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 86.51 CONCLUSIONS: 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.

DaSCI Andalusian Institute of Data Science and Computational Intelligence University of Granada Granada Spain

Faculty of Information Technology HUTECH University Ho Chi Minh City 700000 Vietnam

Faculty of Science University of Hradec Kralove Rokitanskeho 62 Hradec Kralove 50003 Czech Republic

Malaysia Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur 54100 Malaysia

Regional Research Center Iwate Prefectural University Iwate 0200611 Japan

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