The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability.

The work considers automatic sleep stage classification, based on heart rate variability (HRV) analysis, with a focus on the distinction of wakefulness (WAKE) from sleep and rapid eye movement (REM) from non-REM (NREM) sleep. A set of 20 automatically annotated one-night polysomnographic recordings was considered, and artificial neural networks were selected for classification. For each inter-heartbeat (RR) series, beside features previously presented in literature, we introduced a set of four parameters related to signal regularity. RR series of three different lengths were considered (corresponding to 2, 6, and 10 successive epochs, 30 s each, in the same sleep stage). Two sets of only four features captured 99 % of the data variance in each classification problem, and both of them contained one of the new regularity features proposed. The accuracy of classification for REM versus NREM (68.4 %, 2 epochs; 83.8 %, 10 epochs) was higher than when distinguishing WAKE versus SLEEP (67.6 %, 2 epochs; 71.3 %, 10 epochs). Also, the reliability parameter (Cohens's Kappa) was higher (0.68 and 0.45, respectively). Sleep staging classification based on HRV was still less precise than other staging methods, employing a larger variety of signals collected during polysomnographic studies. However, cheap and unobtrusive HRV-only sleep classification proved sufficiently precise for a wide range of applications.

Monitoring nocturnal heart rate with bed sensor.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems". OBJECTIVES: The aim of this study is to assess the reliability of the estimated Nocturnal Heart Rate (HR), recorded through a bed sensor, compared with the one obtained from standard electrocardiography (ECG). METHODS: Twenty-eight sleep deprived patients were recorded for one night each through matrix of piezoelectric sensors, integrated into the mattress, through polysomnography (PSG) simultaneously. The two recording methods have been compared in terms of signal quality and differences in heart beat detection. RESULTS: On average, coverage of 92.7% of the total sleep time was obtained for the bed sensor, testifying the good quality of the recordings. The average beat-to-beat error of the inter-beat intervals was 1.06%. These results suggest a good overall signal quality, however, considering fast heart rates (HR > 100 bpm), performances were worse: in fact, the sensitivity in the heart beat detection was 28.4% while the false positive rate was 3.8% which means that a large amount of fast beats were not detected. CONCLUSIONS: The accuracy of the measurements made using the bed sensor has less than 10% of failure rate especially in periods with HR lower than 70 bpm. For fast heart beats the uncertainty increases. This can be explained by the change in morphology of the bed sensor signal in correspondence of a higher HR.

Sleep-wake detection based on respiratory signal acquired through a pressure bed sensor.

This study proposes an automatic method for the sleep-wake staging in normal and pathologic sleep based only on respiratory effort acquired from a Pressure Bed Sensor (PBS). Motion and respiratory movements were obtained through a PBS and sleep-wake staging was evaluated from those time series. 20 all night polysomnographies, with annotations, used as gold standard and the time series coming from the PBS were used to develop and to evaluate the automatic wake-sleep staging. The database was built up by: 10 healthy subjects and 10 patients with severe sleep apnea. The agreement of the statistical measures between the automatic classification and the human scoring were: 83.59 ± 6.79 of sensitivity, 83.60 ± 15.13 of specificity and 81.91 ± 6.36 of accuracy. These results suggest that some important indexes, such as sleep efficiency, could be computed through a contactless technique.

Tracheal sound parameters of respiratory cycle phases show differences between flow-limited and normal breathing during sleep.

The objective of the present work was to develop new computational parameters to examine the characteristics of respiratory cycle phases from the tracheal breathing sound signal during sleep. Tracheal sound data from 14 patients (10 males and 4 females) were examined. From each patient, a 10 min long section of normal and a 10 min section of flow-limited breathing during sleep were analysed. The computationally determined proportional durations of the respiratory phases were first investigated. Moreover, the phase durations and breathing sound amplitude levels were used to calculate the area under the breathing sound envelope signal during inspiration and expiration phases. An inspiratory sound index was then developed to provide the percentage of this type of area during the inspiratory phase with respect to the combined area of inspiratory and expiratory phases. The proportional duration of the inspiratory phase showed statistically significantly higher values during flow-limited breathing than during normal breathing and inspiratory pause displayed an opposite difference. The inspiratory sound index showed statistically significantly higher values during flow-limited breathing than during normal breathing. The presented novel computational parameters could contribute to the examination of sleep-disordered breathing or as a screening tool.

Intelligent methods for identifying respiratory cycle phases from tracheal sound signal during sleep.

We present two methods for identifying respiratory cycle phases from tracheal sound signal during sleep. The methods utilize the Hilbert transform in envelope extraction. They determine automatically a patient-specific amplitude threshold to be used in the detection. The core of one method is designed to be amplitude-independent whereas the other method uses solely the amplitude information. The methods provided average sensitivities of 98% and 99%, respectively, and positive prediction values of 100% on the total of 1434 respiratory cycles analysed from six different patients. The developed methods seem promising as such or as tools for analysing sleep disordered breathing.

High frequency components of tracheal sound are emphasized during prolonged flow limitation.

A nasal pressure transducer, which is used to study nocturnal airflow, also provides information about the inspiratory flow waveform. A round flow shape is presented during normal breathing. A flattened, non-round shape is found during hypopneas and it can also appear in prolonged episodes. The significance of this prolonged flow limitation is still not established. A tracheal sound spectrum has been analyzed further in order to achieve additional information about breathing during sleep. Increased sound frequencies over 500 Hz have been connected to obstruction of the upper airway. The aim of the present study was to examine the tracheal sound signal content of prolonged flow limitation and to find out whether prolonged flow limitation would consist of abundant high frequency activity. Sleep recordings of 36 consecutive patients were examined. The tracheal sound spectral analysis was performed on 10 min episodes of prolonged flow limitation, normal breathing and periodic apnea-hypopnea breathing. The highest total spectral amplitude, implicating loudest sounds, occurred during flow-limited breathing which also presented loudest sounds in all frequency bands above 100 Hz. In addition, the tracheal sound signal during flow-limited breathing constituted proportionally more high frequency activities compared to normal breathing and even periodic apnea-hypopnea breathing.

New tracheal sound feature for apnoea analysis.

Sleep apnoea syndrome is common in the general population and is currently underdiagnosed. The aim of the present work was to develop a new tracheal sound feature for separation of apnoea events from non-apnoea time. Ten overnight recordings from apnoea patients containing 1,107 visually scored apnoea events totalling 7 h in duration and 72 h of non-apnoea time were included in the study. The feature was designed to describe the local spectral content of the sound signal. The median, maximum and mean smoothing of different time scales were compared in the feature extraction. The feature was designed to range from 0 to 1 irrespective of tracheal sound amplitudes. This constant range could offer application of the feature without patient-specific adjustments. The overall separation of feature values during apnoea events from non-apnoea time across all patients was good, reaching 80.8%. Due to the individual differences in tracheal sound signal amplitudes, developing amplitude-independent means for screening apnoea events is beneficial.

Improved computational fronto-central sleep depth parameters show differences between apnea patients and control subjects.

All-night EEG recordings from 12 male apnea patients and 12 age-matched healthy control subjects were studied in the present work. The spectral mean frequency was used to provide computational sleep depth curves from two frontopolar and two central EEG channels. Our previously presented computational parameters quantifying the properties of the sleep depth curves were improved. The resulting light sleep percentage (LS%) values were higher in apnea patients than in control subjects in the right central brain position (P = 0.028), in concordance to our previous work. Moreover, apnea patients showed higher LS% values in the right frontopolar position (P = 0.008). Also, apnea patients showed a smaller anteroposterior sleep depth difference than control subjects on the right hemisphere (P = 0.002). These are interesting new findings, achieved by the present methodology. Thus, the developed computational parameters were able to quantify, at least to some degree, the disruption of sleep process caused by the recurrent apneic events.

The effect of estrogen plus progestin treatment on sleep: a randomized, placebo-controlled, double-blind trial in premenopausal and late postmenopausal women.

OBJECTIVE: In this prospective randomized, placebo-controlled and double-blind study, the objective was to investigate the effects of estrogen-progestin treatment (EPT) on sleep in pre- and postmenopausal women. DESIGN: Seventeen premenopausal (aged 45-51 years) and 18 postmenopausal (aged 58-70 years) women were studied in a sleep laboratory for two nights (one night for adaptation and one study night) before and after 6 months of treatment with EPT or placebo. During the treatment period, premenopausal women received cyclic EPT or placebo and the postmenopausal women continuous EPT or placebo. Polysomnography and questionnaires were used to evaluate sleep and well-being. RESULTS: At the end of the treatment period, premenopausal women receiving EPT had more awakenings from stage 1 sleep (p = 0.047) and postmenopausal women with EPT had a greater total number of awakenings (p = 0.031) than the corresponding placebo group. Further, sleepiness decreased less in the premenopausal EPT group than in the placebo group (p = 0.031). In postmenopausal women, EPT decreased and placebo slightly increased slow wave activity during the second non-rapid eye movement sleep episode (p = 0.046). CONCLUSIONS: In premenopausal and late postmenopausal women, EPT had only random and marginal effects on sleep. Although the limited findings were mostly unfavorable for EPT, one cannot conclude that EPT deteriorates sleep. Further, neither middle-aged cycling premenopausal women nor older postmenopausal women benefit from estrogen-progestin treatment in terms of their sleep quality.

Diffuse sleep spindles show similar frequency in central and frontopolar positions.

The objective of the present work was to examine fronto-central spindle frequency. A previously validated spindle detector, providing an electroencephalographic (EEG) amplitude independent spindle detection, was used to detect bilateral sleep spindles from sleep EEG recordings of ten healthy subjects with a time resolution of 0.33-s. A bilateral spindle detected centrally and frontopolarly simultaneously is called here a diffuse spindle. A bilateral spindle detected only frontopolarly or centrally at a given time is called a pure frontopolar and a pure central spindle, respectively. Spindle frequency was obtained with zero-padded discrete Fourier transform (DFT). Waveform phase angle of diffuse spindles was also examined. A total of 1230 diffuse spindles and 5316 pure central and 2595 pure frontopolar spindles were detected. The difference of median spindle frequency between central and frontopolar brain positions was clearly smaller in diffuse spindles than in pure spindles. Moreover, 34% of the diffuse spindles showed a similar frequency in central and frontopolar locations. This figure was up to 50.9% when including the 700 diffuse spindles fulfilling a strict anteroposterior (AP) timing criteria. The timing criteria selection in diffuse spindle analysis is a new functionality, enabled by the present spindle analysis method. Diffuse spindles showed coherent spindle oscillation in a large fronto-central area. Pure frontopolar spindles might be special cases of diffuse spindles, both of them seem to be generated in the nucleus medialis dorsalis (NMD) of the thalamus.

Detection of compressed tracheal sound patterns with large amplitude variation during sleep.

The objective of the present work was to develop automated methods for the compressed tracheal breathing sound analysis. Overnight tracheal breathing sound was recorded from ten apnoea patients. From each patient, three different types of tracheal sound deflection pattern, each of 10 min duration, were visually scored, viewing the compressed tracheal sound curve. Among them, high deflection patterns are of special interest due to the possible correlation with apnoea-hypopnoea sequences. Three methods were developed to detect patterns with high deflection, utilizing nonlinear filtering in local characterization of tracheal sounds. Method one comprises of local signal maximum, the second method of its local range, and the third of its relative range. The three methods provided 80% sensitivity with 57, 91 and 93% specificity, respectively. Method three provided an amplitude-independent approach. The nonlinear filtering based methods developed here offer effective means for analysing tracheal sounds of sleep-disordered breathing.

Electroencephalogram spindle activity during dexmedetomidine sedation and physiological sleep.

BACKGROUND: Dexmedetomidine, a selective alpha(2)-adrenoceptor agonist, induces a unique, sleep-like state of sedation. The objective of the present work was to study human electroencephalogram (EEG) sleep spindles during dexmedetomidine sedation and compare them with spindles during normal physiological sleep, to test the hypothesis that dexmedetomidine exerts its effects via normal sleep-promoting pathways. METHODS: EEG was continuously recorded from a bipolar frontopolar-laterofrontal derivation with Entropy Module (GE Healthcare) during light and deep dexmedetomidine sedation (target-controlled infusions set at 0.5 and 3.2 ng/ml) in 11 healthy subjects, and during physiological sleep in 10 healthy control subjects. Sleep spindles were visually scored and quantitatively analyzed for density, duration, amplitude (band-pass filtering) and frequency content (matching pursuit approach), and compared between the two groups. RESULTS: In visual analysis, EEG activity during dexmedetomidine sedation was similar to physiological stage 2 (S2) sleep with slight to moderate amount of slow-wave activity and abundant sleep spindle activity. In quantitative EEG analyses, sleep spindles were similar during dexmedetomidine sedation and normal sleep. No statistically significant differences were found in spindle density, amplitude or frequency content, but the spindles during dexmedetomidine sedation had longer duration (mean 1.11 s, SD 0.14 s) than spindles in normal sleep (mean 0.88 s, SD 0.14 s; P=0.0014). CONCLUSIONS: Analysis of sleep spindles shows that dexmedetomidine produces a state closely resembling physiological S2 sleep in humans, which gives further support to earlier experimental evidence for activation of normal non-rapid eye movement sleep-promoting pathways by this sedative agent.

Prolonged spiking in the Emfit sensor in patients with sleep-disordered breathing is characterized by increase in transcutaneous carbon dioxide.

A phenomenon of prolonged spiking in movement sensors, such as static-charge-sensitive bed or Emfit (electromechanical film) sensors, has been connected to an increase in carbon dioxide tension in wakefulness. Spiking is also a common finding in sleep studies. This made us hypothesize that carbon dioxide changes might also happen in sleep during prolonged spiking episodes in Emfit sheet. We examined four different kinds of breathing pattern episodes: normal breathing, episodes of repetitive apnea, episodes of repetitive hypopnea and episodes with prolonged spiking lasting at least 3 min. One hundred and fifteen episodes from 19 polysomnograms were finally admitted to the study according to the protocol. The changes in the transcutaneous carbon dioxide tension (TcCO(2)) were defined for different breathing patterns. During prolonged spiking episodes the TcCO(2) increased significantly and differed statistically from the TcCO(2) changes of normal breathing and periodic breathing patterns (episodes of apnea and hypopnea). The rise in TcCO(2) during prolonged spiking episodes might suggest that prolonged spiking is representing another type of breathing disturbance during sleep differing from periodic breathing patterns. The Emfit sensor as a small, flexible and non-invasive sensor might provide useful additional information about breathing during sleep.

Perception of sleep: subjective versus objective sleep parameters in patients with Parkinson's disease in comparison with healthy elderly controls. Sleep perception in Parkinson's disease and controls.

INTRODUCTION: Subjective sleep perception, as measured against objective parameters such as those obtained by polysomnography, have not been examined thoroughly to date. Little is known about subjective sleep perception in patients with chronic somatic diseases. PATIENTS AND METHODS: Patients with Parkinson's disease (PD) and healthy elderly controls filled in a sleep log over 14 days, which included a self-rating questionnaire concerning sleep and quality of time awake, sleep times and somatic complaints. All participants underwent polysomnography in the sleep lab on nights 7 and 8, and slept all other nights at home. RESULTS: Seventeen patients with PD (64 +/- 6 years, 6 female, Hoehn and Yahr median = 2), and 62 healthy controls of the same age without sleep disturbances (64 +/- 8 years, 36 female) were included. Patients with PD showed reduced subjective sleep (p = 0.001) and quality of time awake (p = 0.02), decreased sleep duration (p = 0.01) and reduced sleep efficiency (p = 0.004) compared with the controls. Subjective sleep efficiency at home was no different from that in the sleep lab for both groups. Patients with PD reported more somatic complaints (p = 0.001) than controls but did not show a firstnight effect. CONCLUSION: In summary, patients with PD have subjectively and objectively disturbed sleep as compared to healthy controls of the same age. However, they may not rate this poor sleep as much changed from their baseline sleep at home, and they have more somatic complaints. Increasing sleep efficiency might be of importance in PD patients, as it shows an association with subjective quality of time awake in the morning.

Interrater reliability between scorers from eight European sleep laboratories in subjects with different sleep disorders.

Interrater variability of sleep stage scorings is a well-known phenomenon. The SIESTA project offered the opportunity to analyse interrater reliability (IRR) between experienced scorers from eight European sleep laboratories within a large sample of patients with different (sleep) disorders: depression, general anxiety disorder with and without non-organic insomnia, Parkinson's disease, period limb movements in sleep and sleep apnoea. The results were based on 196 recordings from 98 patients (73 males: 52.3 +/- 12.1 years and 25 females: 49.5 +/- 11.9 years) for which two independent expert scorings from two different laboratories were available. Cohen's kappa was used to evaluate the IRR on the basis of epochs and intraclass correlation was used to analyse the agreement on quantitative sleep parameters. The overall level of agreement when five different stages were distinguished was kappa = 0.6816 (76.8%), which in terms of kappa reflects a 'substantial' agreement (Landis and Koch, 1977). For different groups of patients kappa values varied from 0.6138 (Parkinson's disease) to 0.8176 (generalized anxiety disorder). With regard to (sleep) stages, the IRR was highest for rapid eye movement (REM), followed by Wake, slow-wave sleep (SWS), non-rapid eye movement 2 (NREM2) and NREM1. The results of regression analysis showed that age and sex only had a statistically significant effect on kappa when the (sleep) stages are considered separately. For NREM2 and SWS a statistically significant decrease of IRR with age has been observed and the IRR for SWS was lower for males than for females. These variations of IRR most probably reflect changes of the sleep electroencephalography (EEG) with age and gender.

Automatic analysis of electro-encephalogram sleep spindle frequency throughout the night.

A fully automatic method to analyse electro-encephalogram (EEG) sleep spindle frequency evolution during the night was developed and tested. Twenty all-night recordings were studied from ten healthy control subjects and ten sleep apnoea patients. A total of 22,868 spindles were detected. The overall mean spindle frequency was significantly higher in the control subjects than in the apnoea patients (12.5 Hz against 11.7 Hz, respectively; p<0.004). The proposed method further identified the sleep depth cycles, and the mean spindle frequency was automatically determined inside each sleep depth cycle. In control subjects, the mean spindle frequency increased from 12.0 Hz in the first sleep depth cycle to 12.6 Hz in the fifth cycle. No such increase was observed in the sleep apnoea patients. This difference in the spindle frequency evolution was statistically significant (p<0.004). The advantage of the method is that no EEG amplitude thresholds are needed.

Neuromuscular and sensory disturbances in patients with well treated coeliac disease.

OBJECTIVES: A body of evidence shows that coeliac disease is associated with protean manifestations outside the intestine, and neurological disorders are well recognised. However, it remains obscure whether there are signs of clinical or subclinical nervous system involvement even in patients adopting an adequate gluten free diet. The aim of this study was to assess in a controlled study whether patients with treated coeliac disease carry an increased risk for neuropathy and characterise the type of possible neuropathy. METHODS: Electroneuromyographic findings and vibration, thermal, and tactile thresholds of 26 patients with coeliac disease and 23 patients with reflux disease were analysed. RESULTS: Six (23.1 %) coeliac disease patients and one (4.3 %) reflux disease patient showed findings of chronic axonal neuropathy in quantitative needle EMG. In addition, two coeliac disease patients showed findings suggestive for myopathy. There were no significant differences in warm, cold, or vibration thresholds between the groups but means of heat pain thresholds and tactile thresholds were significantly higher in coeliac patients than in controls. CONCLUSION: An increased occurrence of axonal neuropathy was observed in well treated coeliac disease. This further indicates that neurological manifestations occur even in patients without overt malabsorption.

Autoassociative MLP in sleep spindle detection.

Spindles are one of the most important short-lasting waveforms in sleep EEG. They are the hallmarks of the so-called Stage 2 sleep. Visual spindle scoring is a tedious workload, since there are often a thousand spindles in one all-night recording of some 8 hr. Automated methods for spindle detection typically use some form of fixed spindle amplitude threshold, which is poor with respect to inter-subject variability. In this work a spindle detection system allowing spindle detection without an amplitude threshold was developed. This system can be used for automatic decision making of whether or not a sleep spindle is present in the EEG at a certain point of time. An Autoassociative Multilayer Perceptron (A-MLP) network was employed for the decision making. A novel training procedure was developed to remove inconsistencies from the training data, which was found to improve the system performance significantly.

Optimization of sigma amplitude threshold in sleep spindle detection.

Sleep spindles are transient EEG waveforms of non-rapid eye movement sleep. There is considerable intersubject variability in spindle amplitudes. The problem in automatic spindle detection has been that, despite this fact, a fixed amplitude threshold has been used. Selection of the spindle detection threshold value is critical with respect to the sensitivity of spindle detection. In this study a method was developed to estimate the optimal recording-specific threshold value for each all-night recording without any visual scorings. The performance of the proposed method was validated using four test recordings each having a very different number of visually scored spindles. The optimal threshold values for the test recordings could be estimated well. The presented method seems very promising in providing information about sleep spindle amplitudes of individual all-night recordings.