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.

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.

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.

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.

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.

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.

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.

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.

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.

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.