Comparación de Métodos de Extracción de Características Espectrales y Dispersas para Clasificación de Sonidos Cardíacos
DOI:
https://doi.org/10.17488/RMIB.44.4.1Palabras clave:
características espectrales, clasificación, matching pursuit, representación tiempo-frecuencia, sonidos cardiacosResumen
Las enfermedades cardiovasculares (ECVs) han persistido como la principal causa de mortalidad en el mundo. La señal de audio cardiaco o fonocardiograma (FCG) es la herramienta más simple, efectiva y de bajo costo para auxiliar a especialistas diagnosticando ECVs. Los avances en el procesamiento de señales y aprendizaje máquina han motivado el diseño de auscultación y detección computarizada. El objetivo de este trabajo es comparar el uso de características espectrales y dispersas para un sistema de clasificación que detecte la presencia/ausencia de una patología en un audio cardiaco mediante representaciones dispersas usando Matching Pursuit con diccionarios de Gabor tiempo-frequencia, predicción lineal y coeficientes cepstrales Mel. Se crearon 5 conjuntos de características como resultado de combinar las características para cada FCG y se examinó su desempeño usando un clasificador de bosque aleatorio (RF). Se aplicaron métodos de balanceo de muestras basados en sobremuestreo (SMOTE) y submuestreo aleatorio. Se compararon métodos de selección de características por correlación (CFS) y ganancia de información (IG) para reducir la dimensionalidad del conjunto. Los resultados muestran métricas de SE=93.17 %, SP=84.32 % y ACC=85.9 % al juntar los parámetros MP+LPC+MFCC además de una AUC=0.969. El trabajo muestra el potencial de las características espectrales y escasas para la detección de patologías en señales de audio cardiaco.
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