Missing data imputation techniques for wireless continuous vital signs monitoring.
J Clin Monit Comput
; 37(5): 1387-1400, 2023 10.
Article
em En
| MEDLINE
| ID: mdl-36729298
ABSTRACT
Wireless vital signs sensors are increasingly used for remote patient monitoring, but data analysis is often challenged by missing data periods. This study explored the performance of various imputation techniques for continuous vital signs measurements. Wireless vital signs measurements (heart rate, respiratory rate, blood oxygen saturation, axillary temperature) from surgical ward patients were used for repeated random simulation of missing data periods (gaps) of 5-60 min in two-hour windows. Gaps were imputed using linear interpolation, spline interpolation, last observation- and mean carried forwards technique, and cluster-based prognosis. Imputation performance was evaluated using the mean absolute error (MAE) between original and imputed gap samples. Besides, effects on signal features (window's slope, mean) and early warning scores (EWS) were explored. Gaps were simulated in 1743 data windows, obtained from 52 patients. Although MAE ranges overlapped, median MAE was structurally lowest for linear interpolation (heart rate 0.9-2.6 beats/min, respiratory rate 0.8-1.8 breaths/min, temperature 0.04-0.17 °C, oxygen saturation 0.3-0.7% for 5-60 min gaps) but up to twice as high for other techniques. Three techniques resulted in larger ranges of signal feature bias compared to no imputation. Imputation led to EWS misclassification in 1-8% of all simulations. Imputation error ranges vary between imputation techniques and increase with gap length. Imputation may result in larger signal feature bias compared to performing no imputation, and can affect patient risk assessment as illustrated by the EWS. Accordingly, careful implementation and selection of imputation techniques is warranted.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Sinais Vitais
/
Taxa Respiratória
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
J Clin Monit Comput
Assunto da revista:
INFORMATICA MEDICA
/
MEDICINA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Holanda