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Memorias de investigación
Artículos en revistas:
Classification of auditory brainstem responses through symbolic pattern discovery
Año:2016
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
ABSTRACT. Introduction: Numeric time series are present in a very wide range of domains, including many branches of medicine. Data mining techniques have proved to be useful for knowledge discovery in this type of data and for supporting decision-making processes. Objectives: The overall objective is to classify time series based on the discovery of frequent patterns.These patterns will be discovered in symbolic sequences obtained from the time series data by means of a temporal abstraction process. Methods: Firstly, we transform numeric time series into symbolic time sequences, where the symbols aim to represent the relevant domain concepts. These symbols can be defined using either public or expert domain knowledge. Then we apply a symbolic pattern discovery technique to the output symbolic sequences. This technique identifies the subsequences frequently found in a population group. These subsequences (patterns) are representative of population groups. Finally, we employ a classification technique based on the identified patterns in order to classify new individuals. Thanks to the inclusion of domain knowledge, the classification results can be explained using domain terminology. This makes the results easier to interpret for the domain specialist (physician). Results: This method has been applied to brainstem auditory evoked potentials (BAEPs) time series. Preliminary experiments were carried out to analyse several aspects of the method including the best configuration of the pattern discovery technique parameters. We then applied the method to the BAEPs of 83 individuals belonging to four classes (healthy, conductive hearing loss, vestibular schwannoma--brainstem involvement and vestibular schwannoma--8th-nerve involvement). According to the results of the cross-validation, overall accuracy was 99.4%, sensitivity (recall) was 97.6% and specificity was 100% (no false positives). Conclusion: The proposed method effectively reduces dimensionality. Additionally, if the symbolic transformation includes the right domain knowledge, the method arguably outputs a data representation that denotes the relevant domain concepts more clearly. The method is capable of finding patterns in BAEPs time series and is very accurate at correctly predicting whether or not new patients have anauditory-related disorder.
Internacional
Si
JCR del ISI
Si
Título de la revista
Artificial Intelligence in Medicine
ISSN
0933-3657
Factor de impacto JCR
2,142
Información de impacto
Datos del JCR de 2015: - 34/130 (Q2) COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - 7/20 (Q2) MEDICAL INFORMATICS
Volumen
70
DOI
10.1016/j.artmed.2016.05.001
Número de revista
Desde la página
12
Hasta la página
30
Mes
JUNIO
Ranking
- COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE: 34/130 (Q2) - MEDICAL INFORMATICS: 7/20 (Q2)
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Marco Eduardo Molina Bustamante (UPM)
  • Autor: Aurora Perez Perez (UPM)
  • Autor: Juan Pedro Caraca-Valente Hernandez (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Investigación en Tecnología Informática y de las Comunicaciones: CETTICO
  • Departamento: Lenguajes y Sistemas Informáticos e Ingeniería de Software
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