Classification of health deterioration by geometric invariants

The authors are grateful to the Operational Programme "Development of the Internal Grant Agency of the University of Hradec Kralove", reg. no. CZ.02.2.69/0.0/0.0/19_073/0016949, project no. IGRA-TYM-2021008 (investigators: Damian Busovsky and Katerina Voglova) .This study was also possible thanks to the project TP01010032 "The Centre of Creative Activities and Knowledge Transfer at University Hradec Kralove." This project was co -financed by the state budget of the Technology Agency of the Czech Republic under the GAMA 2 Progamme.Furthermore, the authors are grateful to the Excellence project PrF UHK 2215/2023-2024 for its financial support.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.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-2021008Centre of Creative Activities and Knowledge Transfer at Uni- versity Hradec KraloveState budget of the Technology Agency of the Czech RepublicCentre of Creative Activities and Knowledge Transfer at University Hradec KraloveExcellence project PrF UHKTP01010032, 2215/2023-202