Classification of health deterioration by geometric invariants
Metadatos
Mostrar el registro completo del ítemEditorial
Elsevier
Materia
Deterioration detection Ballistocardiography Cartan curvature Convolutional neural network Piezoeceramic sensor
Fecha
2023-09Referencia bibliográfica
D. Cimr, D. Busovsky, H. Fujita et al. Classification of health deterioration by geometric invariants. Computer Methods and Programs in Biomedicine 239 (2023) 107623. [https://doi.org/10.1016/j.cmpb.2023.107623]
Patrocinador
Operational Programme "Development of the Internal Grant Agency of the University of Hradec Kralove" CZ.02.2.69/0.0/0.0/19_073/0016949, IGRA-TYM-2021008; Centre of Creative Activities and Knowledge Transfer at Uni- versity Hradec Kralove; State budget of the Technology Agency of the Czech Republic; Centre of Creative Activities and Knowledge Transfer at University Hradec Kralove; Excellence project PrF UHK; TP01010032, 2215/2023-2024Resumen
Background and Objectives: Prediction of patient deterioration is essential in medical care, and its automation may reduce the risk of patient death. The precise monitoring of a patient's medical state requires devices placed on the body, which may cause discomfort. Our approach is based on the processing of long-term ballistocardiography data, which were measured using a sensory pad placed under the patient's mattress.Methods: The investigated dataset was obtained via long-term measurements in retirement homes and intensive care units (ICU). Data were measured unobtrusively using a measuring pad equipped with piezoceramic sensors. The proposed approach focused on the processing methods of the measured ballistocardiographic signals, Cartan curvature (CC), and Euclidean arc length (EAL).Results: For analysis, 218,979 normal and 216,259 aberrant 2-second samples were collected and classified using a convolutional neural network. Experiments using cross-validation with expert threshold and data length revealed the accuracy, sensitivity, and specificity of the proposed method to be 86.51Conclusions: The proposed method provides a unique approach for an early detection of health concerns in an unobtrusive manner. In addition, the suitability of EAL over the CC was determined.