Pulse Oximetry Signals Research Articles

Phenotypic Characterisation of Obstructive Sleep Apnoea in Acute Coronary Syndrome

Recent neutral randomised clinical trials have created clinical equipoise for treating obstructive sleep apnoea (OSA) for managing cardiovascular risk. The importance of defining the links between OSA and cardiovascular disease is needed with the aim of advancing the robustness of future clinical trials. We aimed to define the clinical correlates and characterise surrogate cardiovascular markers in patients with acute coronary syndrome (ACS) and OSA. Overall, 66 patients diagnosed with ACS were studied. Patients underwent an unattended polysomnogram after hospital discharge (median [interquartile range] 62 [37-132] days). The Epworth Sleepiness Scale, Berlin, and STOP-BANG questionnaires were administered. Surrogate measures of vascular structure and function, and cardiovascular autonomic function were conducted. Pulse wave amplitude drop was derived from the pulse oximetry signals of the overnight polysomnogram. OSA (apnoea-hypopnea index [AHI] ≥5) was diagnosed in 94% of patients. Moderate-to-severe OSA (AHI≥15) was observed in 68% of patients. Daytime sleepiness (Epworth Sleepiness Scale ≥10) was reported in 17% of patients. OSA screening questionnaires were inadequate to identify moderate-to-severe OSA, with an area under the receiver operating characteristic curve of approximately 0.64. Arterial stiffness (carotid-femoral pulse wave velocity, 6.1 [5.2-6.8] vs 7.4 [6.6-8.6] m/s, p=0.002) and carotid intima-media thickness (0.8 [0.7-1.0] vs 0.9 [0.8-1.0] mm, p=0.027) was elevated in patients with moderate-to-severe OSA. After adjusting for age, sex and body mass index, these relationships were not statistically significant. No relationships were observed in other surrogate cardiovascular markers. A high prevalence of OSA in a mostly non-sleepy population with ACS was identified, highlighting a gross underdiagnosis of OSA among cardiovascular patients. The limitations of OSA screening questionnaires highlight the need for new models of OSA screening as part of cardiovascular risk management. A range of inconsistent abnormalities were observed in measures of vascular structure and function, and these appear to be largely explained by confounding factors. Further research is required to elucidate biomarkers for the presence and impact of OSA in ACS patients.

A Wearable Monitoring Device for COVID-19 Biometric Symptoms Detection

BackgroundMonitoring COVID-19 symptoms has become a critical task in controlling the spread of the virus and preventing hospitalizations. Aiming to contribute to efficient monitoring solutions, this article presents the development and testing of a wearable device capable of continuous monitoring biometric signals associated with the presence of COVID-19, such as the heart rate, the blood oxygen saturation, and the body temperature. MethodsTo ensure continuous monitoring the device is designed to be worn in the ear. Here, the temperature is measured through a non-contact infrared temperature sensor placed inside the ear canal while the heart rate and the pulse oximetry signals are monitored through a photoplethysmography reflective sensor positioned at the earlobe.The proposed device's performance was evaluated by comparing it against a medical certified station. Usability and ergonomics were assessed through users' questionnaires. Additionally, experiments were performed to evaluate the hearing loss when the proposed device is in use. Data was acquired from 30 individuals of different sex, aged between 20 and 43 years old. In relation to usability and ergonomics the variation in ear dimensions was accessed and related to the device's comfort limitations. ResultsThe temperature measurement produced a moderate correlation (R=0.42), despite a higher standard deviation was found in the proposed solution. This is due to the limited variability in temperature data, creating a short measuring range, as only healthy people were tested. The heart rate measurement also showed good correlation (R=0.96), with the proposed solution showing good repeatability with a standard deviation of 6.06 BPM, however, the SpO2 measurement was suboptimal (R=0.14).The ergonomic evaluation revealed that most participants found the device shape comfortable, but some found the dimensions not adequate.Additionally, the device was found to be user-friendly, with most participants reporting that they found it to be intuitive, and none reported a major loss in hearing in a normal conversation, however, there's a negligible loss of approximately 0.56 dB. ConclusionsDuring this study, it was possible to develop and evaluate a wearable device that was suggested for monitoring biometric signals. The device demonstrated great reliability in temperature and heart rate measurement but showed limitations in the accuracy of pulse oximetry. The main contribution of this work is the evaluation of a continuous non-invasive monitoring concept for COVID-19 related biometric signals, which indicates good applicability in the case study.

A comparison of hypoxic burden algorithms using three different methods for calculating baseline oxygen saturation for predicting cardiovascular death in the Sleep Heart Health Study.

Respiratory event related oxygen desaturation area measures have recently shown merit as novel predictors of cardiovascular disease (CVD) outcomes. In this study, we investigate one such measure (hypoxic burden (HB)) and investigate how three different ways of calculating the SpO2 baseline of the HB algorithm impact its ability to predict cardiovascular mortality. The three baseline estimation steps include a pre-event baseline, a record-based baseline, and a fixed baseline. Pulse oximetry signals from the Sleep Heart Health Study and the corresponding CVD outcomes were analyzed. The performance of each baseline method was compared using adjusted Cox proportional hazard regression analysis. Results show that HB with the record-based baseline method returned the best performing results with a hazard ratio (HR) of 1.83 (95% CI: 1.03-3.27, p<0.05) in the fully adjusted model, compared to HB with the pre-event baseline method (HR: 1.60, 95%CI: 0.86-3.00, p>0.05) and HB with the fixed baseline method (HR: 1.73, 95%CI: 0.93-3.22, p>0.05).

Correlation between Perfusion Index and Lactate Level in Critically Ill Patients

Background: During shock resuscitation, early detection of tissue hypoperfusion would guide physician to actively manage for the better outcome. Serum lactate is commonly used but it needs blood sampling and obtains intermittent data. Perfusion index (PI) analyzed from pulse-oximetry signal is a non-invasive and continuous monitoring for peripheral tissue perfusion. Previous studies have shown correlations between PI and microcirculation parameters in critically ill patients; however, limited data is available. Objective: To investigate the correlation between PI and lactate level in critically ill patients. Materials and Methods: The present study was a prospective observational study at the surgical intensive care unit of King Chulalongkorn Memorial Hospital (KCMH). All critically ill patients having tissue hypoperfusion and serum lactate of 2 mmol/L or greater were enrolled. PI was measured by MasimoRadical-7®PulseCO-Oximeter® and recorded values simultaneously with serum lactate at 0, 2, 6, and 24-hours during resuscitation. Results: Of the 42 patients, the authors found a significant correlation between PI and lactate level at 0 and 2-hours (r=–0.397, p=0.009 and r=–0.311, p=0.045, respectively). The change in PI also significantly correlated with lactate clearance at first 6-hours of resuscitation (r=0.444, p=0.003 at 0 to 2-hour and r=0.370, p=0.017 at 2 to 6-hours). Twenty-four patients or 57% had lactate clearance of 10% or more within 2-hours, whereas 18 patients (42.8%) did not. The cut-off value of increasing in PI of less than 0.86 predicted patients who were not lactate clearance at 2-hours, (sensitivity 88.9%, specificity 54.2%, AUC 0.699, 95% CI 0.54 to 0.86, p=0.029). Conclusion: PI may have value to adjunct continuous monitoring for peripheral perfusion during early resuscitation by using concurrently with serum lactate. Increase in PI of less than 0.86 within 2-hours should prompt the physician to manage it further. Keywords: Lactate; Perfusion index; PI; Peripheral tissue perfusion; Microcirculatory monitoring

Peripheral Perfusion Index in Assessment of Shock in Paediatric Intensive Care Unit of A Tertiary Care Hospital

Background: Measures of peripheral perfusion can be used to assess the hemodynamic status of critically ill patients. The peripheral Perfusion Index (PI) based on analysis of the pulse oximetry signal has been implemented in monitoring systems as an index of peripheral perfusion. The aim of this study was to evaluate clinical state of shock by PI in a Pediatric Intensive Care Unit (PICU) of a tertiary care hospital of Bangladesh.&#x0D; Materials and methods: This prospective observational study was carried out in the PICU of Chittagong Medical College Hospital. Children aged 1 month to 12 years who needed hemodynamic monitoring were included and categorized into five age groups. Demographic data, vital parameters and PI were recorded. Hemodynamic monitoring was started as early as possible within 24 hours of arrival in PICU, then 30 minutes after, then 8 hourly for a total 4 observations.&#x0D; Results: In total, 199 children were included with or without features of shock and 796 hemodynamic measurements were taken and analyzed. Mean/median PIs were significantly higher in patients without shock compared to patients with shock in all age groups except age group 10-12 years of age. Clinical shock can be reasonably detected when PI value was &lt; 1.25 in children &lt;1 year of age, &lt; 2.05 in 1 to 3 years of age, &lt;2.55 in 3 to 5 years, and &lt;1.95 in 5-10 years of age. These values had low sensitivity but high specificity in detecting clinically assessed shock in that particular age group. Overall, PI had good correlation with systolic, diastolic, mean arterial blood pressure and pulse pressure. Children with different features of shock had significantly lower mean PI compared to children without features of shock.&#x0D; Conclusion: PI can be used as a non-invasive, continuous parameter to monitor peripheral perfusion in critically ill children managed in PICU.&#x0D; IAHS Medical Journal Vol 4(2), December 2021; 46-50

Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest

Sleep apnea (SA) is a common sleep disorder which could impair the human physiological system. Therefore, early diagnosis of SA is of great interest. The traditional method of diagnosing SA is an overnight polysomnography (PSG) evaluation. When PSG has limited availability, automatic SA screening with a fewer number of signals should be considered. The primary purpose of this study is to develop and evaluate a SA detection model based on electrocardiogram (ECG) and blood oxygen saturation (SpO2). We adopted a multimodal approach to fuse ECG and SpO2 signals at the feature level. Then, feature selection was conducted using the recursive feature elimination with cross-validation (RFECV) algorithm and random forest (RF) classifier used to discriminate between apnea and normal events. Experiments were conducted on the Apnea-ECG database. The introduced algorithm obtained an accuracy of 97.5%, a sensitivity of 95.9%, a specificity of 98.4% and an AUC of 0.992 in per-segment classification, and outperformed previous works. The results showed that ECG and SpO2 are complementary in detecting SA, and that the combination of ECG and SpO2 enhances the ability to diagnose SA. Therefore, the proposed method has the potential to be an alternative to conventional detection methods.

Automated detection of obstructive sleep apnea in more than 8000 subjects using frequency optimized orthogonal wavelet filter bank with respiratory and oximetry signals

Obstructive sleep apnea (OSA) is a common respiratory disorder marked by interruption of the respiratory tract and difficulty in breathing. The risk of serious health damage can be reduced if OSA is diagnosed and treated at an early stage. OSA is primarily diagnosed using polysomnography (PSG) monitoring performed for overnight sleep; furthermore, capturing PSG signals during the night is expensive, time-consuming, complex and highly inconvenient to patients. Hence, we are proposing to detect OSA automatically using respiratory and oximetry signals. The aim of this study is to develop a simple and computationally efficient wavelet-based automated system based on these signals to detect OSA in elderly subjects.In this study, we proposed an accurate, reliable, and less complex OSA automated detection system by using pulse oximetry (SpO2) and respiratory signals including thoracic (ThorRes) movement, abdominal (AbdoRes) movement, and airflow (AF). These signals are collected from the Sleep Heart Health Study (SHHS) database from the National Sleep Research Resource (NSRR), which is one of the largest repositories of publicly available sleep databases. The database comprises of two groups SHHS-1 and SHHS-2, which involves 5,793 and 2,651 subjects, respectively with an average age of ≥60 years. The 30-s epochs of the signals are decomposed into sub-bands using frequency optimized orthogonal wavelet filter bank. Tsallis entropies are extracted from the sub-band coefficients of wavelet filter bank. A total 4,415,229 epochs of respiratory and oximetry signals are used to develop the model. The proposed model is developed using GentleBoost and Random under-sampling Boosting (RUSBoosted Tree) algorithms with 10-fold cross-validation technique.Our developed model has obtained the highest classification accuracy of 89.39% and 84.64% for the imbalanced and balanced datasets, respectively using 10-fold cross-validation technique. Using the 20% hold-out validation, the model yielded an accuracy of 88.26% and 84.31% for the imbalanced and balanced datasets, respectively. Hence, the respiratory and SpO2 signals-based model can be used for automated OSA detection. The results obtained from the proposed model are better than the state-of-the-art models and can be used in-home for screening the OSA.

Temporal convolutional networks and transformers for classifying the sleep stage in awake or asleep using pulse oximetry signals

Sleep disorders are very widespread in the world population and suffer from a generalized underdiagnosis, given the complexity of their diagnostic methods. Therefore, there is an increasing interest in developing simpler screening methods. Pulse oximeter is an ideal device for sleep disorder screenings, since it is a portable, low-cost and accessible technology. This device can provide an estimation of the heart rate (HR), which can be useful to obtain information regarding the sleep stage. In this work, we developed a network architecture in order to classify the sleep stage in awake or asleep using only HR signals from a pulse oximeter. The proposed architecture has two fundamental parts. The first part has the aim of obtaining a representation of the HR by using temporal convolutional networks. Then, the obtained representation is used to feed the second part, which is based on transformers, a model built solely with attention mechanisms. Transformers are able to model the sequence, learning the transition rules between sleep stages. The performance of the proposed method was evaluated on the Sleep Heart Health Study dataset, composed of 5000 healthy and pathological subjects. The dataset was split into three subsets: 2500 for training, 1250 for validating and 1250 for testing. The overall accuracy, specificity, sensitivity and Cohen’s Kappa coefficient were 90.0%, 94.9%, 78.1%, and 0.73.

Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signals

Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signals

A novel algorithm for automatic diagnosis of sleep apnea from airflow and oximetry signals

Objective. Sleep apnea significantly decreases the quality of life. The apnea hypopnea index (AHI) is the main indicator for sleep apnea diagnosis. This study explored a novel automatic algorithm to diagnose sleep apnea from nasal airflow (AF) and pulse oximetry (SpO2) signals. Approach. Of the 988 polysomnography (PSG) records from the sleep heart health study (SHHS), 45 were randomly selected for the development of an algorithm and the remainder for validation (n = 943). The algorithm detects apnea events by a digitization process, following the determination of the peak excursion (peak-to-trough amplitude) from AF envelope. Hypopnea events were determined from the AF envelope and oxygen desaturation with correction to time lag in SpO2. Total sleep time (TST) was estimated from an optimized percentage of artefact-free total recording time. AHI was estimated from the number of detected events divided by the estimated TST. The estimated AHI was compared to the scored SHHS data for performance evaluation. Main results. The validation showed good agreement between the estimated and scored AHI (intraclass correlation coefficient of 0.95 and mean ±95% limits of agreement of −1.6 ±12.5 events h−1). The diagnostic accuracies were found: 90.7%, 91%, and 96.7% for AHI cut-off ≥5, ≥15, and ≥30 respectively. Significance. The new algorithm is accurate over other existing methods for the automatic diagnosis of sleep apnea. It is applicable to any portable sleep screeners especially for the home diagnosis of sleep apnea.

Classifying sleep–wake stages through recurrent neural networks using pulse oximetry signals

The regulation of the autonomic nervous system changes with the sleep stages causing variations in the physiological variables. We exploit these changes with the aim of classifying the sleep stages in awake or asleep using pulse oximeter signals. We applied a recurrent neural network to heart rate and peripheral oxygen saturation signals to classify the sleep stage every 30 seconds. The network architecture consists of two stacked layers of bidirectional gated recurrent units (GRUs) and a softmax layer to classify the output. In this paper, we used 5000 patients from the Sleep Heart Health Study dataset. 2500 patients were used to train the network, and two subsets of 1250 were used to validate and test the trained models. In the test stage, the best result obtained was 90.13% accuracy, 94.13% sensitivity, 80.26% specificity, 92.05% precision, and 84.68% negative predictive value. Further, the Cohen’s Kappa coefficient was 0.74 and the average absolute error percentage to the actual sleep time was 8.9%. The performance of the proposed network is comparable with the state-of-the-art algorithms when they use much more informative signals (except those with EEG).

Power spectral densities of nocturnal pulse oximetry signals differ in OSA patients with and without daytime sleepiness

BackgroundAs nocturnal hypoxemia and heart rate variability are associated with excessive daytime sleepiness (EDS) related to OSA, we hypothesize that the power spectral densities (PSD) of nocturnal pulse oximetry signals could be utilized in the assessment of EDS. Thus, we aimed to investigate if PSDs contain features that are related to EDS and whether a convolutional neural network (CNN) could detect patients with EDS using self-learned PSD features. MethodsA total of 915 OSA patients who had undergone polysomnography with multiple sleep latency test on the following day were investigated. PSDs for nocturnal blood oxygen saturation (SpO2), heart rate (HR), and photoplethysmogram (PPG), as well as power in the 15–35 mHz band in SpO2 (PSPO2) and HR (PHR), were computed. Differences in PSD features were investigated between EDS groups. Additionally, a CNN classifier was developed for identifying severe EDS patients based on spectral data. ResultsSpO2 power content increased significantly (p < 0.002) with increasing severity of EDS. Furthermore, a significant (p < 0.001) increase in HR-PSD was found in severe EDS (mean sleep latency < 5 min). Elevated odds of having severe EDS was found in PSPO2 (OR = 1.19–1.29) and PHR (OR = 1.81–1.83). Despite these significant spectral differences, the CNN classifier reached only moderate sensitivity (49.5%) alongside high specificity (80.4%) in identifying patients with severe EDS. ConclusionsWe conclude that PSDs of nocturnal pulse oximetry signals contain features significantly associated with OSA-related EDS. However, CNN-based identification of patients with EDS is challenging via pulse oximetry.

Is Cumulative Time of Oxygen Desaturation a Better Predictor of Cardiovascular Mortality than Apnoea Hypopnoea Index?

In this paper, we explored the link between sleep apnoea and cardiovascular disease (CVD) using a time-series statistical measure of sleep apnoea-related oxygen desaturation. We compared the performance of a hypoxic measure derived from the polysomnogram with the Apnoea Hypopnoea Index (AHI) in predicting CVD mortality in patients of the Sleep Heart Health Study.We estimated the relative cumulative time of SpO2 below 90% (Tr90) using pulse oximetry signals from polysomnogram recordings as the hypoxic measure of desaturation patterns. Then, the survival curves for hypoxia quintiles were evaluated for the prediction of CVD mortality and were compared with the results using AHI for prediction. We also calculated the Cox hazard ratios for Tr90 and AHI. Our results show that the Tr90 was a better predictor of CVD mortality outcomes than AHI.

A Per-sample Digitized Algorithm for Automatically Detecting Apnea and Hypopnea Events from Airflow and Oximetry.

Sleep apnea is a common sleep disorder that can significantly decrease the quality of life. An accurate and early diagnosis of sleep apnea is required before getting proper treatment. A reliable automated detection of sleep apnea can overcome the problems of manual diagnosis (scoring) due to variability in recording and scoring criteria (for example across Europe) and to inter-scorer variability. This study explored a novel automated algorithm to detect apnea and hypopnea events from airflow and pulse oximetry signals, extracted from 30 polysomnography records of the Sleep Heart Health Study. Apneas and hypopneas were manually scored by a trained sleep physiologist according to the updated 2017 American Academy of Sleep Medicine respiratory scoring rules. From pre-processed airflow, the peak signal excursion was precisely determined from the peak-to-trough amplitude using a sliding window, with a per-sample digitized algorithm for detecting apnea and hypopnea. For apnea, the peak signal excursion drop was operationalized at ≥85% and for hypopnea at ≥35% of its pre-event baseline. Using backward shifting of oximetry, hypopneas were filtered with ≥3% oxygen desaturation from its baseline. The performance of the automated algorithm was evaluated by comparing the detection with manual scoring (a standard practice). The sensitivity and positive predictive value of detecting apneas and hypopneas were respectively 98.1% and 95.3%. This automated algorithm is applicable to any portable sleep monitoring device for the accurate detection of sleep apnea.

Respiratory polygraphy data of children investigated for sleep-disordered breathing with different congenital or respiratory diseases.

This short report describes respiratory indices of polygraphies (PG) performed to investigate several sleep-related disorders of breathing in children. It refers to the work of Michelet et al., Successful home respiratory polygraphy to investigate sleep-disordered breathing in children, Sleep Medicine [1]. Indications for PGs were grouped according to 6 categories: craniofacial malformation, neuromuscular disease, obesity, suspected obstructive sleep apnea (OSA), prematurity, and other. The reported data concern the initial interpretable PGs (N = 289); initial was defined as performed for the first time in any subject. Non-interpretability was defined as absent or unreliable oxygen saturation by pulse oximetry (SpO2), and/or airflow and respiratory inductance plethysmography (RIP) flow trace signals during time analyzed. Analyzed time is reported. In a subset of patients, transcutaneous carbon dioxide partial pressure (ptcCO2) was also measured. Data may be re-used for comparison in future validating research for PGs in children [2].

Detection of Abnormal Respiratory Events with Single Channel ECG and Hybrid Machine Learning Model in Patients with Obstructive Sleep Apnea

Respiratory scoring is an important step in the diagnosis of Obstructive Sleep Apnea (OSA). Airflow, abdolmel-thorax and pulse oximetry signals are obtained with the help of Polysomnography (PSG) device for the respiration scoring stage. These signals are visually scored by a specialist physician. The PSG has several disadvantages: one of them is that a technician is required to use the device. In addition, the records must be taken in the hospital environment. The aim of this study is to develop a new machine learning based hybrid sleep/awake detection method with single channel ECG alternative to respiratory scoring. For this purpose, electrocardiography (ECG) signal of 10 patients with OSA was used. The Heart Rate Variable signal was derived from the ECG signal. Then, QRS components in different frequency bands were obtained from ECG signal by digital filtering. In this way, a total of nine more signals were obtained. Each of the nine signals consists of 25 features, which amounts to a total of 225 features. Fisher feature selection algorithm and Principal Component Analysis (PCA) were used to reduce the number of features. Ultimately the features extracted from the first received signals were classified with Decision Tree, Support Vector Machines, k-Nearest Neighborhood Algorithm and Ensemble classifiers. In addition, the proposed model was checked with the Leave One Out method. At the end of the study, for the detection of apnea, 82.11% accuracy with only 3 features and 85.12% accuracy with 13 features were obtained. The sensitivity and specificity values for the 3 properties are 0.82 and 0.82, respectively. For the 13 properties, 0.85 and 0.86, respectively. These results show that the proposed model can be used for the detection of Respiratory Scoring in the OSA diagnostic process.

Impact of flow disruptions in the delivery room

AimFlow disruptions (FDs) are deviations from the progression of care that compromise safety and efficiency of a specific process. The study aim was to identify the impact of FDs during neonatal resuscitation and determine their association with key process and outcome measures. MethodsProspective observational study of video recorded delivery room resuscitations of neonates <32 weeks gestational age. FDs were classified using an adaptation of Wiegmann’s FD tool. The primary outcome was target oxygenation saturation achievement at 5 min. Secondary outcomes included achieving target saturation at 10 min, time to positive pressure ventilation for initially apnoeic/bradycardic neonates, time to electrocardiogram signal, time to pulse oximetry signal, and time to stable airway. Multivariable logistic regression assessed association between FDs and achieving target saturations adjusting for gestational age and leader. Associations between FDs and time to event outcomes were assessed using Cox proportional hazards models. ResultsBetween 10/2017–7/2018, 32 videos were included. A mean of 52.6 FDs (standard deviation 17.9) occurred per resuscitation. Extraneous FDs were the most common FDs. FDs were associated with an adjusted odds ratio of 0.92 (95% confidence interval [CI] 0.80–1.05) of achieving target saturation at 5 min and 0.94 (95% CI 0.84–1.05) at 10 min. There was no significant evidence to show FDs were associated with time to event outcomes. ConclusionsFDs occurred frequently during neonatal resuscitation. Measuring FDs is a feasible method to assess the impact of human factors in the delivery room and identify modifiable factors and practices to improve patient care.

Successful home respiratory polygraphy to investigate sleep-disordered breathing in children

ObjectiveSleep-disordered breathing (SDB) in children is common. Interest in sleep tests, such as polygraphy (PG), which can be performed in a non-attended setting, are gaining is increasing. PG has, however, been little studied in children with co-morbidities other than obstructive sleep apnea (OSA), and in particular, if performed in a non-attended setting. We report on the feasibility and interpretability of implementing PGs at home versus in hospital. MethodsPGs were analyzed according to the setting (hospital or home) and sequence (initial or subsequent) in which they were performed. Non-interpretability was defined as absent or unreliable oxygen saturation by pulse oximetry (SpO2), or airflow and respiratory inductance plethysmography flow trace signals during the time analyzed. ResultsWe retrospectively analyzed 400 PGs; 332/400 were initial PGs. Indications were: suspected OSA (65%), obesity (13%), craniofacial malformations (5%), neuromuscular disease (4%), and other (13%) which included prematurity. 16% were recorded in hospitals and 84% at home. The mean age was 5.7 ± 5.8 years and 7.3 ± 4.5 years for the hospital and home groups, respectively. Interpretability was similar in both settings (87%). In the 68 subsequent PGs, interpretability was 84% when performed for follow-up and 96% when repeated for non-interpretability. Non-interpretability was predominantly due to a failure of the SpO2 channel. ConclusionsPG performed at home is both feasible and interpretable for a variety of indications. Non-interpretability was not predictable in association with the setting, anthropometric data, or indication, independently of the sequence (initial or subsequent PG) in which the parameters were analyzed.

Protocol for a multicentre retrospective observational cohort study in Denmark: association between the intraoperative peripheral perfusion index and postoperative morbidity and mortality in acute non-cardiac surgical patients

IntroductionPerioperative haemodynamic instability is associated with postoperative morbidity and mortality. Macrocirculatory parameters, such as arterial blood pressure and cardiac output are associated with poor outcome but may be uncoupled from...

Vision-Based Heart and Respiratory Rate Monitoring During Sleep - A Validation Study for the Population at Risk of Sleep Apnea.

A reliable, accessible, and non-intrusive method for tracking respiratory and heart rate is important for improving monitoring and detection of sleep apnea. In this study, an algorithm based on motion analysis of infrared video recordings was validated in 50 adults referred for clinical overnight polysomnography (PSG). The algorithm tracks the displacements of selected feature points on each sleeping participant and extracts respiratory rate using principal component analysis and heart rate using independent component analysis. For respiratory rate estimation (mean ± standard deviation), 89.89 % ± 10.95 % of the overnight estimation was accurate within 1 breath per minute compared to the PSG-derived respiratory rate from the respiratory inductive plethysmography signal, with an average root mean square error (RMSE) of 2.10 ± 1.64 breaths per minute. For heart rate estimation, 77.97 % ± 18.91 % of the overnight estimation was within 5 beats per minute of the heart rate derived from the pulse oximetry signal from PSG, with mean RMSE of 7.47 ± 4.79 beats per minute. No significant difference in estimation of RMSE of either signal was found according to differences in body position, sleep stage, or amount of the body covered by blankets. This vision-based method may prove suitable for overnight, non-contact monitoring of respiratory rate. However, at present, heart rate monitoring is less reliable and will require further work to improve accuracy.