COVID-19 Detection from Cough Recordings Using Bag-of-Words Classifiers.
Sensors (Basel)
; 23(11)2023 May 23.
Artículo
en Inglés
| MEDLINE | ID: covidwho-20241146
ABSTRACT
Reliable detection of COVID-19 from cough recordings is evaluated using bag-of-words classifiers. The effect of using four distinct feature extraction procedures and four different encoding strategies is evaluated in terms of the Area Under Curve (AUC), accuracy, sensitivity, and F1-score. Additional studies include assessing the effect of both input and output fusion approaches and a comparative analysis against 2D solutions using Convolutional Neural Networks. Extensive experiments conducted on the COUGHVID and COVID-19 Sounds datasets indicate that sparse encoding yields the best performances, showing robustness against various combinations of feature type, encoding strategy, and codebook dimension parameters.
Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Tos
/
COVID-19
Tipo de estudio:
Estudios diagnósticos
/
Estudio experimental
/
Estudio pronóstico
Límite:
Humanos
Idioma:
Inglés
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
S23114996
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