DOI: 10.5061/dryad.tb2rbp006
title: Reliability of heart rate and respiration rate measurements with a wireless accelerometer in postbariatric recovery

description: Recognition of early signs of deterioration in postoperative course could
 be improved by continuous monitoring of vital parameters. Wearable sensors
 could enable this by wireless transmission of vital signs. A novel
 accelerometer-based device, called Healthdot, has been designed to be worn
 on the skin to measure the two key vital parameters respiration rate
 (RespR) and heart rate (HeartR). The goal of this study is to assess the
 reliability of heart rate and respiration rate measured by the Healthdot
 in comparison to the gold standard, the bedside patient monitor, during
 the postoperative period in bariatric patients. Data were collected in a
 consecutive group of 30 patients who agreed to wear the device after their
 primary bariatric procedure. Directly after surgery, a Healthdot was
 attached on the patients’ left lower rib. Vital signs measured by the
 accelerometer based Healthdot were compared to vital signs collected with
 the gold standard patient monitor for the period that the patient stayed
 at the post-anesthesia care unit. Over all patients, a total of 22 hours
 of vital signs obtained by the Healthdot were recorded simultaneously with
 the bedside patient monitor data. 87.5% of the data met the pre-defined
 bias of 5 beats per minute for HeartR and 92.3 % of the data met the
 pre-defined bias of 5 respirations per minute for RespR. The Healthdot can
 be used to accurately derive heart rate and respiration rate in
 postbariatric patients. Wireless continuous monitoring of key vital signs
 has the potential to contribute to earlier recognition of complications in
 postoperative patients. Future studies should focus on the ability to
 detect patient deterioration in low-care environments and at home after
 discharge from the hospital.
description: All data collected were analyzed retrospectively after patients
 completed the study. The American National Standards Institute standard
 for cardiac monitors, heart rate meters, and alarms defines accuracy as a
 “readout error of no greater than ±10% of the input rate or ±5 beats per
 minute (bpm), whichever is greater”. Therefore, in this study the
 acceptable error between the measurements was set at 5 bpm for HeartR and
 5 respirations per minute (rpm) for RespR. Data management and analysis
 was performed using RStudio.    The Healthdot starts logging
 directly after activation. Only the periods when there was logging of
 patient parameters were evaluated during this study. For this comparative analysis,
 only the internally stored data of the Healthdot was evaluated, because it
 has a higher sampling frequency than the transmitted aggregated data.
 Because the sample frequencies of the HeartR and RespR generated by the
 Healthdot are different, the 8-sec HeartR data were resampled by linear
 interpolation between samples, obtaining a 1-sec interval for the HeartR
 data as well as the RespR data. Extracted reference from the
 patient monitor and Healthdot measurements were represented on the same
 time frequency (1 value/second) and then time-synchronized. The
 synchronization procedure included as first step a fixed time shift of the
 Healthdot measurements by applying the time lag corresponding to the
 maximum of the cross-correlation function between reference and Healthdot
 measurements. The second step corresponded to a visual inspection of the
 offset-corrected Healthdot measurement and the reference to fine tune the
 selected offset in three different instances of the recording so to
 identify via these offsets eventual clock drifts. Clock drift was defined
 as any progressive increase or decrease in the offset over time, which was
 then corrected by linear interpolation of the time offset along the
 measurement samples. Only intervals with quality index &gt; 0 (scale
 0-100) were retained. The vital signs of the Healthdot
 and the reference monitor were compared using the Bland-Altman method for
 repeated measurements. This method was used to account for within-subject
 variation by correcting for the variance of differences between the
 average differences across patients and the number of measurements per
 patient. The mean difference, or bias, between the wireless sensor and the
 reference monitor, and the 95% confidence interval (CI) (+/- 1.96 SD), or
 limits of agreement, were determined for both the HeartR and RespR data.
 Furthermore, the Pearson’s correlation coefficient was calculated to
 assess the strength of the association between the measurements of the
 Healthdot and the measurements of the reference patient monitor.
 Because outliers were observed in the data, error bars of the
 mean differences between the Healthdot and patient monitor, including
 their confidence interval, were made for each patient for both HeartR and
 RespR. These error bars were created on the data with a 1-sec interval as
 well as on the data over a 5-min average. The latter analysis was
 performed because the Healthdot is currently designed to average data and
 send that data package to the cloud every 5 minutes, which represents the
 intended performance in clinical use.