Abstract

Background/Purpose: Measuring vital signs in pediatric patients requires special consideration and adaptation due to varying anatomy and wide age range. In addition, children's anxiety, uncooperativeness, and high activity levels further complicate measurements, necessitating devices and algorithms designed to minimize the inaccuracies and discomfort. In this work, the performance of a custom wearable patch mounted on the mid-sternum was validated in uncontrolled settings on a cohort including 84 pediatric patients. Methods: Three-minute-long electrocardiogram (ECG), seismocardiogram (SCG) and photoplethysmogram (PPG) signals were acquired using the custom patch. First, pre-processing and signal smoothing algorithms were employed to suppress the out-of-band and motion noise. Two different tasks were then studied: (i) Heart rate (HR) and respiration rate were derived from the ECG, PPG and SCG signals individually. During HR derivation from the SCG, a novel Teager-energy-based HR estimation algorithm was proposed. (ii) Clinical relevance of the SCG signals was shown through mapping the SCG characteristics to body mass index (BMI) and blood pressure values. Results: While the best HR estimation was achieved through the PPG-infrared signal with an absolute error of 2.22.1 bpm, the best respiration estimation was achieved with PPG-Red signal with an error of 2.62.2 breaths/min. On the other hand, regression models resulted in a minimum of 85% confidence interval, revealing that the SCG characteristics indeed have salient correlation with the BMI and blood pressure values. Conclusion: Overall, such a system can potentially be leveraged in clinical practices to achieve more comfortable and accurate measurements.

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