Getting the Most Out of Nocturnal Pulse Oximetry

Effects of nocturnal complete denture usage on cardiorespiratory parameters: A pilot study

STATEMENT OF PROBLEM

Sleeping without conventional complete dentures (CCDs) has been stated by some to induce negative effects on the cardiorespiratory functions of edentulous patients with obstructive sleep apnea (OSA), although others have reported the exact opposite. Therefore, a consensus on nocturnal CCD usage is lacking.

PURPOSE

The purpose of this clinical study was to assess the effects of nocturnal denture usage on cardiorespiratory stability by using pulse oximetry (PO).

MATERIALS AND METHODS

Thirty CCD wearers were enrolled in the study. The first nocturnal pulse oximetry (FNPO) recordings were made on 3 different nights while the participants were sleeping without dentures (WOD). Oxygen desaturation index (ODI) and other PO parameters of the participants, including total respiratory event (TRE), basal SpO₂ (BSpO₂), time≤88 (T88), average low SpO₂ (ALSpO₂), total pulse event (TPE), average pulse rate (APR), and heart rate variability index (HRVI), were processed and the obtained data were recorded as WOD condition values. According to the ODI scores, the OSA status of the participants was grouped as normal (ODI<5), mild (5<ODI<15), moderate (15<ODI<30), or severe (ODI>30). Complete dentures were fabricated by an experienced prosthodontist and a dental laboratory technician by following conventional procedures. At the end of the first month of the follow-up period, the second nocturnal PO recordings (SNPO) were made on 3 different nights while the participants slept wearing dentures (WID), and the data obtained were recorded as WID condition values. The comparison of mean PO values obtained from WOD and WID were analyzed with the Wilcoxon signed- rank test (α=.05).

RESULTS

Significant differences were found between WOD and WID values in terms of TRE (P=.01), ODI (P=.001), ALSpO2 (P=.006), TPE (P=.001), and HRVI (P=.001) parameters. The significance of the improvements in the WID condition increased with the severity of OSA.

CONCLUSIONS

Improvements were observed in substantial cardiorespiratory parameters such as the ODI and HRVI of the participants wearing dentures nocturnally.

A REVIEW OF ECG-BASED DIAGNOSIS SUPPORT SYSTEMS FOR OBSTRUCTIVE SLEEP APNEA

Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy.

Regularity analysis of nocturnal oximetry recordings to assist in the diagnosis of sleep apnoea syndrome

The relationship between sleep apnoea-hypopnoea syndrome (SAHS) severity and the regularity of nocturnal oxygen saturation (SaO2) recordings was analysed. Three different methods were proposed to quantify regularity: approximate entropy (AEn), sample entropy (SEn) and kernel entropy (KEn). A total of 240 subjects suspected of suffering from SAHS took part in the study. They were randomly divided into a training set (96 subjects) and a test set (144 subjects) for the adjustment and assessment of the proposed methods, respectively. According to the measurements provided by AEn, SEn and KEn, higher irregularity of oximetry signals is associated with SAHS-positive patients. Receiver operating characteristic (ROC) and Pearson correlation analyses showed that KEn was the most reliable predictor of SAHS. It provided an area under the ROC curve of 0.91 in two-class classification of subjects as SAHS-negative or SAHS-positive. Moreover, KEn measurements from oximetry data exhibited a linear dependence on the apnoea-hypopnoea index, as shown by a correlation coefficient of 0.87. Therefore, these measurements could be used for the development of simplified diagnostic techniques in order to reduce the demand for polysomnographies. Furthermore, KEn represents a convincing alternative to AEn and SEn for the diagnostic analysis of noisy biomedical signals.

Analysis of nocturnal oxygen saturation recordings using kernel entropy to assist in sleep apnea-hypopnea diagnosis

In this study, a new entropy measure known as kernel entropy (KerEnt), which quantifies the irregularity in a series, was applied to nocturnal oxygen saturation (SaO(2)) recordings. A total of 96 subjects suspected of suffering from sleep apnea-hypopnea syndrome (SAHS) took part in the study: 32 SAHS-negative and 64 SAHS-positive subjects. Their SaO(2) signals were separately processed by means of KerEnt. Our results show that a higher degree of irregularity is associated to SAHS-positive subjects. Statistical analysis revealed significant differences between the KerEnt values of SAHS-negative and SAHS-positive groups. The diagnostic utility of this parameter was studied by means of receiver operating characteristic (ROC) analysis. A classification accuracy of 81.25% (81.25% sensitivity and 81.25% specificity) was achieved. Repeated apneas during sleep increase irregularity in SaO(2) data. This effect can be measured by KerEnt in order to detect SAHS. This non-linear measure can provide useful information for the development of alternative diagnostic techniques in order to reduce the demand for conventional polysomnography (PSG).

Automated frequency domain analysis of oxygen saturation as a screening tool for SAHS

Sleep apnea-hypopnea syndrome (SAHS) is significantly underdiagnosed and new screening systems are needed. The analysis of oxygen desaturation has been proposed as a screening method. However, when oxygen saturation (SpO(2)) is used as a standalone single channel device, algorithms working in time domain achieve either a high sensitivity or a high specificity, but not usually both. This limitation arises from the dependence of time-domain analysis on absolute SpO(2) values and the lack of standardized thresholds defined as pathological. The aim of this study is to assess the degree of concordance between SAHS screening using offline frequency domain processing of SpO(2) signals and the apnea-hypopnea index (AHI), and the diagnostic performance of such a new method. SpO(2) signals from 115 subjects were analyzed. Data were divided in a training data set (37) and a test set (78). Power spectral density was calculated and related to the desaturation index scored by physicians. A frequency desaturation index (FDI) was then estimated and its accuracy compared to the classical desaturation index and to the apnea-hypopnea index. The findings point to a high diagnostic agreement: the best sensitivity and specificity values obtained were 83.33% and 80.44%, respectively. Moreover, the proposed method does not rely on absolute SpO(2) values and is highly robust to artifacts.

A classification algorithm based on spectral features from nocturnal oximetry and support vector machines to assist in the diagnosis of obstructive sleep apnea

The aim of this study is to develop and evaluate an algorithm to help in the diagnosis of the obstructive sleep apnea syndrome (OSAS). Arterial oxygen saturation (SaO(2)) signals from nocturnal pulse oximetry were used to identify OSAS patients. A total of 149 SaO(2) recordings from subjects suspected of OSAS were available. The initial population was divided into a training set (74 subjects) and a test set (75 subjects) to optimize and evaluate our algorithm. Support vector machines (SVM) with Gaussian kernel were used to classify spectral features from SaO(2) signals. Several configurations of SVM were assessed by varying the regularization (C) and the kernel width (sigma) parameters. Finally, the selected SVM classifier (C = 235 and sigma = 0.4) provided an accuracy of 88.00% (84.44% sensitivity and 93.33% specificity) and an AROC of 0.921. Our results suggest that the proposed algorithm could be useful for OSAS screening.

Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry

The aim of this study is to assess the utility of traditional statistical pattern recognition techniques to help in obstructive sleep apnoea (OSA) diagnosis. Classifiers based on quadratic (QDA) and linear (LDA) discriminant analysis, K-nearest neighbours (KNN) and logistic regression (LR) were evaluated. Spectral and nonlinear input features from oxygen saturation (SaO(2)) signals were applied. A total of 187 recordings from patients suspected of suffering from OSA were available. This initial dataset was divided into training set (74 subjects) and test set (113 subjects). Twelve classification algorithms were developed by applying QDA, LDA, KNN and LR with spectral features, nonlinear features and combination of both groups. The performance of each algorithm was measured on the test set by means of classification accuracy and receiver operating characteristic (ROC) analysis. QDA, LDA and LR showed better classification capability than KNN. The classifier based on LDA with spectral features provided the best diagnostic ability with an accuracy of 87.61% (91.05% sensitivity and 82.61% specificity) and an area under the ROC curve (AROC) of 0.925. The proposed statistical pattern recognition techniques could be applied as an OSA screening tool.

Pulse oximetric thresholds for tonsillectomy and adenotomy in children: significance of 1-2% decline in oxyhemoglobin saturation

OBJECTIVES

We aimed to establish optimal overnight pulse oximetric thresholds for determining the indication of tonsillectomy and adenotomy (TA) in children by revising the definition of 'desaturation'.

METHODS

One hundred and thirty four children scheduled for TA (TA group, 5.3 +/- 1.4 years old) and 112 otherwise healthy children scheduled for elective minor surgery (control group, 5.4 +/- 1.5 years old) were enrolled into this prospective study. Data were recorded and stored every 10 s using Nellcor N-395. Desaturation/resaturation events were defined as x% change (x = 1-4) of SpO2 (oxyhemoglobin saturation by pulse oximetry) in 10 s. The desaturation/resaturation indices were calculated as events per hour of total sleeping time. For each index, a wide range of temporary thresholds was set. The optimal thresholds for TA were the ones that maximized the weighted average for sensitivity, specificity (based on whether the index improved or not after TA), and the percentage of the control children whose indices were below the threshold.

RESULTS

For all the indices, the optimal thresholds that fulfilled the above condition were determined. Compared with the x = 3-4% results, the application of x = 1-2% approximately doubled the TA patients whose preoperative 'positive' indices improved after TA, with the weighted averages of 84.3-92.3% as described above.

CONCLUSIONS

By defining desaturation/resaturation as a 1-2% change in SpO2 from the preceding value, children with adenotonsillar hypertrophy whose pulse oximetric indices are expected to improve after TA can be detected by pulse oximetry with relatively high sensitivity and specificity.

Poincaré analysis of an overnight arterial oxygen saturation signal applied to the diagnosis of sleep apnea hypopnea syndrome

The analysis of oxygen desaturations is a basic variable in polysomnographic studies for the diagnosis of sleep apnea. Several algorithms operating in the time domain already exist for sleep apnea detection via pulse oximetry, but in a disadvantageous way--they achieve either a high sensitivity or a high specificity. The aim of this study was to assess whether an alternative analysis of arterial oxygen saturation (SaO2) signals from overnight pulse oximetry could yield essential information on the diagnosis of sleep apnea hypopnea syndrome (SAHS). SaO2 signals from 117 subjects were analyzed. The population was divided into a learning dataset (70 patients) and a test set (47 patients). The learning set was used for tuning thresholds among the applied Poincaré quantitative descriptors. Results showed that the presence of apnea events in SAHS patients caused an increase in the SD1 Poincaré parameter. This conclusion was assessed prospectively using the test dataset. 90.9% sensitivity and 84.0% specificity were obtained in the test group. We conclude that Poincaré analysis could be useful in the study of SAHS, contributing to reduce the demand for polysomnographic studies in SAHS screening.

Utility of multilayer perceptron neural network classifiers in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry

The aim of this study is to assess the ability of multilayer perceptron (MLP) neural networks as an assistant tool in the diagnosis of the obstructive sleep apnoea syndrome (OSAS). Non-linear features from nocturnal oxygen saturation (SaO(2)) recordings were used to discriminate between OSAS positive and negative patients. A total of 187 subjects suspected of suffering from OSAS (111 with a positive diagnosis of OSAS and 76 with a negative diagnosis of OSAS) took part in the study. The initial population was divided into training, validation and test sets for deriving and testing our neural network classifier. Three methods were applied to extract non-linear features from SaO(2) signals: approximate entropy (ApEn), central tendency measure (CTM) and Lempel-Ziv complexity (LZC). The selected MLP-based classifier provided a diagnostic accuracy of 85.5% (89.8% sensitivity and 79.4% specificity). Our neural network algorithm could represent a useful technique for OSAS detection. It could contribute to reduce the demand for polysomnographic studies in OSAS screening.

Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry

The aim of this study is to assess the ability of radial basis function (RBF) classifiers as an assistant tool for the diagnosis of the obstructive sleep apnoea syndrome (OSAS). A total of 187 subjects suspected of suffering from OSAS were available for our research. The initial population was divided into training, validation and test sets for deriving and testing our neural classifiers. We used nonlinear features from nocturnal oxygen saturation (SaO(2)) to perform patients' classification. We evaluated three different RBF construction techniques based on the following algorithms: k-means (KM), fuzzy c-means (FCM) and orthogonal least squares (OLS). A diagnostic accuracy of 86.1, 84.7 and 85.5% was provided by the networks developed with KM, FCM and OLS, respectively. The three proposed networks achieved an area under the receiver operating characteristic (ROC) curve over 0.90. Our results showed that a useful non-invasive method could be applied to diagnose OSAS from nonlinear features of SaO(2) with RBF classifiers.

Utility of Approximate Entropy From Overnight Pulse Oximetry Data in the Diagnosis of the Obstructive Sleep Apnea Syndrome

Approximate entropy (ApEn) is a family of statistics introduced as a quantification of regularity in time series without any a priori knowledge about the system generating them. The aim of this preliminary study was to assess whether a time series analysis of arterial oxygen saturation (SaO2) signals from overnight pulse oximetry by means of ApEn could yield essential information on the diagnosis of obstructive sleep apnea (OSA) syndrome. We analyzed SaO2 signals from 187 subjects: 111 with a positive diagnosis of OSA and 76 with a negative diagnosis of OSA. We divided our data in a training set (44 patients with OSA Positive and 30 patients with OSA Negative) and a test set (67 patients with OSA Positive and 46 patients with OSA Negative). The training set was used for algorithm development and optimum threshold selection. Results showed that recurrence of apnea events in patients with OSA determined a significant increase in ApEn values. This method was assessed prospectively using the test dataset, where we obtained 82.09% sensitivity and 86.96% specificity. We conclude that ApEn analysis of SaO2 from pulse oximetric recording could be useful in the study of OSA.

Use of ambulatory overnight oximetry to investigate sleep apnea in a general internal medicine practice

OBJECTIVE

To examine how clinical factors and results from ambulatory overnight oximetry related to recommendations for further sleep evaluation in patients with clinically suspected obstructive sleep apnea syndrome.

PATIENTS AND METHODS

We reviewed 100 medical records selected randomly from 375 consecutive patients for whom ambulatory overnight oximetry had been ordered by internists and completed between September 1, 2001, and May 1, 2002. We analyzed relationships among clinical Information, oximetry results, resultant recommendations, and patient follow-up data.

RESULTS

Only 21 of 100 patients had normal results from ambulatory overnight oximetry; 5 were referred for further sleep consultation. Abnormal results from ambulatory overnight oximetry were seen in 79 patients, but only 51 were referred for further sleep evaluation. Abnormal results from ambulatory overnight oximetry were associated with only a small increased likelihood of referral (likelihood ratio, 2.7; confidence interval, 1.2-6.0). Those with an oxygen desaturation index (number of desaturation events per hour of recording time) of greater than 15 received sleep consultation at a median of 8 days after completion of oximetry, whereas those with an oxygen desaturation index of 6 to 10 were evaluated in a median of 42 days (P=.60). All 17 patients who had minimum oxygen saturation of less than 80% were referred for further evaluation.

CONCLUSIONS

Abnormal results from ambulatory overnight oximetry per se may not substantially influence internist referral of patients with clinically suspected sleep apnea for further sleep evaluation. Rather, severity of oximetry abnormalities is used along with other patient-related factors and sleep study accessibility to prioritize the need and urgency of further evaluation.