Influence of photoplethysmogram signal quality on pulse arrival time during polysomnography

Intervals of low-quality photoplethysmogram (PPG) signals might lead to significant inaccuracies in estimation of pulse arrival time (PAT) during polysomnography (PSG) studies. While PSG is considered to be a “gold standard” test for diagnosing obstructive sleep apnea (OSA), it also enables tracking apnea-related nocturnal blood pressure fluctuations correlated with PAT. Since the electrocardiogram (ECG) is recorded synchronously with the PPG during PSG, it makes sense to use the ECG signal for PPG signal-quality assessment. (1) Objective: to develop a PPG signal-quality assessment algorithm for robust PAT estimation, and investigate the influence of signal quality on PAT during various sleep stages and events such as OSA. (2) Approach: the proposed algorithm uses R and T waves from the ECG to determine approximate locations of PPG pulse onsets. The MESA database of 2055 PSG recordings was used for this study. (3) Results: the proportions of high-quality PPG were significantly lower in apnea-related oxygen desaturation (matched-pairs rc = 0.88 and rc = 0.97, compared to OSA and hypopnea, respectively, when p < 0.001) and arousal (rc = 0.93 and rc = 0.98, when p < 0.001) than in apnea events. The significantly large effect size of interquartile ranges of PAT distributions was between low- and high-quality PPG (p < 0.001, rc = 0.98), and regular and irregular pulse waves (p < 0.001, rc = 0.74), whereas a lower quality of the PPG signal was found to be associated with a higher interquartile range of PAT across all subjects. Suggested PPG signal quality-based PAT evaluation reduced deviations (e.g., rc = 0.97, rc = 0.97, rc = 0.99 in hypopnea, oxygen desaturation, and arousal stages, respectively, when p < 0.001) and allowed obtaining statistically larger differences between different sleep stages and events. (4) Significance: the implemented algorithm has the potential to increase the robustness of PAT estimation in PSG studies related to nocturnal blood pressure monitoring

Characterization of the microvascular cerebral blood flow response to obstructive apneic events during night sleep

Peer ReviewedPostprint (author's final draft

Oximetry use in obstructive sleep apnea

Producción CientíficaIntroduction. Overnight oximetry has been proposed as an accessible, simple, and reliable technique for obstructive sleep apnea syndrome (OSAS) diagnosis. From visual inspection to advanced signal processing, several studies have demonstrated the usefulness of oximetry as a screening tool. However, there is still controversy regarding the general application of oximetry as a single screening methodology for OSAS. Areas covered. Currently, high-resolution portable devices combined with pattern recognition-based applications are able to achieve high performance in the detection this disease. In this review, recent studies involving automated analysis of oximetry by means of advanced signal processing and machine learning algorithms are analyzed. Advantages and limitations are highlighted and novel research lines aimed at improving the screening ability of oximetry are proposed. Expert commentary. Oximetry is a cost-effective tool for OSAS screening in patients showing high pretest probability for the disease. Nevertheless, exhaustive analyses are still needed to further assess unattended oximetry monitoring as a single diagnostic test for sleep apnea, particularly in the pediatric population and in especial groups with significant comorbidities. In the following years, communication technologies and big data analysis will overcome current limitations of simplified sleep testing approaches, changing the detection and management of OSAS.This research has been partially supported by the projects DPI2017-84280-R and RTC-2015-3446-1 from Ministerio de Economía, Industria y Competitividad and European Regional Development Fund (FEDER), the project 66/2016 of the Sociedad Española de Neumología y Cirugía Torácica (SEPAR), and the project VA037U16 from the Consejería de Educación de la Junta de Castilla y León and FEDER. D. Álvarez was in receipt of a Juan de la Cierva grant IJCI-2014-22664 from the Ministerio de Economía y Competitividad

Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature

The uses of overnight pulse oximetry

© 2020 Indian Chest Society Published by Wolters Kluwer - Medknow. Overnight pulse oximetry (OPO) has proven to be an effective and beneficial technique to determine the cardiorespiratory status of patients in both the inpatient and outpatient settings. It is a cheap, safe, reliable, simple, and accurate method of patient monitoring as compared to the expensive and labor-intensive method of multichannel polysomnography for detecting sleep-disordered breathing. It provides accurate information about patient\u27s oxygenation status and also helps in monitoring the response to continuous positive airway pressure and in the surgical treatment of obstructive sleep apnea (OSA). Nocturnal hypoxemia portends a poor prognosis in patients of chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), and neuromuscular diseases. OPO can help its early detection and management

Sleep Breath

PurposeHome sleep testing devices are being widely used in diagnosis/screening for obstructive sleep apnea (OSA). We examined differences in OSA metrics obtained from two devices with divergent home monitoring strategies; the Apnea Risk Evaluation System (ARES\u2122, multiple signals plus forehead reflectance oximetry) and the Nonin WristOx2\u2122 (single channel finger transmission pulse oximeter), compared to differences from night-night variability of OSA.Methods152 male / 26 female subjects (BMI=30.3\ub15.6 kg/m2, age=52.5\ub18.9 yrs) were recruited without regard to OSA symptoms, and simultaneously wore both ARES\u2122 and Nonin WristOx2\u2122 for 2 nights (n=351 nights). Automated analysis of the WristOx2 yielded ODIOx2 (Oxygen Desaturation Index, # 654% O2 dips/hr) and automated analysis with manual editing of ARES\u2122 yielded AHI4ARES (apneas+hypopneas with 654% O2 dips/hr) and RDIARES (apneas+ hypopneas with 654% O2 dips/hr or arousal surrogates). Baseline awake oxygen saturation, percent time <90% O2 saturation (% time <90% O2Sat) and O2 signal loss were compared between the two methods.ResultsCorrelation between AHI4ARES and ODIOx2 was high (ICC=0.9, 95% CI=0.87\u20130.92, p<0.001, bias\ub1SD=0.7\ub16.1 events/hr). Agreement values for OSA diagnosis (77\u201385%) between devices was similar to that seen from night-to-night variability of OSA using a single device. Awake baseline O2 saturation was significantly higher in the ARES\u2122 (96.2\ub11.6%) than WristOx2\u2122 (92.2\ub12.1%, p<0.01). There was a significantly lower %time<90% O2Sat reported by the ARES\u2122 compared to WristOx2 (median (IQR) 0.5(0.0, 2.6) vs 2.1(0.3, 9.7), p<0.001) and the correlation was low (ICC=0.2).ConclusionsOSA severity metrics predominantly dependent on change in oxygen saturation and metrics used in diagnosis of OSA (AHI4 and ODI) correlated well across devices tested. However differences in cumulative oxygen desaturation measures (i.e.% time<90% O2Sat) between the devices suggest caution is needed when interpreting this metric particularly in populations likely to have significant hypoxia.K24 HL109156/HL/NHLBI NIH HHS/United StatesU01 OH010415/OH/NIOSH CDC HHS/United StatesU01OH010415/OH/NIOSH CDC HHS/United StatesK24HL109156/NH/NIH HHS/United States2019-03-01T00:00:00Z28823109PMC5818320vault:2626

Multi-Class AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome Severity from Oximetry Recordings Obtained at Home

Producción CientíficaThis paper aims at evaluating a novel multi-class methodology to establish Sleep Apnea-Hypopnea Syndrome (SAHS) severity by the use of single-channel at-home oximetry recordings. The study involved 320 participants derived to a specialized sleep unit due to SAHS suspicion. These were assigned to one out of the four SAHS severity degrees according to the apnea-hypopnea index (AHI): no-SAHS (AHI<5 events/hour), mild-SAHS (5≤AHI<15 e/h), moderate-SAHS (15≤AHI<30 e/h), and severe-SAHS (AHI≥30 e/h). A set of statistical, spectral, and non-linear features were extracted from blood oxygen saturation (SpO2) signals to characterize SAHS. Then, an optimum set among these features were automatically selected based on relevancy and redundancy analyses. Finally, a multi-class AdaBoost model, built with the optimum set of features, was obtained from a training set (60%) and evaluated in an independent test set (40%). Our AdaBoost model reached 0.386 Cohen’s kappa in the four-class classification task. Additionally, it reached accuracies of 89.8%, 85.8%, and 74.8% when evaluating the AHI thresholds 5 e/h, 15 e/h, and 30 e/h, respectively, outperforming the classic oxygen desaturation index. Our results suggest that SpO2 obtained at home, along with multi-class AdaBoost, are useful to detect SAHS severity.Junta de Castilla y León (project VA059U13)Pneumology and Thoracic Surgery Spanish Society (265/2012

Association Between Uric Acid Levels and Obstructive Sleep Apnea Syndrome in a Large Epidemiological Sample

Introduction: Recurrent hypoxia, which is associated with obstructive sleep apnea syndrome (OSAS), leads to an increase in the degradation of adenosine triphosphatase into xanthine, which in turn increases uric acid concentrations.Objective: the current study aimed to determine whether an association exists between OSAS and uric acid levels in the peripheral blood from a representative population of São Paulo (Brazil).Methods: A population-based survey adopting a probabilistic 3-stage cluster sample of São Paulo was used to represent the population according to gender, age, and socioeconomic class. A total of 1,042 volunteers underwent polysomnography recordings for OSAS diagnosis, blood pressure assessment, and biochemical blood analysis, and answered questionnaires.Results: Uric acid levels were correlated with most important risk factors for OSAS, such as AHI, desaturation time and index, minimum oxyhemoglobin saturation (SpO(2)), blood pressure, cholesterol, BMI, triglycerides and arousal, and with OSAS itself. Also, uric acid was increased in OSAS volunteers even after controlling for all confounders. Hyperuricemic volunteers presented lower mean and minimum SpO(2) and increased desaturation index. Importantly, minimum SpO(2) was a significant predictor of uric acid levels, which in turn was considered an independent predictor for OSAS in the binary logistic model. However, a ROC curve analysis for establishing cut-off points for uric acid levels as a biomarker of OSAS revealed moderate sensitivity and specificity.Conclusion: A strong association was found between uric acid levels and OSAS in a representative sample of the population of São Paulo. Although they do not qualify for a biomarker alone, uric acid levels may be involved in OSAS severity and should be considered in sleep apnea management in the future.Associacao Fundo de Incentivo a Pesquisa (AFIP)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Universidade Federal de São Paulo, Dept Psicobiol, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psicobiol, São Paulo, BrazilFAPESP: CEPID 98/14303-3FAPESP: 10/50129-1Web of Scienc