtl variat two interbeat interv without human intervent symbol use ngram algorithm generat differ kind symbol sequenc symbol sequenc stand variat phase final symbol sequenc categor classic classifiersresult experi present paper aiia method achiev gram cluster accuraci success classifi patient atrial fibril af congest heart failur chf healthi peopl also achiev gram cluster accuraci classifi patient apneaconclus two experi present paper demonstr aiia method can categor differ heart diseas experi acquir best categori result use bayesian network futur work concept aiia method can extend categor physiolog signal featur can ad improv accuraci

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