Diagnosis Of Sleep Apnea-hypopnea Syndrome Research Articles

Mattress-Based Non-Influencing Sleep Apnea Monitoring System.

A mattress-type non-influencing sleep apnea monitoring system was designed to detect sleep apnea-hypopnea syndrome (SAHS). The pressure signals generated during sleep on the mattress were collected, and ballistocardiogram (BCG) and respiratory signals were extracted from the original signals. In the experiment, wavelet transform (WT) was used to reduce noise and decompose and reconstruct the signal to eliminate the influence of interference noise, which can directly and accurately separate the BCG signal and respiratory signal. In feature extraction, based on the five features commonly used in SAHS, an innovative respiratory waveform similarity feature was proposed in this work for the first time. In the SAHS detection, the binomial logistic regression was used to determine the sleep apnea symptoms in the signal segment. Simulation and experimental results showed that the device, algorithm, and system designed in this work were effective methods to detect, diagnose, and assist the diagnosis of SAHS.

Airline Point-of-Care System on Seat Belt for Hybrid Physiological Signal Monitoring.

With a focus on disease prevention and health promotion, a reactive and disease-centric healthcare system is revolutionized to a point-of-care model by the application of wearable devices. The convenience and low cost made it possible for long-term monitoring of health problems in long-distance traveling such as flights. While most of the existing health monitoring systems on aircrafts are limited for pilots, point-of-care systems provide choices for passengers to enjoy healthcare at the same level. Here in this paper, an airline point-of-care system containing hybrid electrocardiogram (ECG), breathing, and motion signals detection is proposed. At the same time, we propose the diagnosis of sleep apnea-hypopnea syndrome (SAHS) on flights as an application of this system to satisfy the inevitable demands for sleeping on long-haul flights. The hardware design includes ECG electrodes, flexible piezoelectric belts, and a control box, which enables the system to detect the original data of ECG, breathing, and motion signals. By processing these data with interval extraction-based feature selection method, the signals would be characterized and then provided for the long short-term memory recurrent neural network (LSTM-RNN) to classify the SAHS. Compared with other machine learning methods, our model shows high accuracy up to 84-85% with the lowest overfit problem, which proves its potential application in other related fields.

Dental caries status in adults with sleep apnea - hypopnea syndrome.

BackgroundA study is made of dental caries in a group of adults with sleep apnea-hypopnea syndrome (SAHS), establishing comparisons with healthy individuals corresponding to the same population. Material and MethodsA case-control series was analyzed, including patients with recently diagnosed SAHS and individuals without SAHS. Dental examinations were made to record the DMF (decayed, missing, filled) dental score, and demographic, lifestyle and clinical data were collected. ResultsA total of 114 participants (60 SAHS cases and 54 controls) were included in the study. Although the mean DMF score in the SAHS group was higher than in the control group (7.03 versus 4.81, respectively), the multivariate regression analysis did not find the difference to be statistically significant (p=0.351). However, a significant correlation was observed between the DMF score and age (r=0.41; p<0.001) and the apnea-hypopnea index (AHI)(r=0.31; p=0.003). ConclusionsOlder age and greater severity of SAHS are associated to higher DMF scores. However, the diagnosis of SAHS alone does not influence dental caries status. Key words:Dental caries, sleep apnea syndrome, oral health, DMF index.

A novel approach to diagnose sleep apnea using enhanced frequency extraction network

Sleep apnea-hypopnea syndrome (SAHS), as a widespread respiratory sleep disorder, if left untreated, can lead to a series of pathological changes. By using Polysomnography (PSG), traditional SAHS diagnosis tends to be complex and costly. Nasal airflow (NA) is the most direct reflection of the severity of SAHS. Therefore, we try to take advantage of NA signals that can be easily recorded by wearable devices. In this paper, we present an automatic detection approach of SAH events based on single-channel signal. Through this approach, an enhanced frequency extraction network is designed, which factorizes the mixed feature maps by their frequencies. And the spatial resolution of low-frequency components is reduced so as to save spending. Besides, in our research, the vanilla convolution block of the high-frequency components are replaced by residual blocks and smaller groups of filters with bigger size kernels. And we use the spatial attention module to facilitate feature extraction. Compared with state-of-the-art networks in this field, the promising results reveal that the proposed network for SAH events multiclass classification shows outstanding performance with accuracy of 91.23%, sensitivity of 90.81% and specificity of 90.59%. Thus, we believe that our approach, as a low-cost and high-efficiency solution, shows a great potential for detecting SAH events.

Sympathetic Hyperactivity and Sleep Disorders in Individuals With Type 2 Diabetes.

Introduction: Many studies on the impact of type 2 diabetes mellitus (T2DM) on sleep breathing have shown a higher prevalence and severity of sleep apnea-hypopnea syndrome (SAHS) in those with T2DM. Moreover, an increased activity of the sympathetic nervous system has been described in both pathologies. This cross-sectional study aimed to assess sympathetic activity in patients with T2DM, and to investigate the relationship between sympathetic activity and polysomnographic parameters.Materials and Methods: Thirty-six patients with T2DM without known clinical macrovascular nor pulmonary disease and 11 controls underwent respiratory polygraphy, and their cardiac variability and 24-h urine total metanephrines were measured.Results: SAHS was highly prevalent with a mean apnea-hypopnea index (AHI) in the range of moderate SAHS. In patients with T2DM, the nocturnal concentration of total metanephrines in urine were higher than diurnal levels [247.0 (120.0–1375.0) vs. 210.0 (92.0–670.0), p = 0.039]. The nocturnal total metanephrine concentration was positively and significantly associatedwith the percentage of sleeping time spent with oxygen saturation <90%(CT90). In the entire population and in subjects with T2DM, the multivariate regression analysis showed a direct interaction between the nocturnal concentration of urine metanephrines and the CT90.Conclusion: These findings suggest that the increase in sympathetic activity previously described in patients with T2DM could be mediated through nocturnal breathing disturbances. The diagnosis and treatment of SAHS may influence sympathetic activity disorders and may contribute to an improvement in T2DM and cardiovascular risk.

Usefulness of recurrence plots from airflow recordings to aid in paediatric sleep apnoea diagnosis

Background and objectiveIn-laboratory overnight polysomnography (PSG) is the gold standard method to diagnose the Sleep Apnoea-Hypopnoea Syndrome (SAHS). PSG is a complex, expensive, labour-intensive and time-consuming test. Consequently, simplified diagnostic methods are desirable. We propose the analysis of the airflow (AF) signal by means of recurrence plots (RP) features. The main goal of our study was to evaluate the utility of the information from RPs of the AF signals to detect paediatric SAHS at different levels of severity. In addition, we also evaluated the complementarity with the 3% oxygen desaturation index (ODI3). Methods946 AF and blood oxygen saturation (SpO2) recordings from children ages 0–13 years were used. The population under study was randomly split into training (60%) and test (40%) sets. RP was computed and 9 RP features were extracted from each AF recording. ODI3 was also calculated from each SpO2 recording. A feature selection stage was conducted in the training group by means of the fast correlation-based filter (FCBF) methodology to obtain a relevant and non-redundant optimum feature subset. A multi-layer perceptron neural network with Bayesian approach (BY-MLP), trained with these optimum features, was used to estimate the apnoea–hypopnoea index (AHI). Results8 of the RP features showed statistically significant differences (p-value <0.01) among the SAHS severity groups. FCBF selected the maximum length of the diagonal lines from RP, as well as the ODI3. Using these optimum features, the BY-MLP model achieved 83.2%, 78.5%, and 91.0% accuracy in the test group for the AHI thresholds 1, 5, and 10 events/h, respectively. Moreover, this model reached a negative likelihood ratio of 0.1 for 1 event/h and a positive likelihood ratio of 13.7 for 10 events/h. ConclusionsRP analysis enables extraction of useful SAHS-related information from overnight AF paediatric recordings. Moreover, it provides complementary information to the widely-used clinical variable ODI3. Thus, RP applied to AF signals can be used along with ODI3 to help in paediatric SAHS diagnosis, particularly to either confirm the absence of SAHS or the presence of severe SAHS.

Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis.

The sleep apnea-hypopnea syndrome (SAHS) is a chronic respiratory disorder of high prevalence among children (up to 4%). Nocturnal polysomnography (PSG) is the gold standard method to diagnose SAHS, which is a complex, expensive, and time-consuming test. Consequently, alternative simplified methods are demanded. We propose the analysis of the respiratory rate variability (RRV) signal, directly obtained from the airflow (AF) signals. The aim of our study is to evaluate the usefulness of the spectral information obtained from RRV in the diagnosis of pediatric SAHS. A database composed of 946 AF and blood oxygen saturation (SpO2) recordings from children between 0 and 13 years old was used. Our database was divided into four severity groups according to the apnea-hipopnea index (AHI): no-SAHS (AHI <; 1 events/h), mild (1 events/h ≤ AHI <; 5 events/h), moderate (5 events/h ≤ AHI <; 10 events/h), and severe SAHS (AHI ≥ 10 events/h). RRV and 3% oxygen desaturation index (ODI3) were obtained from AF and SpO2 recordings, respectively. A spectral band of interest was determined (0.09-0.20 Hz.) and a total of 12 spectral features were extracted. Nine of these features showed statistically significant differences (p-value <; 0.05) among the four severity groups. The spectral features from RRV along with ODI3 were used as inputs to binary logistic regression (LR) classifiers. The diagnostic performance of LR models were evaluated for the AHI cut-off points of 1, 5, and 10 e/h, achieving 66.5%, 84.0%, and 88.5% accuracy, respectively. These results outperformed those obtained by single ODI3. The joint use of the spectral information from RRV and ODI3 achieved a high diagnostic capability in the most severely-affected children, thus showing their complementarity. These results suggest that the information contained in RRV spectrum together with ODI3 is useful to help identify moderate-to-severe SAHS.

Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry.

Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent breathing disorder that is related to many negative consequences for the children's health and quality of life when it remains untreated. The gold standard for pediatric SAHS diagnosis (overnight polysomnography) has several limitations, which has led to the search for alternative tests. In this sense, automated analysis of overnight oximetry has emerged as a simplified technique. Previous studies have focused on the extraction of ad-hoc features from the blood oxygen saturation (SpO2) signal, which may miss useful information related to apnea and hypopnea (AH) events. In order to overcome this limitation of traditional approaches, we propose the use of convolutional neural networks (CNN), a deep learning technique, to automatically detect AH events from the SpO2 raw data. CHAT-baseline dataset, composed of 453 SpO2 recordings, was used for this purpose. A CNN model was trained using 60-s segments from the SpO2 signal using a training set (50% of subjects). Optimum hyperparameters of the CNN architecture were obtained using a validation set (25% of subjects). This model was applied to a third test set (25% of subjects), reaching 93.6% accuracy to detect AH events. These results suggest that the application of CNN may be useful to detect changes produced in the oximetry signal by AH events in pediatric SAHS patients.

Detrended fluctuation analysis of the oximetry signal to assist in paediatric sleep apnoea–hypopnoea syndrome diagnosis

Objective: To evaluate whether detrended fluctuation analysis (DFA) provides information that improves the diagnostic ability of the oximetry signal in the diagnosis of paediatric sleep apnoea–hypopnoea syndrome (SAHS). Approach: A database composed of 981 blood oxygen saturation (SpO2) recordings in children was used to extract DFA-derived features in order to quantify the scaling behaviour and the fluctuations of the SpO2 signal. The 3% oxygen desaturation index (ODI3) was also computed for each subject. Fast correlation-based filter (FCBF) was then applied to select an optimum subset of relevant and non-redundant features. This subset fed a multi-layer perceptron (MLP) neural network to estimate the apnoea–hypopnoea index (AHI). Main results: ODI3 and four features from the DFA reached significant differences associated with the severity of SAHS. An optimum subset composed of the slope in the first scaling region of the DFA profile and the ODI3 was selected using FCBF applied to the training set (60% of samples). The MLP model trained with this feature subset showed good agreement with the actual AHI, reaching an intra-class correlation coefficient of 0.891 in the test set (40% of samples). Furthermore, the estimated AHI showed high diagnostic ability, reaching an accuracy of 82.7%, 81.9%, and 91.1% using three common AHI cut-offs of 1, 5, and 10 events per hour (e h−1), respectively. These results outperformed the overall performance of ODI3. Significance: DFA may serve as a reliable tool to improve the diagnostic performance of oximetry recordings in the evaluation of paediatric patients with symptoms suggestive of SAHS.

Bispectral Analysis to Enhance Oximetry as a Simplified Alternative for Pediatric Sleep Apnea Diagnosis.

This study aims at assessing the bispectral analysis of blood oxygen saturation (SpO2) from nocturnal oximetry to help in pediatric sleep apnea-hypopnea syndrome (SAHS) diagnosis. Recent studies have found excessive redundancy in the SAHS-related information usually extracted from SpO2, while proposing only two features as a reduced set to be used. On the other hand, it has been suggested that SpO2 bispectral analysis is able to provide complementary information to common anthropometric, spectral, and clinical variables. We address these novel findings to assess whether bispectrum provides new non-redundant information to help in SAHS diagnosis. Thus, we use 981 pediatric SpO2 recordings to extract both the reduced set of features recently proposed as well as 9 bispectral features. Then, a feature selection method based on the fast correlationbased filter and bootstrapping is used to assess redundancy among all the features. Finally, the non-redundant ones are used to train a Bayesian multi-layer perceptron neural network (BYMLP) that estimate the apnea-hypopnea index (AHI), which is the diagnostic reference variable. Bispectral phase entropy was found complementary to the two previously recommended features and a BY-MLP model trained with the three of them reached high agreement with actual AHI (intra-class correlation coefficient = 0.889). Estimated AHI also showed high diagnostic ability, reaching 82.1%, 81.9%, and 90.3% accuracies and 0.814, 0.880, and 0.922 area under the receiver-operating characteristics curve for three common AHI thresholds: 1 e/h, 5 e/h, and 10 e/h, respectively. These results suggest that the information extracted from the bispectrum of SpO2 can improve the diagnostic performance of the oximetry test.

Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings.

Complexity, costs, and waiting list issues demand a simplified alternative for sleep apnea-hypopnea syndrome (SAHS) diagnosis. The blood oxygen saturation signal (SpO2) carries useful information about SAHS and can be easily acquired from overnight oximetry. In this study, SpO2 single-channel recordings from 320 subjects were obtained at patients' homes and were used to automatically obtain statistical, spectral, nonlinear, and clinical SAHS-related information. Relevant, nonredundant data from these analyses were subsequently used to train and validate four machine-learning methods with the ability to classify SpO2 signals into one of the four SAHS-severity degrees (no-SAHS, mild, moderate, and severe). All the models trained (linear discriminant analysis, 1-vs-all logistic regression, Bayesian multilayer perceptron, and AdaBoost) outperformed the diagnostic ability of the conventionally used 3% oxygen desaturation index. An AdaBoost model built with linear discriminants as base classifiers reached the highest figures. It achieved 0.479 Cohen's κ in the SAHS severity classification, as well as 92.9%, 87.4%, and 78.7% accuracies in binary classification tasks using increasing severity thresholds (apnea-hypopnea index: 5, 15, and 30 events/hour, respectively). These results suggest that machine-learning can be used along with SpO2 information acquired at a patients' home to help in SAHS diagnosis simplification.

The Development of a Dual-Radar System with Automatic Hypopnea Threshold Optimization for Contact-Free Sleep Apnea-Hypopnea Syndrome Screening

Full-night polysomnography (PSG) examination is regarded as the gold standard for the diagnosis of sleep apnea-hypopnea syndrome (SAHS). However, PSG requires the placement of multiple sensors on the head, face, and chest, which can impose a heavy strain on patients. Therefore, in the present study, we aimed to develop a contact-free, stand-alone SAHS screening system that eliminates body movement artifacts based on automatic optimization of the hypopnea threshold. Doppler radar sensors were placed beneath a mattress. In order to achieve high sensitivity and specificity, the hypopnea was based on the average amplitude of respiration during the full sleep period. The threshold was determined via receiver operating characteristic (ROC) analysis using PSG as a reference. We conducted full-night clinical tests of the proposed system in 27 patients with suspected SAHS (49 ± 12 years) at Tomei Atsugi Hospital. When predicting the severity of SAHS with an apnea-hypopnea index (AHI) of &gt;30/h using PSG as a reference, the proposed system achieved a sensitivity of 100% and a specificity of 100%. These results represent a drastic improvement over those of our previous study (sensitivity: 90%; specificity: 79%).

Computer-assisted diagnosis of the Sleep Apnea-Hypopnea Syndrome: An overview of different approaches.

Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine, and the costs associated to its manual diagnosis. The increment and heterogeneity of the different techniques, however, makes somewhat difficult to adequately follow recent developments. In this paper an overview within the area of computer-assisted diagnosis of SAHS has been performed. This overview of the different methods is presented together with a critical discussion of the current state-of-the-art.

Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review.

Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual diagnosis. The increment and heterogeneity of the different techniques, however, make it somewhat difficult to adequately follow the recent developments. A literature review within the area of computer-assisted diagnosis of SAHS has been performed comprising the last 15 years of research in the field. Screening approaches, methods for the detection and classification of respiratory events, comprehensive diagnostic systems, and an outline of current commercial approaches are reviewed. An overview of the different methods is presented together with validation analysis and critical discussion of the current state of the art.

Exploring the spectral information of airflow recordings to help in pediatric Obstructive Sleep Apnea-Hypopnea Syndrome diagnosis.

This work aims at studying the usefulness of the spectral information contained in airflow (AF) recordings in the context of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) in children. To achieve this goal, we defined two spectral bands of interest related to the occurrence of apneas and hypopneas. We characterized these bands by extracting six common spectral features from each one. Two out of the 12 features reached higher diagnostic ability than the 3% oxygen desaturation index (ODI3), a clinical parameter commonly used as screener for OSAHS. Additionally, the stepwise logistic regression (SLR) feature-selection algorithm showed that the information contained in the two bands was complementary, both between them and with ODI3. Finally, the logistic regression method involving spectral features from the two bands, as well as ODI3, achieved high diagnostic performance after a bootstrap validation procedure (84.6±9.6 sensitivity, 87.2±9.1 specificity, 85.8±5.2 accuracy, and 0.969±0.03 area under ROC curve). These results suggest that the spectral information from AF is helpful to detect OSAHS in children.

Síndrome de apneas-hipoapneas del sueño y factores de riesgo en el niño y el adolescente; revisión sistemática

The sleep apnea-hypopnea syndrome (SAHS) is a disorder that currently affects a large number of children and adolescents. The aim of this review is to assess the factors causing this condition and the risk of suffering another disease associated with SAHS. The 50 articles selected for inclusion in this review were identified through GOOGLE SCHOLAR. The validity of the items was established by the degree of evidence obtained, by recommendations made in this respect and by the applicability to the situation observed. The review considers studies of SAHS in children and adolescents, taking into account those relating this disorder with obesity, hypertension, physical activity and other variables. The studies reviewed show that SAHS is associated with childhood obesity and that it increases the risk of cerebro-cardiovascular disease. It is also shown that SAHS decreases children’s and adolescents’ physical capacity and quality of life. Psychological aspects are also affected, impacting on academic performance, which is poorer than in healthy children. Therefore, early paediatric diagnosis of SAHS is important in order to prevent associated disorders.

Pattern recognition in airflow recordings to assist in the sleep apnoea–hypopnoea syndrome diagnosis

This paper aims at detecting sleep apnoea-hypopnoea syndrome (SAHS) from single-channel airflow (AF) recordings. The study involves 148 subjects. Our proposal is based on estimating the apnoea-hypopnoea index (AHI) after global analysis of AF, including the investigation of respiratory rate variability (RRV). We exhaustively characterize both AF and RRV by extracting spectral, nonlinear, and statistical features. Then, the fast correlation-based filter is used to select those relevant and non-redundant. Multiple linear regression, multi-layer perceptron (MLP), and radial basis functions are fed with the features to estimate AHI. A conventional approach, based on scoring apnoeas and hypopnoeas, is also assessed for comparison purposes. An MLP model trained with AF and RRV selected features achieved the highest agreement with the true AHI (intra-class correlation coefficient=0.849). It also showed the highest diagnostic ability, reaching 92.5% sensitivity, 89.5% specificity and 91.5% accuracy. This suggests that AF and RRV can complement each other to estimate AHI and help in SAHS diagnosis.

Novel multiclass classification for home-based diagnosis of sleep apnea hypopnea syndrome

Currently, Sleep Apnea-Hypopnea Syndrome (SAHS) is accurately diagnosed in Sleep Units. In the last decade, in order to reduce the burden for health systems and the consequent impact in patients, several home-located methods based on the binary classification of SAHS using overnight pulse oximetry (SpO2) have been proposed. Binary classifiers give rise to higher accuracies, but the cost of misclassifying leads to high penalizations in terms of either health care costs or risks in patient’s health.This study presents a novel hierarchical classification scheme for the four-class SAHS diagnosis using a set of features extracted from SpO2 and reported in specialized literature. An accuracy of 82.6% was achieved in the assessment of the four-class classification. The proposed method could be useful in the diagnosis of SAHS in an ambulatory home-based setting and could alleviate under-diagnosis rate and the waiting lists in sleep units.

Utility of portable monitoring device with airflow, oxygen saturation and respiratory effort in the diagnosis of sleep apnea hypopnea syndrome

To validate the values of monitoring airflow, oxygen saturation and respiratory effort in the diagnosis of sleep apnea-hypopnea syndrome (SAHS). A total of 70 subjects with suspected SAHS underwent the tests of polysomnography (PSG) and portable monitoring device (PMD) separately at our sleep lab. The portable monitoring device recorded nasal airflow, oxygen saturation and respiratory effort. Apnea-hypopnea index (AHI) or respiratory disturbed index (RDI), lowest oxygen saturation (LSaO2), oxygen desaturation index (ODI4) and percentage of different types of sleep breathing events (central/obstructive/mixed hypopnea) accounting for the total numbers of sleep disordered breathing were also analyzed. The data of AHI and ODI4 showed skew distribution undergoing log transformation to approximate to normal distribution. Pair t test was used for the comparisons of different parameters. The agreement between two methods was analyzed by Bland-Altman plot. Fifty-eight subjects were diagnosed as SAHS with an AHI (RDI) over 5 on PSG. The sensitivity and specificity of portable monitoring device were 94.8% and 75.0% respectively. The mean AHI derived from PSG and RDI derived from PMD were (27 ± 25) and (29 ± 27) times per hour respectively and those after log transformation were (1.2 ± 0.5) and (1. 2 ± 0.5) times per hour (P = 0.411). The mean ODI4 derived from PSG and PMD were (23 ± 25) and (21 ± 24) and those after log transformation (0.9 ± 0.7) and (1.1 ± 0.5) times per hour respectively (P = 0.042). The mean values of LSaO2 were 79% ± 13% and 79% ± 12% respectively (P = 0.550). No significant differences existed between AHI derived from PSG and RDI derived from PMD. Bland-Altman plot also showed a high agreement between AHI derived from PSG and RDI derived from PMD. PMD could also identify major part of different events so as to aid clinical decision-making. Portable monitoring device recording airflow, oxygen saturation and respiratory effort shows a great agreement with PSG with regards to AHI (RDI) and the identification of different types of respiratory events.

Significance of Including a Surrogate Arousal for Sleep Apnea-Hypopnea Syndrome Diagnosis by Respiratory Polygraphy

Respiratory polygraphy is an accepted alternative to polysomnography (PSG) for sleep apnea/hypopnea syndrome (SAHS) diagnosis, although it underestimates the apnea-hypopnea index (AHI) because respiratory polygraphy cannot identify arousals. We performed a multicentric, randomized, blinded crossover study to determine the agreement between home respiratory polygraphy (HRP) and PSG, and between simultaneous respiratory polygraphy (respiratory polygraphy with PSG) (SimultRP) and PSG by means of 2 AHI scoring protocols with or without hyperventilation following flow reduction considered as a surrogate arousal. We included suspected SAHS patients from 8 hospitals. They were assigned to home and hospital protocols at random. We determined the agreement between respiratory polygraphy AHI and PSG AHI scorings using Bland and Altman plots and diagnostic agreement using receiver operating characteristic (ROC) curves. The agreement in therapeutic decisions (continuous positive airway pressure treatment or not) between HRP and PSG scorings was done with likelihood ratios and post-test probability calculations. Of 366 randomized patients, 342 completed the protocol. AHI from HRP scorings (with and without surrogate arousal) had similar agreement with PSG. AHI from SimultRP with surrogate arousal scoring had better agreement with PSG than AHI from SimultRP without surrogate arousal. HRP with surrogate arousal scoring had slightly worse ROC curves than HRP without surrogate arousal, and the opposite was true for SimultRP scorings. HRP with surrogate arousal showed slightly better agreement with PSG in therapeutic decisions than for HRP without surrogate arousal. Incorporating a surrogate arousal measure into HRP did not substantially increase its agreement with PSG when compared with the usual procedure (HRP without surrogate arousal).