Speech dysfunction of obstructive sleep apnea. A discriminant analysis of its descriptors

Estimating the severity of obstructive sleep apnea during wakefulness using speech: A review

Obstructive sleep apnea (OSA) is a chronic breathing disorder during sleep that affects 10-30% of adults in North America. The gold standard for diagnosing OSA is polysomnography (PSG). However, PSG has several drawbacks, for example, it is a cumbersome and expensive procedure, which can be quite inconvenient for patients. Additionally, patients often have to endure long waitlists before they can undergo PSG. As a result, other alternatives for screening OSA have gained attention. Speech, as an accessible modality, is generated by variations in the pharyngeal airway, vocal tract, and soft tissues in the pharynx, which shares similar anatomical structures that contribute to OSA. Consequently, in this study, we aim to provide a comprehensive review of the existing research on the use of speech for estimating the severity of OSA. In this regard, a total of 851 papers were initially identified from the PubMed database using a specified set of keywords defined by population, intervention, comparison and outcome (PICO) criteria, along with a concatenated graph of the 5 most cited papers in the field extracted from ConnectedPapers platform. Following a rigorous filtering process that considered the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach, 32 papers were ultimately included in this review. Among these, 28 papers primarily focused on developing methodology, while the remaining 4 papers delved into the clinical perspective of the association between OSA and speech. In the next step, we investigate the physiological similarities between OSA and speech. Subsequently, we highlight the features extracted from speech, the employed feature selection techniques, and the details of the developed models to predict OSA severity. By thoroughly discussing the current findings and limitations of studies in the field, we provide valuable insights into the gaps that need to be addressed in future research directions.

Association between obstructive sleep apnea and risk of Benign vocal fold lesions: A nationwide 9-year follow-up cohort study

Since obstructive sleep apnea (OSA) affects various parts of the body, there has been little interest about the effect of OSA on voice. The objective of this study was to evaluate the risk of benign vocal fold lesions (BVFL) in OSA patients. This study used data from the National Health Insurance Service (NHIS) database. The study group was defined as the group diagnosed with OSA between 2008 and 2011. Non-OSA groups were selected based on propensity score (PS) matching. Incidence of BVFL among participants during the follow-up was analyzed. Cox proportional hazard regression analyses were performed to evaluate the association between OSA and incident BVFL. The HR value of the OSA group calculated by considering 8 variables indicates that the risk of developing BVFL is 79% higher than that of the control group. Further, among OSA patients, patients with a history of OP had a 35% lower risk of developing BVFL. The relationships between BVFL and 7 individual variables considered were as follows: For age, HR for the 40 to 59 years group was 1.20 (95%CI, 1.09-1.32). For sex, the HR in the female group was 1.22 (95%CI, 1.10-1.35). For residential areas, the HR values for "Seoul" 1.39 (95%CI, 1.23-1.59). In the high economic status group, the HR was 1.10 (95%CI, 1.01-1.21). This observational study indicated that OSA is associated with an increased incidence of BVFL. The incidence of BVFL increased with older age, female sex, and high SES.

What is the Effect of CPAP Treatment With Humidifier on Vocal Quality?

OBJECTIVE

Evaluate vocal quality in patients with OSA before and after continuous use of CPAP with a humidifier using subjective patient perception and clinical assessment. The hypothesis was that CPAP treatment with a humidifier would benefit voice quality.

STUDY DESIGN

Randomized, sham-controlled, blinded clinical trial.

METHODS

Forty-three natal males with obstructive sleep apnea for whom CPAP treatment was recommended following polysomnography were randomized into two therapy groups: CPAP and Sham-CPAP. Participants completed questionnaires on voice use, a voice self-assessment with the ten-item vocal handicap index (VHI-10), and complementary questionnaires: the Epworth sleepiness scale (ESS), Pittsburgh sleep quality index (PSQI), reflux symptoms index (LPRSI) and oral dryness visual analog scale (DRY). Their voices were recorded at three different times: before CPAP therapy, and after 3 and 6 months of continuous CPAP use. The acoustic voice quality index (AVQI), and an auditory-perceptual judgment (APJ) were also applied before and after the CPAP and Sham treatments.

RESULTS

After 6 months of treatment, the CPAP group presented improvements in their sleep patterns; however, no statistically significant differences were observed between the groups in respect of the results of the voice-related questionnaires, the AVQI values, and the APJ of the voice quality. All of the participants had some degree of vocal deviation at baseline.

CONCLUSIONS

CPAP therapy with a humidifier did not improve vocal quality as evaluated by the clinician or patient self-assessment. However, it did not have any significant negative effects on voice quality, so can be considered safe to use in male OSA patients.

Selection of OSA-specific pronunciations and assessment of disease severity assisted by machine learning

STUDY OBJECTIVES

To screen all of the obstructive sleep apnea (OSA)-characteristic pronunciations, explore the pronunciations which provide a better OSA classification effect than those used previously, and further clarify the correlation between speech signals and OSA.

METHODS

A total of 158 adult male Mandarin native speakers who completed polysomnography at the Sleep Medicine Center of Beijing Tongren Hospital from November 15, 2019, to January 19, 2020, were enrolled in this study. All Chinese syllables were collected from each participant in the sitting position. The syllables, vowels, consonants, and tones were screened to identify the pronunciations that were most effective for OSA classification.

RESULTS

The linear prediction coefficients of Chinese syllables were extracted as features and mathematically modeled using a decision tree model to dichotomize participants with apnea-hypopnea index thresholds of 10 and 30 events/h, and the leave-one-out method was used to verify the classification performance of Chinese syllables for OSA. Chinese syllables such as [leng] and [jue], consonant pronunciations such as [zh] and [f], and vowel pronunciations such as [ing] and [ai] were the most suitable pronunciations for classification of OSA. An OSA classification model consisting of several syllable combinations was constructed, with areas under curve of 0.83 (threshold of apnea-hypopnea index = 10) and 0.87 (threshold of apnea-hypopnea index = 30), respectively.

CONCLUSIONS

This study is the first comprehensive screening of OSA-characteristic pronunciations and can act as a guideline for the construction of OSA speech corpora in other languages.

CITATION

Ding Y, Sun Y, Li Y, et al. Selection of OSA-specific pronunciations and assessment of disease severity assisted by machine learning. J Clin Sleep Med. 2022;18(11):2663-2672.

Severity evaluation of obstructive sleep apnea based on speech features

PURPOSE

There are upper airway abnormalities in patients with obstructive sleep apnea (OSA), and their speech signal characteristics are different from those of unaffected people. In this study, the severity of OSA was evaluated automatically by machine learning technology based on the speech signals of Chinese people.

METHODS

In total, 151 adult male Mandarin native speakers who had suspected OSA completed polysomnography to assess the severity of the disease. Chinese vowels and nasal sounds were recorded in sitting and supine positions, and the accuracy of predicting the apnea-hypopnea index (AHI) of the participants using a machine learning method was analyzed based on features extracted from the speech signals.

RESULTS

Among the 151 participants, 75 had AHI > 30 events/h, and 76 had AHI ≤ 30 events/h. Various features including linear prediction cepstral coefficients (LPCC) were extracted from the data collected from participants recorded in the sitting and supine positions and by using a linear support vector machine (SVM); we classified the participants with thresholds of AHI = 30 and AHI = 10 events/h. The accuracies of the classifications were both 78.8%, the sensitivities were 77.3% and 79.1%, and the specificities were 80.3% and 78.0%, respectively.

CONCLUSION

This study constructed a severity evaluation model of OSA based on speech signal processing and machine learning, which can be used as an effective method to screen patients with OSA. In addition, it was found that Chinese pronunciation can be used as an effective feature to predict OSA.

Obstructive Sleep Apnea in Women: Study of Speech and Craniofacial Characteristics

Background

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by frequent cessation of breathing lasting 10 seconds or longer. The diagnosis of OSA is performed through an expensive procedure, which requires an overnight stay at the hospital. This has led to several proposals based on the analysis of patients’ facial images and speech recordings as an attempt to develop simpler and cheaper methods to diagnose OSA.

Objective

The objective of this study was to analyze possible relationships between OSA and speech and facial features on a female population and whether these possible connections may be affected by the specific clinical characteristics in OSA population and, more specifically, to explore how the connection between OSA and speech and facial features can be affected by gender.

Methods

All the subjects are Spanish subjects suspected to suffer from OSA and referred to a sleep disorders unit. Voice recordings and photographs were collected in a supervised but not highly controlled way, trying to test a scenario close to a realistic clinical practice scenario where OSA is assessed using an app running on a mobile device. Furthermore, clinical variables such as weight, height, age, and cervical perimeter, which are usually reported as predictors of OSA, were also gathered.

Acoustic analysis is centered in sustained vowels. Facial analysis consists of a set of local craniofacial features related to OSA, which were extracted from images after detecting facial landmarks by using the active appearance models. To study the probable OSA connection with speech and craniofacial features, correlations among apnea-hypopnea index (AHI), clinical variables, and acoustic and facial measurements were analyzed.

Results

The results obtained for female population indicate mainly weak correlations (r values between .20 and .39). Correlations between AHI, clinical variables, and speech features show the prevalence of formant frequencies over bandwidths, with F2/i/ being the most appropriate formant frequency for OSA prediction in women. Results obtained for male population indicate mainly very weak correlations (r values between .01 and .19). In this case, bandwidths prevail over formant frequencies. Correlations between AHI, clinical variables, and craniofacial measurements are very weak.

Conclusions

In accordance with previous studies, some clinical variables are found to be good predictors of OSA. Besides, strong correlations are found between AHI and some clinical variables with speech and facial features. Regarding speech feature, the results show the prevalence of formant frequency F2/i/ over the rest of features for the female population as OSA predictive feature. Although the correlation reported is weak, this study aims to find some traces that could explain the possible connection between OSA and speech in women. In the case of craniofacial measurements, results evidence that some features that can be used for predicting OSA in male patients are not suitable for testing female population.

Obstructive sleep apnea severity estimation: Fusion of speech-based systems

Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder. Previous studies associated OSA with anatomical abnormalities of the upper respiratory tract that may be reflected in the acoustic characteristics of speech. We tested the hypothesis that the speech signal carries essential information that can assist in early assessment of OSA severity by estimating apnea-hypopnea index (AHI). 198 men referred to routine polysomnography (PSG) were recorded shortly prior to sleep onset while reading a one-minute speech protocol. The different parts of the speech recordings, i.e., sustained vowels, short-time frames of fluent speech, and the speech recording as a whole, underwent separate analyses, using sustained vowels features, short-term features, and long-term features, respectively. Applying support vector regression and regression trees, these features were used in order to estimate AHI. The fusion of the outputs of the three subsystems resulted in a diagnostic agreement of 67.3% between the speech-estimated AHI and the PSG-determined AHI, and an absolute error rate of 10.8 events/hr. Speech signal analysis may assist in the estimation of AHI, thus allowing the development of a noninvasive tool for OSA screening.

Voice disorders and associated risk markers among young adults in the United States

OBJECTIVES/HYPOTHESIS

To examine the prevalence of voice disorders in young adults and identify sociodemographic factors, health conditions, and behaviors associated with voice disorder prevalence.

STUDY DESIGN

Cross-sectional analysis of data from the National Longitudinal Study of Adolescent to Adult Health.

METHODS

During home interviews, 14,794 young adults, aged 24 to 34 years, reported their health conditions and behaviors. Presence and duration of voice disorders were reported over the past 12 months. We computed overall and stratified prevalence estimates by age, gender, race/ethnicity, medical conditions, smoking, and alcohol use. Multiple logistic regression was used to identify independent risk factors for a voice disorder while accounting for the complex sample design.

RESULTS

Six percent of participants reported a voice disorder lasting at least 3 days. Females had 56% greater odds of voice disorders than males. Number of days drinking alcohol was associated with voice disorders, but number of smoking days was not. Conditions that increased the likelihood of voice disorders included hypertension (OR = 1.42 [95% confidence interval {CI}: 1.07-1.89]), tinnitus (OR = 1.53 [95% CI: 1.06-2.20]), and anxiety/panic disorder (OR = 1.26 [95% CI: 1.00-1.60]). Results were independent of gender, alcohol consumption, upper respiratory symptoms, and lower respiratory conditions including asthma, bronchitis/emphysema, and gastrointestinal symptoms (diarrhea/nausea/vomiting).

CONCLUSIONS

Voice disorders in young adulthood were associated with hypertension, tinnitus, and anxiety. Greater awareness of these relationships may facilitate voice evaluation among people who seek healthcare for these chronic conditions.

LEVEL OF EVIDENCE

2b Laryngoscope, 127:2093-2099, 2017.

Reviewing the connection between speech and obstructive sleep apnea

BACKGROUND

Sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). The altered UA structure or function in OSA speakers has led to hypothesize the automatic analysis of speech for OSA assessment. In this paper we critically review several approaches using speech analysis and machine learning techniques for OSA detection, and discuss the limitations that can arise when using machine learning techniques for diagnostic applications.

METHODS

A large speech database including 426 male Spanish speakers suspected to suffer OSA and derived to a sleep disorders unit was used to study the clinical validity of several proposals using machine learning techniques to predict the apnea-hypopnea index (AHI) or classify individuals according to their OSA severity. AHI describes the severity of patients' condition. We first evaluate AHI prediction using state-of-the-art speaker recognition technologies: speech spectral information is modelled using supervectors or i-vectors techniques, and AHI is predicted through support vector regression (SVR). Using the same database we then critically review several OSA classification approaches previously proposed. The influence and possible interference of other clinical variables or characteristics available for our OSA population: age, height, weight, body mass index, and cervical perimeter, are also studied.

RESULTS

The poor results obtained when estimating AHI using supervectors or i-vectors followed by SVR contrast with the positive results reported by previous research. This fact prompted us to a careful review of these approaches, also testing some reported results over our database. Several methodological limitations and deficiencies were detected that may have led to overoptimistic results.

CONCLUSION

The methodological deficiencies observed after critically reviewing previous research can be relevant examples of potential pitfalls when using machine learning techniques for diagnostic applications. We have found two common limitations that can explain the likelihood of false discovery in previous research: (1) the use of prediction models derived from sources, such as speech, which are also correlated with other patient characteristics (age, height, sex,…) that act as confounding factors; and (2) overfitting of feature selection and validation methods when working with a high number of variables compared to the number of cases. We hope this study could not only be a useful example of relevant issues when using machine learning for medical diagnosis, but it will also help in guiding further research on the connection between speech and OSA.

Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). OSA is generally diagnosed through a costly procedure requiring an overnight stay of the patient at the hospital. This has led to proposing less costly procedures based on the analysis of patients' facial images and voice recordings to help in OSA detection and severity assessment. In this paper we investigate the use of both image and speech processing to estimate the apnea-hypopnea index, AHI (which describes the severity of the condition), over a population of 285 male Spanish subjects suspected to suffer from OSA and referred to a Sleep Disorders Unit. Photographs and voice recordings were collected in a supervised but not highly controlled way trying to test a scenario close to an OSA assessment application running on a mobile device (i.e., smartphones or tablets). Spectral information in speech utterances is modeled by a state-of-the-art low-dimensional acoustic representation, called i-vector. A set of local craniofacial features related to OSA are extracted from images after detecting facial landmarks using Active Appearance Models (AAMs). Support vector regression (SVR) is applied on facial features and i-vectors to estimate the AHI.

Detection of Obstructive sleep apnea in awake subjects by exploiting body posture effects on the speech signal

Obstructive sleep apnea (OSA) is a common sleep disorder. OSA is associated with several anatomical and functional abnormalities of the upper airway. It was shown that these abnormalities in the upper airway are also likely to be the reason for increased rate of apneic events in the supine position. Functional and structural changes in the vocal tract can affect the acoustic properties of speech. We hypothesize that acoustic properties of speech that are affected by body position may aid in distinguishing between OSA and non-OSA patients. We aimed to explore the possibility to differentiate OSA and non-OSA patients by analyzing the acoustic properties of their speech signal in upright sitting and supine positions. 35 awake patients were recorded while pronouncing sustained vowels in the upright sitting and supine positions. Using linear discriminant analysis (LDA) classifier, accuracy of 84.6%, sensitivity of 92.7%, and specificity of 80.0% were achieved. This study provides the proof of concept that it is possible to screen for OSA by analyzing and comparing speech properties acquired in upright sitting vs. supine positions. An acoustic-based screening system during wakefulness may address the growing needs for a reliable OSA screening tool; further studies are needed to support these findings.

Automatic detection of obstructive sleep apnea using speech signals

Obstructive sleep apnea (OSA) is a common disorder associated with anatomical abnormalities of the upper airways that affects 5% of the population. Acoustic parameters may be influenced by the vocal tract structure and soft tissue properties. We hypothesize that speech signal properties of OSA patients will be different than those of control subjects not having OSA. Using speech signal processing techniques, we explored acoustic speech features of 93 subjects who were recorded using a text-dependent speech protocol and a digital audio recorder immediately prior to polysomnography study. Following analysis of the study, subjects were divided into OSA (n=67) and non-OSA (n=26) groups. A Gaussian mixture model-based system was developed to model and classify between the groups; discriminative features such as vocal tract length and linear prediction coefficients were selected using feature selection technique. Specificity and sensitivity of 83% and 79% were achieved for the male OSA and 86% and 84% for the female OSA patients, respectively. We conclude that acoustic features from speech signals during wakefulness can detect OSA patients with good specificity and sensitivity. Such a system can be used as a basis for future development of a tool for OSA screening.

Obstructive sleep apnea, seizures, and childhood apraxia of speech

Associations between obstructive sleep apnea and motor speech disorders in adults have been suggested, though little has been written about possible effects of sleep apnea on speech acquisition in children with motor speech disorders. This report details the medical and speech history of a nonverbal child with seizures and severe apraxia of speech. For 6 years, he made no functional gains in speech production, despite intensive speech therapy. After tonsillectomy for obstructive sleep apnea at age 6 years, he experienced a reduction in seizures and rapid growth in speech production. The findings support a relationship between obstructive sleep apnea and childhood apraxia of speech. The rather late diagnosis and treatment of obstructive sleep apnea, especially in light of what was such a life-altering outcome (gaining functional speech), has significant implications. Most speech sounds develop during ages 2-5 years, which is also the peak time of occurrence of adenotonsillar hypertrophy and childhood obstructive sleep apnea. Hence it is important to establish definitive diagnoses, and to consider early and more aggressive treatments for obstructive sleep apnea, in children with motor speech disorders.

Neuropsychological Functioning Associated with High-Altitude Exposure

This article focuses on neuropsychological functioning at moderate, high, and extreme altitude. This article summarizes the available literature on respiratory, circulatory, and brain determinants on adaptation to hypoxia that are hypothesized to be responsible for neuropsychological impairment due to altitude. Effects on sleep are also described. At central level, periventricular focal damages (leuko-araiosis) and cortical atrophy have been observed. Frontal lobe and middle temporal lobe alterations are also presumed. A review is provided regarding the effects on psychomotor performance, perception, learning, memory, language, cognitive flexibility, and metamemory. Increase of reaction time and latency of P300 are observed. Reduced thresholds of tact, smell, pain, and taste, together with somesthetic illusions and visual hallucinations have been reported. Impairment in codification and short-term memory are especially noticeable above 6,000 m. Alterations in accuracy and motor speed are identified at lower altitudes. Deficits in verbal fluency, language production, cognitive fluency, and metamemory are also detected. The moderating effects of personality variables over the above-mentioned processes are discussed. Finally, methodological flaws found in the literature are detailed and some applied proposals are suggested.

University of Cincinnati Dyspnea Questionnaire for Evaluation of Dyspnoea during physical and speech activities in patients with chronic obstructive pulmonary disease: a validation analysis

University of Cincinnati Dyspnea Questionnaire (UCDQ) was developed to measure the impact of dyspnoea during (1) physical activity (Phys), (2) speech activity (Speech) and (3) simultaneous speech and physical activity (Comb). The aim of this study was to evaluate the validity of UCDQ in COPD patients, comparing it to a large set of dyspnoeic indices and functional parameters. Fifty COPD patients (age 58.7 +/- 9.1 years, FEV1%pred = 39.3 +/- 17.0%, Baseline Dyspnoea Index (BDI) = 4.9 +/- 2.5, Six Minute Walk Distance (6MWD) = 373 +/- 128 m, Symptoms score = 9.4 +/- 2.5; mean +/- SD) participated in the study. We found the following mean scores for the three sections of the questionnaire: Phys = 3.5 +/- 0.9; Speech = 2.4 +/- 1.1; Comb = 4.2 +/- 1.0, meaning that patients report the most breathlessness during the combination of speaking and physical activity and the least breathlessness during speech activities. All three section of UCDQ had significant strong correlation with dyspnoea indices (BDI, Borg, MRC, OCD), 6MWD and symptoms score, which proves its concurrent and construct validity. Differentiation of patients by speech section (=3<) discriminated them significantly with respect to all dyspnoeic indices, symptoms score and 6MWD. All three dimensions of UCDQ had high test-retest reliability - ICC between 0.76 and 0.93. Factor analysis yielded three interpretable factors, as all dyspnoeic indices, three sections of UCDQ, symptoms score and 6MWD were loaded on the first factor. In conclusion, UCDQ provides valid and reliable information about the effect of dyspnoea on speech and daily activities.

Can snoring induce or worsen obstructive sleep apnea?

Snoring and sleep apneas are breathing disorders intimately associated during sleep. Most snorers are 'simple' or 'nonapneic', as the prevalence of snoring is much higher than that of sleep apneas. The vibrations transmitted to the pharyngeal structures by snoring span a large range of frequencies, while the energy transmitted may reach high values. A deleterious effect of these vibrations can therefore be considered. In 1983 a group of investigators from Bologna described five cases of heavy snorers of increasing severity, suggesting that they correspond to the natural history of 'heavy snorers' disease'. The present article reviews the data published since 1983 in favor of this hypothesis: anatomic lesions of the upper airway mucosa, pharyngeal muscles and nerves, and clinical observations in snorers. The conclusion stresses the absence of ultimate proof in favor of this attractive hypothesis: we lack the demonstration of a significant increase of the incidence of sleep apnea in a group of nonapneic snorers in a longitudinal follow-up study.

Acoustic analysis of vowel emission in obstructive sleep apnea

We studied vocalization in 18 men with obstructive sleep apnea syndrome (OSAS) (age, 49 [7.5] years; body mass index [BMI] 33.6 [7.6]) and 10 normal men as a control group (age, 46.7 [6.2] years; BMI 24.6 [2.2]). Polysomnographic data for patients with OSAS were as follows: total sleep time (TST), 387.5 [27.9] min; awake, 17.6 (12.6% TST); stage 1, 19.8 (18.7 percent TST); stage 2, 54.8 (23.2 percent TST); stage 3 and 4, 1.5 (0.3 percent TST); and stage REM, 4.2 (1.7 percent TST). Apnea hypopnea index (AHI) was 43.0 (18.2) and lowest O2 saturation was 73.6 (11.4). We recorded the following sounds in all subjects: /a/ as in "father"; /e/ as in "get"; /i/ as in "see"; /o/ as in "go"; /u/ as in "too." Three maneuvers for each vowel sound were taken for analysis. Signals were digitized at 10,000 Hz. Fast Fourier transformation was applied to segments of 512 points of each utterance corresponding to the vowel sound. The following parameters were obtained: maximum frequency of harmonics, mean frequency of harmonics, and the number of harmonics.

RESULTS

There were significant differences between both groups in the maximum frequency of harmonics of /i/ and /e/ vowels. (For /i/: 2,650 [672] Hz controls; 425 [71.2] Hz OSAS. For /e/: 2,605 [772.3] Hz controls; 1,250.0 [828.4] OSAS). The number of harmonics for /i/ vowel was 4.5 (1.2) for controls as compared with 2.7 (1) Hz for OSAS.

CONCLUSIONS

Vocalization in patients with OSAS is different from normal subjects. Vowel /i/ can distinguish these patients from normal subjects.

Sleep apnea syndrome: symptomatology, associated features, and neurocognitive correlates

This article reviews the essential features, types, prevalence, pathophysiology, and neuropsychological correlates associated with the sleep apnea syndrome. Persons who experience the intermittent hypoxia and fragmented sleep characteristic of the sleep apnea syndrome tend to exhibit moderate symptoms of diffuse cognitive dysfunction as well as multiple emotional and psychosocial sequela. It is concluded that more research is required in order to elucidate the relationship between the hypoxic parameters and neurocognitive deficits seen in the sleep apnea syndrome, and that neuropsychological assessment might represent a means whereby the effectiveness of various treatments for sleep apnea may be evaluated.