Validation of a wrist monitor for accurate estimation of RR intervals during sleep

Wearable sensors for prediction of intraamniotic infection in women with preterm premature rupture of membranes: a prospective proof of principle study

PURPOSE

To evaluate the use of wearable sensors for prediction of intraamniotic infection in pregnant women with PPROM.

MATERIALS AND METHODS

In a prospective proof of principle study, we included 50 patients diagnosed with PPROM at the University Hospital Zurich between November 2017 and May 2020. Patients were instructed to wear a bracelet during the night, which measures physiological parameters including wrist skin temperature, heart rate, heart rate variability, and breathing rate. A two-way repeated measures ANOVA was performed to evaluate the difference over time of both the wearable device measured parameters and standard clinical monitoring values, such as body temperature, pulse, leucocytes, and C-reactive protein, between women with and without intraamniotic infection.

RESULTS

Altogether, 23 patients (46%) were diagnosed with intraamniotic infection. Regarding the physiological parameters measured with the bracelet, we observed a significant difference in breathing rate (19 vs 16 per min, P < .01) and heart rate (72 vs 67 beats per min, P = .03) in women with intraamniotic infection compared to those without during the 3 days prior to birth. In parallel to these changes standard clinical monitoring values were significantly different in the intraamniotic infection group compared to women without infection in the 3 days preceding birth.

CONCLUSION

Our results suggest that wearable sensors are a promising, noninvasive, patient friendly approach to support the early detection of intraamniotic infection in women with PPROM. However, confirmation of our findings in larger studies is required before implementing this technique in standard clinical management.

A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia

Objective: We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR. Methods: Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung’s Gear S3 and Galaxy Watch 3. Results: The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors—30% and 66% lower—and mean heart rate and mean interbeat interval estimation errors—60% and 77% lower—when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias. Conclusion: The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks. Significance: By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data.

Measuring Heart Rate Variability in Free-Living Conditions Using Consumer-Grade Photoplethysmography: Validation Study

Background

Heart rate variability (HRV) is used to assess cardiac health and autonomic nervous system capabilities. With the growing popularity of commercially available wearable technologies, the opportunity to unobtrusively measure HRV via photoplethysmography (PPG) is an attractive alternative to electrocardiogram (ECG), which serves as the gold standard. PPG measures blood flow within the vasculature using color intensity. However, PPG does not directly measure HRV; it measures pulse rate variability (PRV). Previous studies comparing consumer-grade PRV with HRV have demonstrated mixed results in short durations of activity under controlled conditions. Further research is required to determine the efficacy of PRV to estimate HRV under free-living conditions.

Objective

This study aims to compare PRV estimates obtained from a consumer-grade PPG sensor with HRV measurements from a portable ECG during unsupervised free-living conditions, including sleep, and examine factors influencing estimation, including measurement conditions and simple editing methods to limit motion artifacts.

Methods

A total of 10 healthy adults were recruited. Data from a Microsoft Band 2 and a Shimmer3 ECG unit were recorded simultaneously using a smartphone. Participants wore the devices for >90 min during typical day-to-day activities and while sleeping. After filtering, ECG data were processed using a combination of discrete wavelet transforms and peak-finding methods to identify R-R intervals. P-P intervals were edited for deletion using methods based on outlier detection and by removing sections affected by motion artifacts. Common HRV metrics were compared, including mean N-N, SD of N-N intervals, percentage of subsequent differences >50 ms (pNN50), root mean square of successive differences, low-frequency power (LF), and high-frequency power. Validity was assessed using root mean square error (RMSE) and Pearson correlation coefficient (R2).

Results

Data sets for 10 days and 9 corresponding nights were acquired. The mean RMSE was 182 ms (SD 48) during the day and 158 ms (SD 67) at night. R2 ranged from 0.00 to 0.66, with 2 of 19 (2 nights) trials considered moderate, 7 of 19 (2 days, 5 nights) fair, and 10 of 19 (8 days, 2 nights) poor. Deleting sections thought to be affected by motion artifacts had a minimal impact on the accuracy of PRV measures. Significant HRV and PRV differences were found for LF during the day and R-R, SDNN, pNN50, and LF at night. For 8 of the 9 matched day and night data sets, R2 values were higher at night (P=.08). P-P intervals were less sensitive to rapid R-R interval changes.

Conclusions

Owing to overall poor concurrent validity and inconsistency among participant data, PRV was found to be a poor surrogate for HRV under free-living conditions. These findings suggest that free-living HRV measurements would benefit from examining alternate sensing methods, such as multiwavelength PPG and wearable ECG.

Actigraphy to Evaluate Sleep in the Intensive Care Unit. A Systematic Review

RATIONALE

Poor sleep quality is common in the intensive care unit (ICU) and may be associated with adverse outcomes. Hence, ICU-based efforts to promote sleep are gaining attention, motivating interest in methods to measure sleep in critically ill patients. Actigraphy evaluates rest and activity by algorithmically processing gross motor activity data, usually collected by a noninvasive wristwatch-like accelerometer device. In critically ill patients, actigraphy has been used as a surrogate measure of sleep; however, its use has not been systematically reviewed.

OBJECTIVES

To conduct a systematic review of ICU-based studies that used actigraphy as a surrogate measure of sleep, including its feasibility, validity, and reliability as a measure of sleep in critically ill patients.

METHODS

We searched PubMed, EMBASE, CINAHL, Proquest, and Web of Science for studies that used actigraphy to evaluate sleep in five or more patients in an ICU setting.

RESULTS

Our search yielded 4,869 citations, with 13 studies meeting eligibility criteria. These 13 studies were conducted in 10 countries, and eight (62%) were published since 2008. Across the 13 studies, the mean total sleep time of patients in the ICU, as estimated using actigraphy, ranged from 4.4 to 7.8 hours at nighttime and from 7.1 to 12.1 hours over a 24-hour period, with 1.4 to 49.0 mean nocturnal awakenings and a sleep efficiency of 61 to 75%. When compared side-by-side with other measures of sleep (polysomnography, nurse assessments, and patient questionnaires), actigraphy consistently yielded higher total sleep time and sleep efficiency, fewer nighttime awakenings (vs. polysomnography), and more overall awakenings (vs. nurse assessment and patient questionnaires). None of the studies evaluated the association between actigraphy-based measures of sleep and outcomes of patients in the ICU.

CONCLUSIONS

In critically ill patients, actigraphy is being used more frequently as a surrogate measure of sleep; however, because actigraphy only measures gross motor activity, its ability to estimate sleep is limited by the processing algorithm used. Prior ICU-based studies involving actigraphy were heterogeneous and lacked data regarding actigraphy-based measures of sleep and patient outcomes. Larger, more rigorous and standardized studies are needed to better understand the role of actigraphy in evaluating sleep and sleep-related outcomes in critically ill patients.

Actigraphy to Measure Physical Activity in the Intensive Care Unit: A Systematic Review

OBJECTIVE

In the intensive care unit (ICU), prolonged inactivity is common, increasing patients' risk for adverse outcomes, including ICU-acquired weakness. Hence, interventions to minimize inactivity are gaining popularity, highlighting actigraphy, a measure of activity involving a wristwatch-like accelerometer, as a method to inform these efforts. Therefore, we performed a systematic review of studies that used actigraphy to measure patient activity in the ICU setting.

DATA SOURCES

We searched PubMed, EMBASE, CINAHL, Cochrane Library, and ProQuest from inception until December 2016.

STUDY SELECTION

Two reviewers independently screened studies for inclusion. A study was eligible for inclusion if it was published in a peer-reviewed journal and used actigraphy to measure activity in ≥5 ICU patients.

DATA EXTRACTION

Two reviewers independently performed data abstraction and risk of bias assessment. Abstracted actigraphy-based activity data included total activity time and activity counts.

RESULTS

Of 16 studies (607 ICU patients) identified, 14 (88%) were observational, 2 (12%) were randomized control trials, and 5 (31%) were published after 2009. Mean patient activity levels per 15 to 60 second epoch ranged from 25 to 37 daytime and 2 to 19 nighttime movements. Actigraphy was evaluated in the context of ICU and post-ICU outcomes in 11 (69%) and 5 (31%) studies, respectively, and demonstrated potential associations between actigraphy-based activity levels and delirium, sedation, pain, anxiety, time to extubation, and length of stay.

CONCLUSION

Actigraphy has demonstrated that patients are profoundly inactive in the ICU with actigraphy-based activity levels potentially associated with important measures, such as delirium, sedation, and length of stay. Larger and more rigorous studies are needed to further evaluate these associations and the overall utility of actigraphy in the ICU setting.

The Accuracy of Atrial Fibrillation Detection from Wrist Photoplethysmography. A Study on Post-Operative Patients

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Although not life-threatening itself, AF significantly increases the risk of stroke and myocardial infarction. Current tools available for screening and monitoring of AF are inadequate and an unobtrusive alternative, suitable for long-term use, is needed. This paper evaluates an atrial fibrillation detection algorithm based on wrist photoplethysmographic (PPG) signals. 29 patients recovering from surgery in the post-anesthesia care unit were monitored. 15 patients had sinus rhythm (SR, 67.5± 10.7 years old, 7 female) and 14 patients had AF (74.8± 8.3 years old, 8 female) during the recordings. Inter-beat intervals (IBI) were estimated from PPG signals. As IBI estimation is highly sensitive to motion or other types of noise, acceleration signals and PPG waveforms were used to automatically detect and discard unreliable IBI. AF was detected from windows of 20 consecutive IBI with 98.45±6.89% sensitivity and 99.13±1.79% specificity for 76.34±19.54% of the time. For the remaining time, no decision was taken due to the lack of reliable IBI. The results show that wrist PPG is suitable for long term monitoring and AF screening. In addition, this technique provides a more comfortable alternative to ECG devices.

Respiratory and cardiac monitoring at night using a wrist wearable optical system

Sleep monitoring provides valuable insights into the general health of an individual and helps in the diagnostic of sleep-derived illnesses. Polysomnography, is considered the gold standard for such task. However, it is very unwieldy and therefore not suitable for long-term analysis. Here, we present a non-intrusive wearable system that, by using photoplethysmography, it can estimate beat-to-beat intervals, pulse rate, and breathing rate reliably during the night. The performance of the proposed approach was evaluated empirically in the Department of Psychology at the University of Fribourg. Each participant was wearing two smart-bracelets from Ava as well as a complete polysomnographic setup as reference. The resulting mean absolute errors are 17.4ms (MAPE 1.8%) for the beat-to-beat intervals, 0.13beats-per-minute (MAPE 0.20%) for the pulse rate, and 0.9breaths-per-minute (MAPE 6.7%) for the breath rate.

A review on wearable photoplethysmography sensors and their potential future applications in health care

Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes. PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation. Recently, there has been much interest from numerous researchers around the globe to extract further valuable information from the PPG signal in addition to heart rate estimation and pulse oxymetry readings. PPG signal’s second derivative wave contains important health-related information. Thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness. Moreover, investigating the second derivative wave of PPG signal can also assist in early detection and diagnosis of various cardiovascular illnesses that may possibly appear later in life. For early recognition and analysis of such illnesses, continuous and real-time monitoring is an important approach that has been enabled by the latest technological advances in sensor technology and wireless communications. The aim of this article is to briefly consider some of the current developments and challenges of wearable PPG-based monitoring technologies and then to discuss some of the potential applications of this technology in clinical settings.

Evaluation of the beat-to-beat detection accuracy of PulseOn wearable optical heart rate monitor

Heart rate variability (HRV) provides significant information about the health status of an individual. Optical heart rate monitoring is a comfortable alternative to ECG based heart rate monitoring. However, most available optical heart rate monitoring devices do not supply beat-to-beat detection accuracy required by proper HRV analysis. We evaluate the beat-to-beat detection accuracy of a recent wrist-worn optical heart rate monitoring device, PulseOn (PO). Ten subjects (8 male and 2 female; 35.9±10.3 years old) participated in the study. HRV was recorded with PO and Firstbeat Bodyguard 2 (BG2) device, which was used as an ECG based reference. HRV was recorded during sleep. As compared to BG2, PO detected on average 99.57% of the heartbeats (0.43% of beats missed) and had 0.72% extra beat detection rate, with 5.94 ms mean absolute error (MAE) in beat-to-beat intervals (RRI) as compared to the ECG based RRI BG2. Mean RMSSD difference between PO and BG2 derived HRV was 3.1 ms. Therefore, PO provides an accurate method for long term HRV monitoring during sleep.

Comparison between Electrocardiographic and Earlobe Pulse Photoplethysmographic Detection for Evaluating Heart Rate Variability in Healthy Subjects in Short- and Long-Term Recordings

Heart rate variability (HRV) is commonly used to assess autonomic functions and responses to environmental stimuli. It is usually derived from electrocardiographic signals; however, in the last few years, photoplethysmography has been successfully used to evaluate beat-to-beat time intervals and to assess changes in the human heart rate under several conditions. The present work describes a simple design of a photoplethysmograph, using a wearable earlobe sensor. Beat-to-beat time intervals were evaluated as the time between subsequent pulses, thus generating a signal representative of heart rate variability, which was compared to RR intervals from classic electrocardiography. Twenty-minute pulse photoplethysmography and ECG recordings were taken simultaneously from 10 healthy individuals. Ten additional subjects were recorded for 24 h. Comparisons were made of raw signals and on time-domain and frequency-domain HRV parameters. There were small differences between the inter-beat intervals evaluated with the two techniques. The current findings suggest that our wearable earlobe pulse photoplethysmograph may be suitable for short and long-term home measuring and monitoring of HRV parameters.

Determination of saturation, heart rate, and respiratory rate at forearm using a Nellcor™ forehead SpO2-saturation sensor

Alterations in arterial blood oxygen saturation, heart rate (HR), and respiratory rate (RR) are strongly associated with intra-hospital cardiac arrests and resuscitations. A wireless, easy-to-use, and comfortable method for monitoring these important clinical signs would be highly useful. We investigated whether the Nellcor™ OxiMask MAX-FAST forehead sensor could provide data for vital sign measurements when located at the distal forearm instead of its intended location at the forehead to provide improved comfortability and easy placement. In a prospective setting, we recruited 30 patients undergoing surgery requiring postoperative care. At the postoperative care unit, patients were monitored for two hours using a standard patient monitor and with a study device equipped with a Nellcor™ Forehead SpO₂ sensor. The readings were electronically recorded and compared in post hoc analysis using Bland-Altman plots, Spearman's correlation, and root-mean-square error (RMSE). Bland-Altman plot showed that saturation (SpO₂) differed by a mean of -0.2 % points (SD, 4.6), with a patient-weighted Spearman's correlation (r) of 0.142, and an RMSE of 4.2 points. For HR measurements, the mean difference was 0.6 bpm (SD, 2.5), r = 0.997, and RMSE = 1.8. For RR, the mean difference was -0.5 1/min (4.1), r = 0.586, and RMSE = 4.0. The SpO₂ readings showed a low mean difference, but also a low correlation and high RMSE, indicating that the Nellcor™ saturation sensor cannot reliably assess oxygen saturation at the forearm when compared to finger PPG measurements.