TY - JOUR
T1 - Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology
AU - Amer, Ahmed Youssef Ali
AU - Wouters, Femke
AU - Vranken, Julie
AU - de Korte-de Boer, Dianne
AU - Smit-Fun, Valerie
AU - Duflot, Patrick
AU - Beaupain, Marie-Helene
AU - Vandervoort, Pieter
AU - Luca, Stijn
AU - Aerts, Jean-Marie
AU - Vanrumste, Bart
N1 - Funding Information:
This research is funded by a European Union Grant through wearIT4health project. The wearIT4health project is being carried out within the context of the Interreg V-A Euregio Meuse-Rhine programme, with EUR 2,3 million coming from the European Regional Development Fund (ERDF). With the investment of EU funds in Interreg projects, the European Union directly invests in economic development, innovation, territorial development, social inclusion, and education in the Euregio Meuse-Rhine.
Funding Information:
Funding: This research is funded by a European Union Grant through wearIT4health project. The wearIT4health project is being carried out within the context of the Interreg V-A Euregio Meuse-Rhine programme, with EUR 2,3 million coming from the European Regional Development Fund (ERDF). With the investment of EU funds in Interreg projects, the European Union directly invests in economic development, innovation, territorial development, social inclusion, and education in the Euregio Meuse-Rhine.
Publisher Copyright:
©2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/11/2
Y1 - 2020/11/2
N2 - In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients' vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.
AB - In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients' vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.
KW - vital signs
KW - early warning score
KW - time-series prediction
KW - kNN-LS-SVM
KW - wearable technology
KW - NOVELTY DETECTION
KW - SUPPORT
KW - ANTECEDENTS
KW - RISK
U2 - 10.3390/s20226593
DO - 10.3390/s20226593
M3 - Article
C2 - 33218084
SN - 1424-8220
VL - 20
JO - Sensors
JF - Sensors
IS - 22
M1 - 6593
ER -