Automatic identification of sleep and wakefulness using single‐channel EEG and respiratory polygraphy signals for the diagnosis of obstructive sleep apnea

Polysomnography (PSG) is necessary for the accurate estimation of total sleep time (TST) and the calculation of the apnea–hypopnea index (AHI). In type III home sleep apnea testing (HSAT), TST is overestimated because of the lack of electrophysiological sleep recordings. The aim of this study was to evaluate the accuracy and reliability of a novel automated sleep/wake scoring algorithm combining a single electroencephalogram (EEG) channel with actimetry and HSAT signals. The study included 160 patients investigated by PSG for suspected obstructive sleep apnea (OSA). Each PSG was recorded and scored manually using American Academy of Sleep Medicine (AASM) rules. The automatic sleep/wake‐scoring algorithm was based on a single‐channel EEG (FP2‐A1) and the variability analysis of HSAT signals (airflow, snoring, actimetry, light and respiratory inductive plethysmography). Optimal detection thresholds were derived for each signal using a training set. Automatic and manual scorings were then compared epoch by epoch considering two states (sleep and wake). Cohen's kappa coefficient between the manual scoring and the proposed automatic algorithm was substantial, 0.74 ± 0.18, in separating wakefulness and sleep. The sensitivity, specificity and the positive and negative predictive values for the detection of wakefulness were 76.51% ± 21.67%, 95.48% ± 5.27%, 81.84% ± 15.42% and 93.85% ± 6.23% respectively. Compared with HSAT signals alone, AHI increased by 22.12% and 27 patients changed categories of OSA severity with the automatic sleep/wake‐scoring algorithm. Automatic sleep/wake detection using a single‐channel EEG combined with HSAT signals was a reliable method for TST estimation and improved AHI calculation compared with HSAT.     

Ambulatory detection of sleep apnea using a non‐contact biomotion sensor

The high prevalence of obstructive sleep apnea has led to increasing interest in ambulatory diagnosis. The SleepMinder? (SM) is a novel non‐contact device that employs radiofrequency wave technology to assess the breathing pattern, and thereby estimate obstructive sleep apnea severity. We assessed the performance of SleepMinder? in the home diagnosis of obstructive sleep apnea. One‐hundred and twenty‐two subjects were prospectively recruited in two protocols, one from an unselected sleep clinic cohort (n = 67, mean age 51 years) and a second from a hypertension clinic cohort (n = 55, mean age 58 years). All underwent 7 consecutive nights of home monitoring (SM_HOME) with the SleepMinder? as well as inpatient‐attended polysomnography in the sleep clinic cohort or cardiorespiratory polygraphy in the hypertension clinic cohort with simultaneous SleepMinder? recordings (SM_LAB). In the sleep clinic cohort, median SM_HOME apnea–hypopnea index correlated significantly with polysomnography apnea–hypopnea index (r = .68; p < .001), and in the hypertension clinic cohort with polygraphy apnea–hypopnea index (r = .7; p < .001). The median SM_HOME performance against polysomnography in the sleep clinic cohort showed a sensitivity and specificity of 72% and 94% for apnea–hypopnea index ≥ 15. Device performance was inferior in females. In the hypertension clinic cohort, SM_HOME showed a 50% sensitivity and 72% specificity for apnea–hypopnea index ≥ 15. SleepMinder? classified 92% of cases correctly or within one severity class of the polygraphy classification. Night‐to‐night variability in home testing was relatively high, especially at lower apnea–hypopnea index levels. We conclude that the SleepMinder? device provides a useful ambulatory screening tool, especially in a population suspected of obstructive sleep apnea, and is most accurate in moderate–severe obstructive sleep apnea.     

Quantitative assessment of the quality of home sleep studies: A computer‐assisted approach

Home monitoring is the most practical means of collecting sleep data in large‐scale research investigations. Because the portion of recording time with poor‐quality data is higher than in attended polysomnography, a quantitative assessment of the quality of each signal should be recommended. Currently, only qualitative or semi‐quantitative assessments are carried out, likely because of the lack of computer‐based applications to carry out this task efficiently. This paper presents an innovative computer‐assisted procedure designed to perform a quantitative quality assessment of standard respiratory signals recorded by Type 2 and Type 3 portable sleep monitors. The proposed system allows to assess the quality (good versus bad) of consecutive 1‐min segments of thoraco‐abdominal movements, oronasal, nasal airflow and oxygen saturation through an automatic classifier. The performance of the classifier was evaluated in a sample of 30 unattended polysomnography recordings, comparing the computer output with the consensus of two expert scorers. The difference (computer versus scorers) in the percentage of good‐quality segments was on average very small, ranging from ?3.1% (abdominal movements) to 0.8% (nasal flow), with an average total classification accuracy from 90.2 (oronasal flow) to 94.9 (nasal flow), a Sensitivity from 0.93 (oronasal flow) to 0.98 (nasal flow), and a Specificity from 0.74 (nasal flow) to 0.86 (abdominal movements). In practical applications, the scorer can run a check‐and‐edit procedure, further improving the classification accuracy. Considering a sample of 270 unattended polysomnography recordings (recording time: 545 ± 44 min), the average time taken for the check‐and‐edit procedure of each recording was 6.9 ± 2.1 min for all respiratory signals.     

The Utility of Single-Channel Nasal Airflow Pressure Transducer in the Diagnosis Of OSA at Home

Rationale:

Given the high prevalence of obstructive sleep apnea (OSA) and the demand on polysomnography (PSG), there is a need for low cost accurate simple diagnostic modalities that can be easily deployed in primary care to improve access to diagnosis.

Study Objectives:

The aim was to examine the utility of single-channel nasal airflow monitoring using a pressure transducer at home in patients with suspected OSA.

Design:

Cross-sectional study

Setting:

Laboratory and home

Participants:

The study was conducted in two populations. Consecutive patients with suspected OSA were recruited from the sleep disorders clinic at a tertiary referral center and from 6 local metropolitan primary care centers.

Interventions:

All patients answered questionnaires and had laboratory PSG. Nasal airflow was monitored for 3 consecutive nights at home in random order either before or after PSG.

Results:

A total of 193 patients participated (105 sleep clinic patients and 88 from primary care). The mean bias PSG apnea hypopnea index (AHI) minus nasal flow respiratory disturbance index (NF RDI) was –4.9 events per hour with limits of agreement (2 SD) of 27.8. NF RDI monitored over 3 nights had high accuracy for diagnosing both severe OSA (defined as PSG AHI > 30 events per hour) with area under the receiver operating characteristic curve (AUC) 0.92 (95% confidence interval (CI) 0.88-0.96) and any OSA (PSG AHI >5), AUC 0.87 (95% CI 0.80-0.94).

Conclusions:

Single-channel nasal airflow can be implemented as an accurate diagnostic tool for OSA at home in both primary care and sleep clinic populations.

Citation:

Makarie Rofail L; Wong KKH; Unger G; Marks GB; Grunstein RR. The utility of single-channel nasal airflow pressure transducer in the diagnosis of OSA at home. SLEEP 2010;33(8):1097-1105.     

Automatic scoring of sleep stages and cortical arousals using two electrodes on the forehead: validation in healthy adults

Accuracy and limitations of automatic scoring of sleep stages and electroencephalogram arousals from a single derivation (F_p1–F_p2) were studied in 29 healthy adults using a portable wireless polysomnographic recorder. All recordings were scored five times: twice by a referent scorer who viewed the standard polysomnographic montage and observed the American Academy of Sleep Medicine rules (referent scoring and blind rescoring); and once by the same scorer who viewed only the F_p1–F_p2 signal (alternative scoring), by another expert from the same institution, and by the algorithm. Automatic, alternative and independent expert scoring were compared with the referent scoring on an epoch‐by‐epoch basis. The algorithm's agreement with the reference (81.0%, Cohen's κ = 0.75) was comparable to the inter–rater agreement (83.3%, Cohen's κ = 0.78) or agreement between the referent scoring and manual scoring of the frontopolar derivation (80.7%, Cohen's κ = 0.75). Most misclassifications by the algorithm occurred during uneventful wake/sleep transitions, whereas cortical arousals, rapid eye movement and stable non‐rapid eye movement sleep were detected accurately. The algorithm yielded accurate estimates of total sleep time, sleep efficiency, sleep latency, arousal indices and times spent in different stages. The findings affirm the utility of automatic scoring of stages and arousals from a single frontopolar derivation as a method for assessment of sleep architecture in healthy adults.     

Modeling Respiratory Movement Signals During Central and Obstructive Sleep Apnea Events Using Electrocardiogram

Obstructive sleep apnea (OSA) causes a pause in airflow with continuing breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. CSA is recognized when respiratory effort falls below 15% of pre-event peak-to-peak amplitude of the respiratory effort. The aim of this study is to investigate whether a combination of respiratory sinus arrhythmia (RSA), ECG-derived respiration (EDR) from R-wave amplitudes and wavelet-based features of ECG signals during OSA and CSA can act as surrogate of changes in thoracic movement signal measured by respiratory inductance plethysmography (RIP). Therefore, RIP and ECG signals during 250 pre-scored OSA and 150 pre-scored CSA events, and 10 s preceding the events were collected from 17 patients. RSA, EDR, and wavelet decomposition of ECG signals at level 9 (0.15–0.32 Hz) were used as input to the support vector regression (SVR) model to recognize the RIP signals and classify OSA from CSA. Using cross-validation test, an optimal SVR (radial basis function kernel; C = 2⁸ and ε = 2⁻² where C is the coefficient for trade-off between empirical and structural risk and ε is the width of ε-insensitive region) showed that it correctly recognized 243/250 OSA and 139/150 CSA events (95.5% detection accuracy). Independent test was performed on 80 OSA and 80 CSA events from 12 patients. The independent test accuracies of OSA and CSA detections were found to be 92.5 and 95.0%, respectively. Results suggest superior performance of SVR using ECG as the surrogate in recognizing the reduction of respiratory movement during OSA and CSA. Results also indicate that ECG-based SVR model could act as a potential surrogate signal of respiratory movement during sleep-disordered breathing.     

Mandible behaviour interpretation during wakefulness,sleep and sleep‐disordered breathing

The mandible movement (MM) signal provides information on mandible activity. It can be read visually to assess sleep–wake state and respiratory events. This study aimed to assess (1) the training of independent scorers to recognize the signal specificities; (2) intrascorer reproducibility and (3) interscorer variability. MM was collected in the mid‐sagittal plane of the face of 40 patients. The typical MM was extracted and classified into seven distinct pattern classes: active wakefulness (AW), quiet wakefulness or quiet sleep (QW/S), sleep snoring (SS), sleep obstructive events (OAH), sleep mixed apnea (MA), respiratory related arousal (RERA) and sleep central events (CAH). Four scorers were trained; their diagnostic capacities were assessed on two reading sessions. The intra‐ and interscorer agreements were assessed using Cohen's κ. Intrascorer reproducibility for the two sessions ranged from 0.68 [95% confidence interval (CI): 0.59–0.77] to 0.88 (95% CI: 0.82–0.94), while the between‐scorer agreement amounted to 0.68 (95% CI: 0.65–0.71) and 0.74 (95% CI: 0.72–0.77), respectively. The overall accuracy of the scorers was 75.2% (range: 72.4–80.7%). CAH MMs were the most difficult to discern (overall accuracy 65.6%). For the two sessions, the recognition rate of abnormal respiratory events (OAH, CAH, MA and RERA) was excellent: the interscorer mean agreement was 90.7% (Cohen's κ: 0.83; 95% CI: 0.79–0.88). The discrimination of OAH, CAH, MA characteristics was good, with an interscorer agreement of 80.8% (Cohen's κ: 0.65; 95% CI: 0.62–0.68). Visual analysis of isolated MMs can successfully diagnose sleep–wake state, normal and abnormal respiration and recognize the presence of respiratory effort.     

Utility of actigraphy in the diagnosis of obstructive sleep apnea

STUDY OBJECTIVES: To determine whether adding actimetry to simplified polygraphy (respiratory-parameter monitoring without neurophysiologic variable recording) improves apnea-hypopnea index (AHI) evaluation as compared to simplified polygraphy alone. DESIGN: Comparison of AHI values obtained by all-night polysomnography and by simplified polygraphy with and without actimetry. SETTING: A teaching-hospital sleep laboratory in Garches, France. PATIENTS: 20 adults with suspected obstructive sleep apnea syndrome (OSAS). MEASUREMENTS AND RESULTS: Data were analyzed by two scorers working independently. AHI was calculated as the number of apneas and hypopneas per hour of sleep time (polysomnography: AHI-pg), per hour of time in bed (simplified polygraphy: AHI-tib), and per hour of actimetry-estimated total sleep time (AHI-act). AHI-pg showed that 12 patients had OSAS (AHI>10), which was severe (AHI > or =30) in eight. AHI-act was more closely correlated to AHI-pg (r=0.976) than was AHI-tib (r=0.940). According to the Bland and Altman method, AHI-tib underestimated the AHI in two patients and AHI-act overestimated the AHI in one patient. For the diagnosis of severe OSAS, sensitivity and negative predictive value were 50% and 75% with AHI-tib as compared to 88% and 92.5% with AHI-act. CONCLUSIONS: Actimetry, when added to simplified polygraphy, may assist in the diagnosis of OSAS.     

How to organise teaching activities for the scoring of cardiorespiratory polygraphies? Experiences from three Swedish Sleep Society teaching courses

Education in the scoring of sleep‐disordered breathing is organised differently and data on the evaluation of teaching strategies based on student ratings are sparse. The aim of the study was to analyse the gain in theoretical and practical knowledge achieved during different course settings offered by a national sleep society. The course contained 1 hr of theoretical teaching (scoring rules and recording methodology) and 2.5 hr of practical scoring (physiological breathing, obstructive/central sleep apnea and hypoventilation). Three different settings were provided: a large scoring group including participants with mixed experiences (1), a small scoring group with inexperienced scorers (2), and guided scoring for experienced scorers (3). Evaluation was performed at the end of the course. Data from 67 out of 82 participants (79%) were analysed (42 nurses/technicians and 25 physicians/dentists). Previous scoring experience was limited, moderate and extended in 25, 26, and 15 students, respectively. Gain of practical knowledge and overall course rating were significantly higher in settings 2 and 3 compared with 1 (p = 0.005 and p = 0.018 respectively). Guided scoring was the preferred practical teaching setting in experienced scorers, whereas scoring in large groups together with an experienced student was preferred in newcomers. Profession did not influence gain of knowledge, course satisfaction or preferred course design. From the student's perspective, consensus scoring in groups or guided scoring for all students may have advantages compared with single/small‐group scoring settings. Technical teaching knowledge of faculty members is of significant importance.     

Social inequalities in sleep‐disordered breathing: Evidence from the CoLaus|HypnoLaus study

Sleep‐disordered breathing is a common condition, related to a higher cardiometabolic and neurocognitive risk. The main risk factors for sleep‐disordered breathing include obesity, craniofacial characteristics, male sex and age. However, some studies have suggested that adverse socioeconomic circumstances and lifestyle‐related behaviours such as smoking and alcohol use, may also be risk factors for sleep‐disordered breathing. Here, we investigate the associations between socioeconomic status and sleep‐disordered breathing, as measured by sleep apnea–hypopnea and oxygen desaturation indexes. Furthermore, we assess whether these associations are explained by lifestyle‐related factors (smoking, sedentary behaviour, alcohol use and body mass index [BMI]). We used data from the CoLaus|HypnoLaus study, a population‐based study including 2162 participants from Lausanne (Switzerland). Socioeconomic status was measured through occupation and education. Sleep‐disordered breathing was assessed through polysomnography and measured using the apnea–hypopnea index (AHI: number of apnea/hypopnea events/hr: ≥15/≥30 events), and the ≥3% oxygen desaturation index (ODI: number of oxygen desaturation events/hr: ≥15/≥30 events). Lower occupation and education were associated with higher AHI and ODI (occupation: AHI30, odds ratio (OR) = 1.88, 95% confidence interval (CI) [1.07; 3.31]; ODI30, OR = 2.29, 95% CI [1.19; 4.39]; education: AHI30, OR = 1.21, 95% CI [0.85; 1.72]; ODI30, OR = 1.26, 95% CI [0.83; 1.91]). BMI was associated with socioeconomic status and AHI/ODI, and contributed to the socioeconomic gradient in SDB, with mediation estimates ranging between 43% and 78%. In this Swiss population‐based study, we found that low socioeconomic status is a risk factor for sleep‐disordered breathing, and that these associations are partly explained by BMI. These findings provide a better understanding of the mechanisms underlying social differences in sleep‐disordered breathing and may help implement policies for identifying high‐risk profiles for this disorder.     

A randomised crossover trial comparing nasal masks with oronasal masks: No differences in therapeutic pressures or residual apnea‐hypopnea indices

In treating obstructive sleep apnea (OSA), the use of oronasal masks with continuous positive airway pressure (CPAP) has been reported to increase pressure levels and reduce compliance. These reports come mostly from large observational studies. In this study, we examined the impact that oronasal masks have on 95th centile pressures, the residual apnea‐hypopnea index (AHI) and compliance compared with nasal masks. A randomised crossover design was implemented. Participants already established on CPAP were randomly allocated to a nasal mask or oronasal mask with auto‐titrating positive airway pressure (APAP) for 2 weeks. Participants then crossed over to use the alternate mask for another 2 weeks. Seventy‐one participants were recruited but only 60 completed the trial. There were no differences in median 95th centile pressure (nasal, 11.5 cm H₂O; oronasal, 11.7 cm H₂O; p = 0.115), median residual AHI (nasal, 4.9 events/hr; oronasal, 5.3 events/hr; p = 0.234) or median compliance (nasal, 7.3 hr/night; oronasal, 7.3 hr/night; p = 0.961). Only four patients had 95th centile pressures that were at least 1.5 cm H₂O greater with oronasal masks. Oronasal masks do not systematically increase therapeutic CPAP requirements. Rather, a small subset of patients display significant differences in CPAP levels.     

穿戴式呼吸感应体积描记用于睡眠呼吸事件检测

可穿戴式呼吸感应体积描记(背心式RIP)系统是我们根据呼吸感应体积描记技术的基本原理研发的一种可穿戴、低负荷的呼吸监测系统.在实现通气量无创测量的基础上,我们将该系统用于睡眠期呼吸事件检测,将该系统与多导睡眠图仪(PSG)对9例疑似睡眠呼吸暂停低通气综合症(SAHS)病人和7名健康男性志愿者进行同步对照检测与分析.通过对比实验,根据背心式RIP系统发生呼吸事件的特征性变化,提出了背心式RIP系统判别呼吸事件的规则.依据该规则,所有经背心式RIP系统诊断为SAHS患者的结果与PSG的诊断结果完全一致,背心式RIP系统检测呼吸事件的敏感性为97.8%,特异性为95.8%,实验结果表明背心式RIP系统能够可靠地检测出睡眠呼吸事件.由于其低生理、心理负荷特性,不需要佩带口鼻气流传感器,可用于家庭环境下、自然睡眠过程的睡眠呼吸紊乱性疾病的诊断.     

Accurate scoring of the apnea–hypopnea index using a simple non‐contact breathing sensor

Sleep apnea is a serious condition that afflicts many individuals and is associated with serious health complications. Polysomnography, the gold standard for assessing and diagnosing sleep apnea, uses breathing sensors that are intrusive and can disrupt the patient's sleep during the overnight testing. We investigated the use of breathing signals derived from non‐contact force sensors (i.e. load cells) placed under the supports of the bed as an alternative to traditional polysomnography breathing sensors (e.g. nasal pressure, oral‐nasal thermistor, chest belt and abdominal belt). The apnea–hypopnea index estimated using the load cells was not different than that estimated using standard polysomnography leads (t₄₄ = 0.37, P = 0.71). Overnight polysomnography sleep studies scored using load cell breathing signals had an intra‐class correlation coefficient of 0.97 for the apnea–hypopnea index and an intra‐class correlation coefficient of 0.85 for the respiratory disturbance index when compared with scoring using traditional polysomnography breathing sensors following American Academy of Sleep Medicine guidelines. These results demonstrate the feasibility of using unobtrusive load cells installed under the bed to measure the apnea–hypopnea index.     

Arousal responses to respiratory events during sleep: the role of pulse wave amplitude

The study aims at assessing the changes in electroencephalography (as measured by the A‐phases of cyclic alternating pattern) and autonomic activity (based on pulse wave amplitude) at the recovery of airway patency in patients with obstructive sleep apnea syndrome. Analysis of polysomnographic recordings from 20 male individuals with obstructive sleep apnea syndrome was carried out in total sleep time, non‐rapid eye movement and rapid eye movement sleep. Scoring quantified the combined occurrence (time range of 4 s before and 4 s after respiratory recovery) or separate occurrence of A‐phases (cortical activation), and pulse wave amplitude drops (below 30%) to apneas, hypopneas or flow limitation events. A dual response (A‐phase associated with a pulse wave amplitude drop) was the most frequent response (71.8% in total sleep time) for all types of respiratory events, with a progressive reduction from apneas to hypopneas and flow limitation events. The highly significant correlation in total sleep time (r = 0.9351; P < 0.0001) between respiratory events combined with A‐phases and respiratory events combined with pulse wave amplitude drops was confirmed both in non‐rapid eye movement (r = 0.9622; P < 0.0001) and rapid eye movement sleep (r = 0.7162; P < 0.0006). In conclusion, a dual cortical and autonomic activation is the most common manifestation at the recovery of airway patency. The significant correlation between A‐phases and relevant pulse wave amplitude drops suggests a possible role of pulse wave amplitude as a marker of cerebral response to respiratory events.     

Reliability of scoring respiratory disturbance indices and sleep staging

STUDY OBJECTIVES: Unattended, home-based polysomnography (PSG) is increasingly used in both research and clinical settings as an alternative to traditional laboratory-based studies, although the reliability of the scoring of these studies has not been described. The purpose of this study is to describe the reliability of the PSG scoring in the Sleep Heart Health Study (SHHS), a multicenter study of the relation between sleep-disordered breathing measured by unattended, in-home PSG using a portable sleep monitor, and cardiovascular outcomes. DESIGN: The reliability of SHHS scorers was evaluated based on 20 randomly selected studies per scorer, assessing both interscorer and intrascorer reliability. RESULTS: Both inter- and intrascorer comparisons on epoch-by-epoch sleep staging showed excellent reliability (kappa statistics >0.80), with stage 1 having the greatest discrepancies in scoring and stage 3/4 being the most reliably discriminated. The arousal index (number of arousals per hour of sleep) was moderately reliable, with an intraclass correlation (ICC) of 0.54. The scorers were highly reliable on various respiratory disturbance indices (RDIs), which incorporate an associated oxygen desaturation in the definition of respiratory events (2% to 5%) with or without the additional use of associated EEG arousal in the definition of respiratory events (ICC>0.90). When RDI was defined without considering oxygen desaturation or arousals to define respiratory events, the RDI was moderately reliable (ICC=0.74). The additional use of associated EEG arousals, but not oxygen desaturation, in defining respiratory events did little to increase the reliability of the RDI measure (ICC=0.77). CONCLUSIONS: The SHHS achieved a high degree of intrascorer and interscorer reliability for the scoring of sleep stage and RDI in unattended in-home PSG studies.     

Association between objective sleep measures and cognitive performance: a cross-sectional analysis in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) study

Sleep disturbances often co-exist, which challenges our understanding of their potential impact on cognition. We explored the cross-sectional associations of insomnia and objective measures of sleep with cognitive performance in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) study stratified by middle-aged and older adults. Participants aged ≥55 years underwent cognitive evaluations, polygraphy for 1 night, and actigraphy for 7 days. Insomnia was evaluated using the Clinical Interview Scheduled Revised. Obstructive sleep apnea (OSA) and short sleep duration (SSD) were defined by an apnea–hypopnea index (AHI) of ≥15 events/h and <6 h/ night, respectively. In 703 participants (mean [SD] age 62 [6] years, 44% men), cognition was evaluated using a 10-word list, verbal fluency, and trail-making tests. The frequencies of insomnia, SSD, and OSA were 11%, 24%, and 33%, respectively. In all, 4% had comorbid OSA and insomnia, and 11% had both OSA and SSD. Higher wake after sleep onset (β = −0.004, 95% confidence interval [CI] −0.008, −0.001) and the number of awakenings (β = −0.006, 95% CI −0.012, −0.001) were associated with worse verbal fluency performance. Compared to those without insomnia, older participants with insomnia had worse global performance (β = −0.354, 95% CI −0.671, −0.038). Insomnia was an effect modifier in the associations between AHI and executive function performance (p for the interaction between insomnia and AHI = 0.004) and between oxygen saturation <90% and memory performance (p for the interaction between insomnia and oxygen saturation = 0.02). Although some associations between sleep measures and cognition were significant, they should be considered with caution due to the large sample size and multiple testing performed in this study.     

Actigraphy scoring reliability in the study of osteoporotic fractures

STUDY OBJECTIVES: The editing and scoring of actigraphy data are important for calculating variables that describe sleep. Scoring is dependent on marking time points for when a participant got in and out of bed, plus time when the actigraph was removed. This placement of time points is subject to error. We examined interscorer reliability to determine if files scored by 2 different people were comparable. DESIGN: Observational study. SETTING: Community-based. PARTICIPANTS: A subset of 36 women taken from the latest biannual visit of the Study of Osteoporotic Fractures. All women had actigraphy data scored by 1 scorer for the Study of Osteoporotic Fractures staff, plus a blinded rescoring by an expert scorer at a different site. INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: The outcomes of interest from actigraphy are duration of in-bed interval, total sleep time, sleep latency, sleep efficiency, wake after sleep onset, total nap time, and total daytime minutes of watch removal. Clearly documented actigraphy scoring procedures were used. There were no significant differences between the expert scorer and the study scorer in sleep outcomes (all P values >.16 from a paired t test). There was a small but statistically significant difference between scorers for watch removal times (mean absolute difference 3.4 minutes +/- 5.4, P=.02). The intraclass correlation coefficients showed a high level of agreement (range, 0.84-0.99). CONCLUSIONS: Even in a large study with 2 scorers, it is possible to use actigraphy as a measure of sleep without introducing interscorer measurement error. Using well-documented scoring and data-gathering procedures are essential for data quality control.     

Automated Sleep Apnea Quantification Based on Respiratory Movement

Obstructive sleep apnea (OSA) is a prevalent and treatable disorder of neurological and medical importance that is traditionally diagnosed through multi-channel laboratory polysomnography(PSG). However, OSA testing is increasingly performed with portable home devices using limited physiological channels. We tested the hypothesis that single channel respiratory effort alone could support automated quantification of apnea and hypopnea events. We developed a respiratory event detection algorithm applied to thoracic strain-belt data from patients with variable degrees of sleep apnea. We optimized parameters on a training set (n=57) and then tested performance on a validation set (n=59). The optimized algorithm correlated significantly with manual scoring in the validation set (R² = 0.73 for training set, R² = 0.55 for validation set; p<0.05). For dichotomous classification, the AUC was >0.92 and >0.85 using apnea-hypopnea index cutoff values of 5 and 15, respectively. Our findings demonstrate that manually scored AHI values can be approximated from thoracic movements alone. This finding has potential applications for automating laboratory PSG analysis as well as improving the performance of limited channel home monitors.     

Impact of sleep‐disordered breathing in patients with acute myocardial infarction: a retrospective analysis

Sleep‐disordered breathing (SDB) is associated with an increased risk of cardiovascular events. Previous studies showed that severe SDB has a negative impact on myocardial salvage and progression of left ventricular dysfunction after acute myocardial infarction (AMI). This study investigated the frequency of SDB and the effects of SDB on left ventricular function after AMI. This retrospective study enrolled all patients with AMI who had undergone cardiorespiratory polygraphy for SDB diagnosis. The apnea–hypopnea index was used as a standard metric of SDB severity. SDB was classified as mild (apnea–hypopnea index >5 to <15 per h), moderate (≥15 to <30 per h) or severe (apnea–hypopnea index ≥30 per h). According to the majority of events, SDB was classified as predominant obstructive sleep apnea, central sleep apnea or mixed sleep apnea (mixed SDB). A total of 223 patients with AMI (112 with ST elevation and 111 without ST elevation; 63.2 ± 11.2 years, 82% male, left ventricular ejection fraction 49 ± 12%) were enrolled. SDB was present in 85.6%, and was moderate‐to‐severe in 63.2%; 40.8% had obstructive sleep apnea, 41.7% had central sleep apnea and 3.1% had mixed SDB. Left ventricular ejection fraction was lower in patients with AMI with severe SDB (45 ± 14%) versus those without SDB (57 ± 7%; P < 0.005). In addition, lower left ventricular ejection fraction (≤45%) was associated with increased frequency (apnea–hypopnea index ≥5 per h in 96%) and severity (apnea–hypopnea index ≥30 per h in 48%) of SDB in general and a higher percentage of central sleep apnea (57%) in particular. SDB is highly frequent in patients with AMI. SDB severity appeared to be linked to impaired left ventricular function, especially in patients with central sleep apnea.     

Comparison of a single‐channel EEG sleep study to polysomnography

An accurate home sleep study to assess electroencephalography (EEG)‐based sleep stages and EEG power would be advantageous for both clinical and research purposes, such as for longitudinal studies measuring changes in sleep stages over time. The purpose of this study was to compare sleep scoring of a single‐channel EEG recorded simultaneously on the forehead against attended polysomnography. Participants were recruited from both a clinical sleep centre and a longitudinal research study investigating cognitively normal ageing and Alzheimer's disease. Analysis for overall epoch‐by‐epoch agreement found strong and substantial agreement between the single‐channel EEG compared to polysomnography (κ = 0.67). Slow wave activity in the frontal regions was also similar when comparing the single‐channel EEG device to polysomnography. As expected, Stage N1 showed poor agreement (sensitivity 0.2) due to lack of occipital electrodes. Other sleep parameters, such as sleep latency and rapid eye movement (REM) onset latency, had decreased agreement. Participants with disrupted sleep consolidation, such as from obstructive sleep apnea, also had poor agreement. We suspect that disagreement in sleep parameters between the single‐channel EEG and polysomnography is due partially to altered waveform morphology and/or poorer signal quality in the single‐channel derivation. Our results show that single‐channel EEG provides comparable results to polysomnography in assessing REM, combined Stages N2 and N3 sleep and several other parameters, including frontal slow wave activity. The data establish that single‐channel EEG can be a useful research tool.