Pulse rate variability compared with Heart Rate Variability in children with and without sleep disordered breathing

Camera-Based Seismocardiogram for Heart Rate Variability Monitoring

Heart rate variability (HRV) is a crucial metric that quantifies the variation between consecutive heartbeats, serving as a significant indicator of autonomic nervous system (ANS) activity. It has found widespread applications in clinical diagnosis, treatment, and prevention of cardiovascular diseases. In this study, we proposed an optical model for defocused speckle imaging, to simultaneously incorporate out-of-plane translation and rotation-induced motion for highly-sensitive non-contact seismocardiogram (SCG) measurement. Using electrocardiogram (ECG) signals as the gold standard, we evaluated the performance of photoplethysmogram (PPG) signals and speckle-based SCG signals in assessing HRV. The results indicated that the HRV parameters measured from SCG signals extracted from laser speckle videos showed higher consistency with the results obtained from the ECG signals compared to PPG signals. Additionally, we confirmed that even when clothing obstructed the measurement site, the efficacy of SCG signals extracted from the motion of laser speckle patterns persisted in assessing the HRV levels. This demonstrates the robustness of camera-based non-contact SCG in monitoring HRV, highlighting its potential as a reliable, non-contact alternative to traditional contact-PPG sensors.

Clinical feasibility of an advanced neonatal epidermal multiparameter continuous monitoring technology in a large public maternity hospital in Nairobi, Kenya

Clinically feasible multiparameter continuous physiological monitoring technologies are needed for use in resource-constrained African healthcare facilities to allow for early detection of critical events and timely intervention for major morbidities in high-risk neonates. We conducted a prospective clinical feasibility study of a novel multiparameter continuous physiological monitoring technology in neonates at Pumwani Maternity Hospital in Nairobi, Kenya. To assess feasibility, we compared the performance of Sibel's Advanced Neonatal Epidermal (ANNE) technology to reference technologies, including Masimo's Rad-97 pulse CO-oximeter with capnography technology for heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO₂) measurements and Spengler's Tempo Easy non-contact infrared thermometer for temperature measurements. We evaluated key performance criteria such as up-time, clinical event detection performance, and the agreement of measurements compared to those from the reference technologies in an uncontrolled, real-world setting. Between September 15 and December 15, 2020, we collected and analyzed 503 h of ANNE data from 109 enrolled neonates. ANNE's up-time was 42 (11%) h more for HR, 77 (25%) h more for RR, and 6 (2%) h less for SpO₂ compared to the Rad-97. However, ANNE's ratio of up-time to total attached time was less than Rad-97's for HR (0.79 vs 0.86), RR (0.68 vs. 0.79), and SpO₂ (0.69 vs 0.86). ANNE demonstrated adequate performance in identifying high and low HR and RR and high temperature events; however, showed relatively poor performance for low SpO₂ events. The normalized spread of limits of agreement were 8.4% for HR and 52.2% for RR and the normalized root-mean-square deviation was 4.4% for SpO₂. Temperature agreement showed a spread of limits of agreement of 2.8 °C. The a priori-identified optimal limits were met for HR and temperature but not for RR or SpO₂. ANNE was clinically feasible for HR and temperature but not RR and SpO₂ as demonstrated by the technology's up-time, clinical event detection performance, and the agreement of measurements compared to those from the reference technologies.

OSA Patient Monitoring Based on the Beidou System

This paper presents an OSA patient interactive monitoring system based on the Beidou system. This system allows OSA patients to get timely rescue when they become sleepy outside. Because the Beidou position marker has an interactive function, it can reduce the anxiety of the patient while waiting for the rescue. At the same time, if a friend helps the OSA patients to call the doctor, the friend can also report the patient's condition in time. This system uses the popular IoT framework. At the bottom is the data acquisition layer, which uses wearable sensors to collect vital signs from patients, with a focus on ECG and SpO2 signals. The middle layer is the network layer that transmits the collected physiological signals to the Beidou indicator using the Bluetooth Low Energy (BLE) protocol. The top layer is the application layer, and the application layer uses the mature rescue interactive platform of Beidou. The Beidou system was developed by China itself, the main coverage of the satellite is in Asia, and is equipped with a high-density ground-based augmentation system. Therefore, the Beidou model improves the positioning accuracy and is equipped with a special communication satellite, which increases the short message interaction function. Therefore, patients can report disease progression in time while waiting for a rescue. After our simulation test, the effectiveness of the OSA patient rescue monitoring system based on the Beidou system and the positioning accuracy of OSA patients have been greatly improved. Especially when OSA patients work outdoors, the cell phone base station signal coverage is relatively weak. The satellite signal is well-covered, plus the SMS function of the Beidou indicator. Therefore, the system can be used to provide timely patient progress and provide data support for the medical rescue team to provide a more accurate rescue plan. After a comparative trial, the rescue rate of OSA patients using the detection device of this system was increased by 15 percentage points compared with the rescue rate using only GPS satellite phones.

Identification of thresholds for accuracy comparisons of heart rate and respiratory rate in neonates

Background: Heart rate (HR) and respiratory rate (RR) can be challenging to measure accurately and reliably in neonates. The introduction of innovative, non-invasive measurement technologies suitable for resource-constrained settings is limited by the lack of appropriate clinical thresholds for accuracy comparison studies. Methods: We collected measurements of photoplethysmography-recorded HR and capnography-recorded exhaled carbon dioxide across multiple 60-second epochs (observations) in enrolled neonates admitted to the neonatal care unit at Aga Khan University Hospital in Nairobi, Kenya. Trained study nurses manually recorded HR, and the study team manually counted individual breaths from capnograms. For comparison, HR and RR also were measured using an automated signal detection algorithm. Clinical measurements were analyzed for repeatability. Results: A total of 297 epochs across 35 neonates were recorded. Manual HR showed a bias of -2.4 (-1.8%) and a spread between the 95% limits of agreement (LOA) of 40.3 (29.6%) compared to the algorithm-derived median HR. Manual RR showed a bias of -3.2 (-6.6%) and a spread between the 95% LOA of 17.9 (37.3%) compared to the algorithm-derived median RR, and a bias of -0.5 (1.1%) and a spread between the 95% LOA of 4.4 (9.1%) compared to the algorithm-derived RR count. Manual HR and RR showed repeatability of 0.6 (interquartile range (IQR) 0.5-0.7), and 0.7 (IQR 0.5-0.8), respectively. Conclusions: Appropriate clinical thresholds should be selected a priori when performing accuracy comparisons for HR and RR. Automated measurement technologies typically use a smoothing or averaging filter, which significantly impacts accuracy. A wider spread between the LOA, as much as 30%, should be considered to account for the observed physiological nuances and within- and between-neonate variability and different averaging methods. Wider adoption of thresholds by data standards organizations and technology developers and manufacturers will increase the robustness of clinical comparison studies.

Heart rate variability as a strain indicator for psychological stress for emergency physicians during work and alert intervention: a systematic review

Background

The workloads of emergency physicians are severe. The prevalence of burnout among emergency physicians is higher than with other physicians or compared to the general population. The analysis of heart rate variability (HRV) is a valid method for objective monitoring of workload. The aim of this paper is to systematically evaluate the literature on heart rate variability as an objective indicator for mental stress of emergency physicians.

Methods

A systematic literature review examining heart rate variability of emergency physicians in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement for reporting systematic reviews was performed. PubMed, Ovid, Cochrane Libary, Scopus, and Web of Science electronic databases were used. The methodological quality was evaluated by using a modified STARD for HRV.

Results

Two studies matched the inclusion criteria by using HRV between alert intervention and two other studies were considered that used HRV in other question areas. It showed an adaptation of HRV under stress. The studies were not comparable.

Conclusions

There is a need for occupational health studies that examine strains and stress of emergency physicians. The well-established parasympathetic mediated HRV parameters seem to be suitable parameters to objectify the stress.

Feasibility of Using a Single Heart Rate-Based Measure for Real-time Feedback in a Voluntary Deep Breathing App for Children: Data Collection and Algorithm Development

Background

Deep diaphragmatic breathing, also called belly breathing, is a popular behavioral intervention that helps children cope with anxiety, stress, and their experience of pain. Combining physiological monitoring with accessible mobile technology can motivate children to comply with this intervention through biofeedback and gaming. These innovative technologies have the potential to improve patient experience and compliance with strategies that reduce anxiety, change the experience of pain, and enhance self-regulation during distressing medical procedures.

Objective

The aim of this paper was to describe a simple biofeedback method for quantifying breathing compliance in a mobile smartphone app.

Methods

A smartphone app was developed that combined pulse oximetry with an animated protocol for paced deep breathing. We collected photoplethysmogram data during spontaneous and subsequently paced deep breathing in children. Two measures, synchronized respiratory sinus arrhythmia (RSA_sync) and the corresponding relative synchronized inspiration/expiration heart rate ratio (HR-I:E_sync), were extracted from the photoplethysmogram.

Results

Data collected from 80 children aged 5-17 years showed a positive RSA_sync effect in all participants during paced deep breathing, with a median (IQR; range) HR-I:E_sync ratio of 1.26 (1.16-1.35; 1.01-1.60) during paced deep breathing compared to 0.98 (0.96-1.02; 0.82-1.18) during spontaneous breathing (median difference 0.25, 95% CI 0.23-0.30; P<.001). The measured HR-I:E_sync values appeared to be independent of age.

Conclusions

An HR-I:E_sync level of 1.1 was identified as an age-independent threshold for programming the breathing pattern for optimal compliance in biofeedback.

Herzfrequenzvariabilitätsanalyse in der betriebsärztlichen Praxis

In dem vorliegenden Beitrag werden basierend auf einer selektiven Literaturrecherche und unter Berücksichtigung aktueller Leitlinien die Rahmenbedingungen der Herzfrequenzvariabilitätsanalyse (HRV-Analyse), die Auswahl der HRV-Parameter bei den verschiedenen Fragestellungen und die Möglichkeiten der Nutzbarkeit der HRV für die betriebsärztliche Praxis zusammengefasst. Inzwischen in der praktischen Anwendung gut etabliert, werden die HRV-Parameter als Beanspruchungsindikatoren für die Einschätzung der Regulationsmechanismen und funktionalen Reserven des Herz-Kreislauf-Systems eingesetzt.

Airflow from nasal pulse oximetry in the screening of obstructive sleep apnea

Obstructive Sleep Apnea (OSA) is recognized as an increasing health risk, leading to daytime sleepiness and various medical conditions, such as hypertension and heart failure. Polysomnography (PSG), the gold standard to diagnose OSA, is a resource-intensive and expensive investigation confined to the hospital.Portable home monitoring, i.e. pulse oximetry, may become an acceptable OSA screening method. The novel nasal pulse oximeter sensor (Xhale Alar) adds the possibility of combining pulse oximetry (SpO2) with airflow analysis by an integrated thermistor, which might increase the diagnostic accuracy.In the Alar pilot study, 39 adults were measured during an overnight PSG recording together with the Alar sensor. This study aims to investigate the additional value of an airflow signal compared to SpO2 analysis in OSA screening. Both time and spectral features were extracted from SpO2 and airflow signals recorded with the Alar sensor. Leave one out cross-validation was used to develop Random Forest models in screening for apnea-hypopnea index (AHI) thresholds 5 and 10. Using both AHI ≥ 5 and AHI ≥ 10 as the diagnostic cutoff, the airflow signal shows respectively an AUC of 89% and 80% compared to 78% and 77% with SpO2 analysis, showing a higher performance using an airflow signal in screening adults for OSA.

Pulse Rate Variability in Emergency Physicians During Shifts: Pilot Cross-Sectional Study

Background

The high prevalence of physician burnout, particularly in emergency medicine, has garnered national attention in recent years. Objective means of measuring stress while at work can facilitate research into stress reduction interventions, and wearable photoplethysmography (PPG) technology has been proposed as a potential solution. However, the use of low-burden wearable biosensors to study training and clinical practice among emergency physicians (EP) remains untested.

Objective

This pilot study aimed to (1) determine the feasibility of recording on-shift photoplethysmographic data from EP, (2) assess the quality of these data, and (3) calculate standard pulse rate variability (PRV) metrics from the acquired dataset and examine patterns in these variables over the course of an academic year.

Methods

A total of 21 EP wore PPG biosensors on their wrists during clinical work in the emergency department during a 9-hour shift. Recordings were collected during the first quarter of the academic year, then again during the fourth quarter of the same year for comparison. The overall rate of usable data collection per time was computed. Standard pulse rate (PR) and PRV metrics from these two time points were calculated and entered into Student t tests.

Results

More than 400 hours of data were entered into these analyses. Interpretable data were captured during 8.54% of the total recording time overall. In the fourth quarter of the academic year compared with the first quarter, there was no significant difference in median PR (75.8 vs 76.8; P=.57), mean R-R interval (0.81 vs 0.80; P=.32), SD of R-R interval (0.11 vs 0.11; P=.93), root mean square of successive difference of R-R interval (0.81 vs 0.80; P=.96), low-frequency power (3.5×103 vs 3.4×103; P=.79), high-frequency power (8.5×103 vs 8.3×103; P=.91), or low-frequency to high-frequency ratio (0.42 vs 0.41; P=.43), respectively. Power estimates for each of these tests exceeded .90. A secondary analysis of the resident-only subgroup similarly showed no significant differences over time, despite power estimates greater than .80.

Conclusions

Although the use of PPG biosensors to record real-time physiological data from EP while providing clinical care seems operationally feasible, this study fails to support the notion that such an approach can efficiently provide reliable estimates of metrics of interest. No significant differences in PR or PRV metrics were found at the end of the year compared with the beginning. Although these methods may offer useful applications to other domains, it may currently have limited utility in the contexts of physician training and wellness.

Night to night pulse oximetry variability in children with suspected sleep apnea

Obstructive Sleep Apnea (OSA) is the most common form of sleep-disordered breathing in children. The gold standard to screen for OSA, polysomnography (PSG), requires an overnight stay in the hospital and is resource intensive. The Phone Oximeter is a non-invasive smartphone-based tool to record pulse oximetry. This portable device is able to measure patients over multiple nights while at home, causing less sleep disturbance than PSG and is able to measure night to night variability in sleep. This study analyzed the Screen My Sleep children (SMS) dataset, in which 74 children were monitored over multiple nights with the Phone Oximeter, including one night simultaneously with PSG in the hospital and two nights at home. In this study, we aim to investigate the night to night variability and assess the accuracy of the oxygen desaturation index (ODI) screening for children with significant OSA. In order to assess the performance of the ODI calculation in children, we implemented different ODIs at different desaturation levels and time durations. The variability was studied using a one-way ANOVA, and ODI's performance screening for OSA using the area under the ROC curve (AUC). The implemented ODIs provide similar OSA screening results, using different apnea/hypopnea index (AHI) thresholds, as the ODI recommended for adults by the American academy of sleep medicine (AASM). The ODI provides an AUC of around 0.77, 0.76, 0.94 and 0.97 classifying children with an AHI > 1, AHI > 5 AHI > 10 and AHI > 15, respectively. The SMS dataset shows no significant night to night variability between the two nights at home. However, when comparing with the night at the hospital, both nights at home show a decrease in the lowest SpO₂ value as well as overall SpO₂ signal quality percentage. This study shows that there is variability in SpO₂ signal between at-home versus in hospital 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.

Assessing Pain Intensity Using Photoplethysmography Signals in Chronic Myofascial Pain Syndrome

BACKGROUND

Efficacy of pain assessment is the basis for effective therapy. Clinically, assessing pain is by subjective scale, but these methods have some shortcomings. Therefore, studies have been conducted on assessment of pain using physiological signals. Photoplethysmography (PPG) signals provide much information about the cardiovascular system. PPG-derived parameters (PPG parameters) reflect nociceptive stimulation, and obtain an approximation of the R-R interval from the PPG period. The aim of this study was to evaluate PPG signals for assessment of pain intensity in chronic myofascial pain syndrome (MPS) patients.

METHODS

This study recruited 37 patients with chronic MPS; all of them were treated with electrotherapy and thermotherapy. The difference between pre- and post-therapy PPG parameters, and the correlation between pulse rate variability (PRV) and heart rate variability (HRV) were determined. We also obtained patients' pain intensity scores by visual analog scale, visual rating scale, and Wong-Banker face pain rating scale.

RESULTS

Photoplethysmography and PRV/HRV parameters showed significant differences between pre- and post-treatment. The variation trend of PRV was similar with HRV in heart rate, R-R interval, low frequency, high frequency, and LF/HF; in addition, a high correlation between the parameters was observed either in pre- or post-therapy. PPG parameters indicated increased sympathetic tone.

CONCLUSION

The results of the study indicated that PRV substituted for HRV in assessment of pain intensity in chronic MPS reflected parasympathetic nervous tone increase, and PPG parameters might reflect stress stimulation on skin.

Comparison of pulse rate variability and heart rate variability for high frequency content estimation

Heart Rate Variability (HRV) analysis can be of precious help in most of clinical situations because it is able to quantify the Autonomic Nervous System (ANS) activity. The HRV high frequency (HF) content, related to the parasympathetic tone, reflects the ANS response to an external stimulus responsible of pain, stress or various emotions. We have previously developed the Analgesia Nociception Index (ANI), based on HRV high frequency content estimation, which quantifies continuously the vagal tone in order to guide analgesic drug administration during general anesthesia. This technology has been largely validated during the peri-operative period. Currently, ANI is obtained from a specific algorithm analyzing a time series representing successive heart periods measured on the electrocardiographic (ECG) signal. In the perspective of widening the application fields of this technology, in particular for homecare monitoring, it has become necessary to simplify signal acquisition by using e.g. a pulse plethysmographic (PPG) sensor. Even if Pulse Rate Variability (PRV) analysis issued from PPG sensors has been shown to be unreliable and a bad predictor of HRV analysis results, we have compared PRV and HRV both estimated by ANI as well as HF and HF/(HF+LF) spectral analysis on both signals.

Pulse oximetry recorded from the Phone Oximeter for detection of obstructive sleep apnea events with and without oxygen desaturation in children

Obstructive sleep apnea (OSA) disrupts normal ventilation during sleep and can lead to serious health problems in children if left untreated. Polysomnography, the gold standard for OSA diagnosis, is resource intensive and requires a specialized laboratory. Thus, we proposed to use the Phone Oximeter™, a portable device integrating pulse oximetry with a smartphone, to detect OSA events. As a proportion of OSA events occur without oxygen desaturation (defined as SpO2 decreases ≥ 3%), we suggest combining SpO2 and pulse rate variability (PRV) analysis to identify all OSA events and provide a more detailed sleep analysis. We recruited 160 children and recorded pulse oximetry consisting of SpO2 and plethysmography (PPG) using the Phone Oximeter™, alongside standard polysomnography. A sleep technician visually scored all OSA events with and without oxygen desaturation from polysomnography. We divided pulse oximetry signals into 1-min signal segments and extracted several features from SpO2 and PPG analysis in the time and frequency domain. Segments with OSA, especially the ones with oxygen desaturation, presented greater SpO2 variability and modulation reflected in the spectral domain than segments without OSA. Segments with OSA also showed higher heart rate and sympathetic activity through the PRV analysis relative to segments without OSA. PRV analysis was more sensitive than SpO2 analysis for identification of OSA events without oxygen desaturation. Combining SpO2 and PRV analysis enhanced OSA event detection through a multiple logistic regression model. The area under the ROC curve increased from 81% to 87%. Thus, the Phone Oximeter™ might be useful to monitor sleep and identify OSA events with and without oxygen desaturation at home.

Respiratory rate and pulse oximetry derived information as predictors of hospital admission in young children in Bangladesh: a prospective observational study

OBJECTIVE

Hypoxaemia is a strong predictor of mortality in children. Early detection of deteriorating condition is vital to timely intervention. We hypothesise that measures of pulse oximetry dynamics may identify children requiring hospitalisation. Our aim was to develop a predictive tool using only objective data derived from pulse oximetry and observed respiratory rate to identify children at increased risk of hospital admission.

SETTING

Tertiary-level hospital emergency department in Bangladesh.

PARTICIPANTS

Children under 5 years (n=3374) presenting at the facility (October 2012-April 2013) without documented chronic diseases were recruited. 1-minute segments of pulse oximetry (photoplethysmogram (PPG), blood oxygen saturation (SpO2) and heart rate (HR)) and respiratory rate were collected with a mobile app.

PRIMARY OUTCOME

The need for hospitalisation based on expert physician review and follow-up.

METHODS

Pulse rate variability (PRV) using pulse peak intervals of the PPG signal and features extracted from the SpO2 signal, all derived from pulse oximetry recordings, were studied. A univariate age-adjusted logistic regression was applied to evaluate differences between admitted and non-admitted children. A multivariate logistic regression model was developed using a stepwise selection of predictors and was internally validated using bootstrapping.

RESULTS

Children admitted to hospital showed significantly (p<0.01) decreased PRV and higher SpO2 variability compared to non-admitted children. The strongest predictors of hospitalisation were reduced PRV-power in the low frequency band (OR associated with a 0.01 unit increase, 0.93; 95% CI 0.89 to 0.98), greater time spent below an SpO2 of 98% and 94% (OR associated with 10 s increase, 1.4; 95% CI 1.3 to 1.4 and 1.5; 95% CI 1.4 to 1.6, respectively), high respiratory rate, high HR, low SpO2, young age and male sex. These variables provided a bootstrap-corrected AUC of the receiver operating characteristic of 0.76.

CONCLUSIONS

Objective measurements, easily obtained using a mobile device in low-resource settings, can predict the need for hospitalisation. External validation will be required before clinical adoption.

Comparison of Pulse Rate Variability and Heart Rate Variability for Hypoglycemia Syndrome

BACKGROUND

Heart rate variability (HRV) is a signal obtained from RR intervals of electrocardiography (ECG) signals to evaluate the balance between the sympathetic nervous system and the parasympathetic nervous system; not only HRV but also pulse rate variability (PRV) extracted from finger pulse plethysmography (PPG) can reflect irregularities that may occur in heart rate and control procedures.

OBJECTIVES

The purpose of this study is to compare the HRV and PRV during hypoglycemia in order to evaluate the features that computed from PRV that can be used in detection of hypoglycemia.

METHODS

To this end, PRV and HRV of 10 patients who required testing with insulin-induced hypoglycemia (IIHT) in Clinics of Endocrinology and Metabolism Diseases of Bezm-i Alem University (Istanbul, Turkey), were obtained. The recordings were done at three stages: prior to IIHT, during the IIHT, and after the IIHT. We used Bland-Altman analysis for comparing the parameters and to evaluate the correlation between HRV and PRV if exists.

RESULTS

Significant correlation (r > 0.90, p < 0.05) and close agreement were found between HRV and PRV for mean intervals, the root-mean square of the difference of successive intervals, standard deviation of successive intervals and the ratio of the low-to-high frequency power.

CONCLUSIONS

In conclusion, all the features computed from PRV and HRV have close agreement and correlation according to Bland-Altman analyses' results and features computed from PRV can be used in detection of hypoglycemia.

Investigation of heart rate variability in major depression patients using wavelet packet transform

Studies conducted in major depression (MD) patients have reported a high risk of cardiac morbidity as a result of the relationship between changed cardiovascular activity (CA) and autonomic dysfunctions. The investigation of heart rate variability (HRV) gives valuable idea about variances in autonomic CA of MD patients. To get this knowledge, frequency-domain HRV analysis is frequently performed using Fourier transformation (FT) or discrete-wavelet transformation (DWT) to decompose the data into high-frequency (HF) and low-frequency (LF) bands. Nevertheless, it has been reported that the FT is not useful for nonstationary HRV signals and the DWT does not ensure required frequency boundaries of each band. This study aims to compare the frequency-domain HRV features using wavelet-packet-transform (WPT) with absolutely excellent approximation to required band ranges between the controls and patients. In addition to LF and HF band energies, sympathovagal balance that indicates the variation of sympathetic and parasympathetic activities were compared between two groups. Patients had a significantly lower HF energy, higher values of LF energy and higher LF/HF ratio. Our results recommend that impairments in coordination between parasympathetic and sympathetic behavior in MD patients can be assessed by HRV analysis using WPT with high resolution decomposition for needed bands.

Oxygen saturation resolution influences regularity measurements

The measurement of regularity in the oxygen saturation (SpO(2)) signal has been suggested for use in identifying subjects with sleep disordered breathing (SDB). Previous work has shown that children with SDB have lower SpO(2) regularity than subjects without SDB (NonSDB). Regularity was measured using non-linear methods like approximate entropy (ApEn), sample entropy (SamEn) and Lempel-Ziv (LZ) complexity. Different manufacturer's pulse oximeters provide SpO(2) at various resolutions and the effect of this resolution difference on SpO(2) regularity, has not been studied. To investigate this effect, we used the SpO(2) signal of children with and without SDB, recorded from the Phone Oximeter (0.1% resolution) and the same SpO(2) signal rounded to the nearest integer (artificial 1% resolution). To further validate the effect of rounding, we also used the SpO(2) signal (1% resolution) recorded simultaneously from polysomnography (PSG), as a control signal. We estimated SpO(2) regularity by computing the ApEn, SamEn and LZ complexity, using a 5-min sliding window and showed that different resolutions provided significantly different results. The regularity calculated using 0.1% SpO(2) resolution provided no significant differences between SDB and NonSDB. However, the artificial 1% resolution SpO(2) provided significant differences between SDB and NonSDB, showing a more random SpO(2) pattern (lower SpO(2) regularity) in SDB children, as suggested in the past. Similar results were obtained with the SpO(2) recorded from PSG (1% resolution), which further validated that this SpO(2) regularity change was due to the rounding effect. Therefore, the SpO(2) resolution has a great influence in regularity measurements like ApEn, SamEn and LZ complexity that should be considered when studying the SpO(2) pattern in children with SDB.

Photoplethysmography variability as an alternative approach to obtain heart rate variability information in chronic pain patient

Heart rate variability (HRV) is a well-known method for the assessment of autonomic nervous function of the heart. Previous study suggested that pulse rate variability (PRV) determined by photoplethysmography could be used instead of HRV to more simply assess autonomic nervous function. However, most research studies included healthy subjects. Thus, the aim of this study was to investigate the feasibility for PRV as a surrogate index for patients with chronic pain. This study investigated the correlation coefficient (by Pearson correlation) and agreement (by Bland-Altman analysis) between PRV and HRV in chronic pain patients in the clinical setting. The results showed high significant correlations (p < 0.001, r > 0.86) between all the HRV and PRV parameters and good agreements (ratio < 0.1) between the parameters in terms of HR, mean RR, VLF, LF, nLF, nHF, and SD1/SD2. Our study suggests that HRV can also be reliably estimated using the photoplethysmography-based PP interval in elderly patients with chronic pain.

Development of a screening tool for sleep disordered breathing in children using the phone Oximeter™

Background

Sleep disordered breathing (SDB) can lead to daytime sleepiness, growth failure and developmental delay in children. Polysomnography (PSG), the gold standard to diagnose SDB, is a highly resource-intensive test, confined to the sleep laboratory.

Aim

To combine the blood oxygen saturation (SpO₂) characterization and cardiac modulation, quantified by pulse rate variability (PRV), to identify children with SDB using the Phone Oximeter, a device integrating a pulse oximeter with a smartphone.

Methods

Following ethics approval and informed consent, 160 children referred to British Columbia Children's Hospital for overnight PSG were recruited. A second pulse oximeter sensor applied to the finger adjacent to the one used for standard PSG was attached to the Phone Oximeter to record overnight pulse oximetry (SpO₂ and photoplethysmogram (PPG)) alongside the PSG.

Results

We studied 146 children through the analysis of the SpO₂ pattern, and PRV as an estimate of heart rate variability calculated from the PPG. SpO₂ variability and SpO₂ spectral power at low frequency, was significantly higher in children with SDB due to the modulation provoked by airway obstruction during sleep (p-value ). PRV analysis reflected a significant augmentation of sympathetic activity provoked by intermittent hypoxia in SDB children. A linear classifier was trained with the most discriminating features to identify children with SDB. The classifier was validated with internal and external cross-validation, providing a high negative predictive value (92.6%) and a good balance between sensitivity (88.4%) and specificity (83.6%). Combining SpO₂ and PRV analysis improved the classification performance, providing an area under the receiver operating characteristic curve of 88%, beyond the 82% achieved using SpO₂ analysis alone.

Conclusions

These results demonstrate that the implementation of this algorithm in the Phone Oximeter will provide an improved portable, at-home screening tool, with the capability of monitoring patients over multiple nights.