Validity of Ultra-Short-Term HRV Analysis Using PPG-A Preliminary Study.

Continuous measurement of heart rate variability (HRV) in the short and ultra-short-term using wearable devices allows monitoring of physiological status and prevention of diseases. This study aims to evaluate the agreement of HRV features between a commercial device (Bora Band, Biosency) measuring photoplethysmography (PPG) and reference electrocardiography (ECG) and to assess the validity of ultra-short-term HRV as a surrogate for short-term HRV features. PPG and ECG recordings were acquired from 5 healthy subjects over 18 nights in total. HRV features include time-domain, frequency-domain, nonlinear, and visibility graph features and are extracted from 5 min 30 s and 1 min 30 s duration PPG recordings. The extracted features are compared with reference features of 5 min 30 s duration ECG recordings using repeated-measures correlation, Bland-Altman plots with 95% limits of agreements, Cliff's delta, and an equivalence test. Results showed agreement between PPG recordings and ECG reference recordings for 37 out of 48 HRV features in short-term durations. Sixteen of the forty-eight HRV features were valid and retained very strong correlations, negligible to small bias, with statistical equivalence in the ultra-short recordings (1 min 30 s). The current study concludes that the Bora Band provides valid and reliable measurement of HRV features in short and ultra-short duration recordings.

Agreement Between StepWatch3 and ActiGraph wGT3X+ for Measuring Step-Based Metrics in People With Peripheral Artery Disease.

The authors investigated the agreement between StepWatch3™ (SW3) and ActiGraph™ wGT3X+ monitors for measuring step-based metrics in patients with peripheral artery disease and older adults. In 23 patients with peripheral artery disease and 38 older participants, the authors compared the metrics obtained during an outdoor (400-m track) walking session (step count) and a 7-day free-living period (step count and 60/30/5/1-min maximal or peak step accumulation) using the SW3 (ankle) and the wGT3X+ (hip) with the low-frequency extension filter enabled (wGT3X+/LFE) or not (wGT3X+/N). During outdoor walking session, agreement was high, particularly for wGT3X+/LFE: correlations ≥.98, median absolute percentage errors <1%, and significant equivalence using a ± 15% equivalence zone or narrower. In free living, no wGT3X+ method was equivalent to SW3 for step count. The wGT3X+/LFE was equivalent to SW3 regarding all step accumulation metrics using a ± 20% equivalence zone or narrower, with median absolute percentage errors <11%. The wGT3X+/LFE method is the best option for comparisons with SW3 in peripheral artery disease and older adults.

Comparison of Activity Monitors Accuracy in Assessing Intermittent Outdoor Walking.

PURPOSE: This study aimed to determine and compare the accuracy of different activity monitors in assessing intermittent outdoor walking in both healthy and clinical populations through the development and validation of processing methodologies. METHODS: In study 1, an automated algorithm was implemented and tested for the detection of short (≤1 min) walking and stopping bouts during prescribed walking protocols performed by healthy subjects in environments with low and high levels of obstruction. The following parameters obtained from activity monitors were tested, with different recording epochs0.1s/0.033s/1s/3s/10s and wearing locationsscapula/hip/wrist/ankle: GlobalSat DG100 (GS) and Qstarz BT-Q1000XT/-Q1000eX (QS) speed; ActiGraph wGT3X+ (AG) vector magnitude (VM) raw data, VM counts, and steps; and StepWatch3 (SW) steps. Furthermore, linear mixed models were developed to estimate walking speeds and distances from the monitors parameters. Study 2 validated the performance of the activity monitors and processing methodologies in a clinical population showing profile of intermittent walking due to functional limitations during outdoor walking sessions. RESULTS: In study 1, GS1s, scapula, QS1s, scapula/wrist speed, and AG0.033s, hip VM raw data provided the highest bout detection rates (>96.7%) and the lowest root mean square errors in speed (≤0.4 km·h-1) and distance (<18 m) estimation. Using SW3s, ankle steps, the root mean square error for walking/stopping duration estimation reached 13.6 min using proprietary software and 0.98 min using our algorithm (total recording duration, 282 min). In study 2, using AG0.033s, hip VM raw data, the bout detection rate (95% confidence interval) reached 100% (99%-100%), and the mean (SD) absolute percentage errors in speed and distance estimation were 9% (6.6%) and 12.5% (7.9%), respectively. CONCLUSIONS: GPS receivers and AG demonstrated high performance in assessing intermittent outdoor walking in both healthy and clinical populations.

Early-warning of ARDS using novelty detection and data fusion.

Acute respiratory distress syndrome (ARDS) is a critical condition that disturbs the respiratory system and may lead to death. Early identification of this syndrome is crucial for the implementation of preventive measures. The present paper focuses on the prediction of the onset of this syndrome using physiological records of patients. Heart rate, respiratory rate, peripheral arterial oxygen saturation and mean airway blood pressure were considered. The method proposed in this paper uses first distance-based novelty detection that allows detecting deviations from normal states for each signal. Then, linear and nonlinear kernel-based data fusion algorithms are introduced to combine the individual signal decisions. The proposed method is evaluated using the MIMIC II physiological database. As a result, ARDS is detected in the early phases of occurrence with sensitivity and specificity of 65% and 100% respectively for the combination of all the signals in study. Moreover, the proposed method outperforms current state-of-the-art methods in real-time surveillance of ARDS using only physiological data with an average prediction before 39 h of onset.