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01.12.2012 | Technical advance | Ausgabe 1/2012 Open Access

BMC Medical Informatics and Decision Making 1/2012

Using n-gram analysis to cluster heartbeat signals

BMC Medical Informatics and Decision Making > Ausgabe 1/2012
Yu-Chen Huang, Hanjun Lin, Yeh-Liang Hsu, Jun-Lin Lin
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1472-6947-12-64) contains supplementary material, which is available to authorized users.

Competing interests

The authors have no competing interests.

Authors’ contributions

YCH, HL and JLL conceived the study. YCH and HL participated in the acquisition of data, designed the experiment, wrote the program, and drafted the manuscript. YLH revised and restructured the study and the manuscript. All of authors read and approved the final manuscript.



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.


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.


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.


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.
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