Evaluation of the different sleep-disordered breathing patterns of the compressed tracheal sound.

OBJECTIVE: Suitability of the compressed tracheal sound signal for screening different sleep-disordered breathing patterns was evaluated. The previous results suggest that the plain pattern in the compressed sound signal represents mostly normal, unobstructed breathing, the thick pattern consists of periodic apneas/hypopneas and during the thin pattern, flow limitation in the nasal cannula signal is abundant. METHODS: Twenty-seven patients underwent a polysomnography with a tracheal sound and oesophageal pressure monitoring. The tracheal sound data was compressed and scored visually into three different breathing patterns. The percentage of oesophageal pressure values under -8cm H2O, the minimum pressure value and the average duration of the breathing cycles were extracted from 10-min episodes of those plain, thick and thin patterns. In addition, the spectral contents of the tracheal sound during the different breathing patterns were evaluated. RESULTS: The percentage of time when the oesophageal pressure negativity increased was highest during the thin pattern and lowest during the plain pattern. In addition, the thin pattern presented most high frequency components in the 1001-2000Hz frequency band of the tracheal sound. CONCLUSIONS: The results confirmed our previous findings that both the thick and thin patterns seem to consist of obstructed breathing, whereas during the plain pattern the breathing is normal, unobstructed. SIGNIFICANCE: Most screening methods for sleep-disordered breathing reveal only periodic apneas/hypopneas, but with the compressed sound signal the sustained partial obstruction can be estimated as well.

Sleep stage classification with low complexity and low bit rate.

Standard sleep stage classification is based on visual analysis of central (usually also frontal and occipital) EEG, two-channel EOG, and submental EMG signals. The process is complex, using multiple electrodes, and is usually based on relatively high (200-500 Hz) sampling rates. Also at least 12 bit analog to digital conversion is recommended (with 16 bit storage) resulting in total bit rate of at least 12.8 kbit/s. This is not a problem for in-house laboratory sleep studies, but in the case of online wireless self-applicable ambulatory sleep studies, lower complexity and lower bit rates are preferred. In this study we further developed earlier single channel facial EMG/EOG/EEG-based automatic sleep stage classification. An algorithm with a simple decision tree separated 30 s epochs into wakefulness, SREM, S1/S2 and SWS using 18-45 Hz beta power and 0.5-6 Hz amplitude. Improvements included low complexity recursive digital filtering. We also evaluated the effects of a reduced sampling rate, reduced number of quantization steps and reduced dynamic range on the sleep data of 132 training and 131 testing subjects. With the studied algorithm, it was possible to reduce the sampling rate to 50 Hz (having a low pass filter at 90 Hz), and the dynamic range to 244 microV, with an 8 bit resolution resulting in a bit rate of 0.4 kbit/s. Facial electrodes and a low bit rate enables the use of smaller devices for sleep stage classification in home environments.

Compressed tracheal sound analysis in screening of sleep-disordered breathing.

OBJECTIVE: To evaluate the suitability of compressed tracheal sound signal for screening sleep-disordered breathing. METHODS: Thirty-three consecutive patients underwent a polysomnography with a tracheal sound analysis. Nineteen patients were healthy except for the sleep complaint, 9 were hypertonic and 3 were hypertonic and had elevated cholesterol. Minimum and maximum values of each consecutive, non-overlapping segment of 15s of original sound data were extracted. All these compressed tracheal sound traces were divided into plain, thin and thick signal periods. Also pure, 10-min episodes of plain, thin and thick tracheal sound periods were selected and the nasal pressure flow shapes during these pure sound periods were examined. RESULTS: There was a significant positive correlation between the total nocturnal amount of thick periods and AHI. Apneas and hypopneas were most common during the 10-min episodes of thick sound periods. The proportion of round (normal, non-flattened) inspiratory flow shape was highest during the pure plain periods. CONCLUSIONS: Breathing consisting of apneas and hypopneas can quite reliably be visualised with compressed tracheal sound analysis. The other interesting outcome of the study is that even prolonged flow limitation might be revealed with the method. SIGNIFICANCE: Compressed tracheal sound analysis might provide a promising screening method for obstructive apneas and hypopneas.

Automatic sleep detection using activity and facial electrodes.

The most commonly applied unobtrusive sleep monitoring method is actigraphy, the measurement of body limb movements. In spite of its wide clinical acceptance, actigraphy has a low specificity for sleep detection leaving room for novel approaches of unobtrusive sleep monitoring. The present study compared sleep detection by a novel single channel electro-oculography (EOG) method and three activity monitors, with the golden standard of polysomnographic sleep analysis as a reference. With standard actigraphy (Actiwatch placed at the left wrist) sleep detection specificity and sensitivity were 42% and 95%. With the Alive Monitor attached on the same wrist, activity-based sleep detection specificity and sensitivity were 40% and 97%. With another Alive Monitor placed over the sternum sleep detection specificity and sensitivity were 21% and 99%. With two self-applied EOG electrodes combined with automatic sleep detection analysis, specificity and sensitivity were 72% and 96%. The results confirm low specificity of actigraphic sleep estimates, and demonstrate that the novel single-channel EOG method provides a substantial improvement in specificity.

Automatic sleep stage classification using two facial electrodes.

Standard sleep stage classification is based on visual analysis of central EEG, EOG and EMG signals. Automatic analysis with a reduced number of sensors has been studied as an easy alternative to the standard. In this study, a single-channel electro-oculography (EOG) algorithm was developed for separation of wakefulness, SREM, light sleep (S1, S2) and slow wave sleep (S3, S4). The algorithm was developed and tested with 296 subjects. Additional validation was performed on 16 subjects using a low weight single-channel Alive Monitor. In the validation study, subjects attached the disposable EOG electrodes themselves at home. In separating the four stages total agreement (and Cohen's Kappa) in the training data set was 74% (0.59), in the testing data set 73% (0.59) and in the validation data set 74% (0.59). Self-applicable electro-oculography with only two facial electrodes was found to provide reasonable sleep stage information.

Reducing the effects of electrocardiographic artifacts on electro-oculography in automatic sleep analysis.

Removal of electrocardiographic (ECG) artifacts of QRS complexes from a single channel electroencephalography (EEG) and electro-oculography (EOG) can be problematic especially when no reference ECG signal is available. This study examined a simple estimation method excluding the possible QRS part of the EOG trace before spectrum estimation. The method was tested using a simple sleep classifier based on 0.5-30 Hz mean frequency of single channel sleep EOG, with the left EOG electrode referenced to the left mastoid (EOG L-M1). When QRS peaks were automatically excluded from the least square (LS) mean frequency estimation the average optimal mean frequency threshold decreased from 9.3 Hz to 8.8 Hz and agreement and Cohen's Kappa increased respectively from 89% to 90% and from 0.44 to 0.50 when compared to the traditional spectral estimation.

Automatic sleep stage classification using two-channel electro-oculography.

An automatic method for the classification of wakefulness and sleep stages SREM, S1, S2 and SWS was developed based on our two previous studies. The method is based on a two-channel electro-oculography (EOG) referenced to the left mastoid (M1). Synchronous electroencephalographic (EEG) activity in S2 and SWS was detected by calculating cross-correlation and peak-to-peak amplitude difference in the 0.5-6 Hz band between the two EOG channels. An automatic slow eye-movement (SEM) estimation was used to indicate wakefulness, SREM and S1. Beta power 18-30 Hz and alpha power 8-12 Hz was also used for wakefulness detection. Synchronous 1.5-6 Hz EEG activity and absence of large eye movements was used for S1 separation from SREM. Simple smoothing rules were also applied. Sleep EEG, EOG and EMG were recorded from 265 subjects. The system was tuned using data from 132 training subjects and then applied to data from 131 validation subjects that were different to the training subjects. Cohen's Kappa between the visual and the developed new automatic scoring in separating 30s wakefulness, SREM, S1, S2 and SWS epochs was substantial 0.62 with epoch by epoch agreement of 72%. With automatic subject specific alpha thresholds for offline applications results improved to 0.63 and 73%. The automatic method can be further developed and applied for ambulatory sleep recordings by using only four disposable, self-adhesive and self-applicable electrodes.

Development and comparison of four sleep spindle detection methods.

OBJECTIVE: The objective of the present work was to develop and compare methods for automatic detection of bilateral sleep spindles. METHODS AND MATERIALS: All-night sleep electroencephalographic (EEG) recordings of 12 healthy subjects with a median age of 40 years were studied. The data contained 6043 visually scored bilateral spindles occurring in frontopolar or central brain location. In the present work a new sigma index for spindle detection was developed, based on the fast Fourier transform (FFT) spectrum, aiming at approximating our previous fuzzy spindle detector. The sigma index was complemented with spindle amplitude analysis, based on finite impulse response (FIR) filtering, to form of a combination detector of bilateral spindles. In this combination detector, the spindle amplitude distribution of each recording was estimated and used to tune two different amplitude thresholds. This combination detector was compared to bilaterally extracted sigma indexes and fuzzy detections, which aim to be independent of absolute spindle amplitudes. As a fourth method a fixed spindle amplitude detector was included. RESULTS: The combination detector provided the best overall performance; in S2 sleep a 70% true positive rate was reached with a specificity of 98.6%, and a false-positive rate of 32%. The bilateral sigma indexes provided the second best results, followed by fuzzy detector, while the fixed amplitude detector provided the poorest results so that in S2 sleep a 70% true positive rate was reached with a specificity of 97.7% and false-positive rate of 46%. The spindle amplitude distributions automatically determined for each recording by the combination detector were compared to amplitudes of visually scored spindles and they proved to correspond well. Inter-hemispheric amplitude variation of visually scored bilateral spindles is also presented. CONCLUSION: Flexibility is beneficial in the detection of bilateral spindles. The present work advances automated spindle detection and increases the knowledge of bilateral sleep spindle characteristics.

The use of two-channel electro-oculography in automatic detection of unintentional sleep onset.

An automatic method was developed for detecting unintentional sleep onset. The automatic method is based on a two-channel electro-oculography (EOG) with left mastoid (M1) as reference. An automatic estimation of slow eye movements (SEM) was developed and used as the main criterion to separate sleep stage 1 (S1) from wakefulness. Additionally synchronous electroencephalographic (EEG) activity of sleep stages 1 and 2 was detected by calculating cross-correlation and amplitude difference in the 1.5-6 Hz theta band between the two EOG channels. Alpha power 8-12 Hz and beta power 18-30 Hz were used to determine wakefulness. Unintentional sleep onsets were studied using data from four separate maintenance of wakefulness test (MWT) sessions of 228 subjects. The automatic scoring of 30s sleep onset epochs using only EOG was compared to standard visual sleep stage scoring. The optimal detection thresholds were derived using data from 114 subjects and then applied to the data from different 114 subjects. Cohen's Kappa between the visual and the new automatic scoring system in separating wakefulness and sleep was substantial (0.67) with epoch by epoch agreement of 98%. The sleep epoch detection sensitivity was 70% and specificity 99%. The results are provided with a 1s delay for each 30s epoch. The developed method has to be tested in field applications. The advantage of the automatic method is that it could be applied during online recordings using only four disposable self-adhesive self-applicable electrodes.

Automatic detection of slow wave sleep using two channel electro-oculography.

An automatic method was developed for detecting slow wave sleep (SWS). The automatic method is based on a two-channel electro-oculography (EOG) with left mastoid (M1) as reference. Synchronous electroencephalographic (EEG) activity was detected by calculating cross-correlation between the two EOG channels by using 0.5-6 Hz band. An amplitude criterion was used for detecting slow waves and beta power 18-30 Hz was used to exclude artefacts. The automatic scoring was compared to a standard visual sleep scoring based on EOG, central EEG and submental EMG. Sleep EEG and EOG were recorded from 265 subjects. The optimal cross-correlation, amplitude and beta thresholds were derived using data from 133 training subjects and then applied to the data from different 132 validation subjects. Results were most sensitive to the changes in the amplitude criteria. Cohen's Kappa between the visual and the new developed automatic scoring in separating non-SWS and SWS was substantial (0.70) with epoch-by-epoch agreement of 93%. SWS epoch detection sensitivity was 75% and specificity was 96%. Also the total amount of slow waves, slow wave time (SWT), was estimated. The advantage of the automatic method is that it could be applied during online recordings using only four disposable self-adhesive electrodes.

Systematic performance evaluation of a continuous-scale sleep depth measure.

In this article, systematic performance evaluation of a continuous-scale sleep depth measure will be discussed. Our main objective has been to select the adjustable analysis parameters such that the best possible correspondence between method output and standard visual sleep staging could be achieved. Sleep depth estimation was based on continuous monitoring of short-time EEG synchronization through the local mean frequency of the EEG. During the experiments, total amount of 752 different combinations of four adjustable parameters were compared based on all-night sleep EEG recordings of 15 healthy subjects. Optimization strategy applied was based on maximizing the weighted average of pair-wise separabilities of EEG mean frequency distributions in all the standard sleep stage pairs. Finally, robustness of the optimized parameters was verified with an independent dataset of 34 all-night sleep recordings. Our results show that clear topological differences between brain hemispheres and different electrode locations exist. Performance improvements of even 20-30% units can be achieved by proper selection of analysis parameters and the EEG derivation used for the analysis. Remarkable independence of system performance on the analysis window length leads to improved temporal resolution compared to that achieved through standard visual analysis. In addition to giving practical suggestions on the parameter selection, we also propose a possible method for improving stage separability especially between S2 and REM.

Apnea patients show a frontopolar inter-hemispheric spindle frequency difference.

Sleep apnea syndrome is known to disturb sleep. The purpose of the present work was to study spindle frequency in apnea patients. All-night sleep EEG recordings of 15 apnea patients and 15 control subjects with median ages of 47 and 46 years, respectively, were studied. A previously presented and validated multi-channel spindle analysis method was applied for automatic detection and frequency analysis of bilateral frontopolar and central spindles. Bilateral frontopolar spindles of apnea patients were found to show lower frequencies on the left hemisphere than on the right. Such an inter-hemispheric spindle frequency difference in apnea patients is a novel finding. It could be that the hypoxias and hypercapnias caused by apneic episodes result in local disruption in the regulation of sleep in the frontal lobes.

Topographic differences in mean computational sleep depth between healthy controls and obstructive sleep apnoea patients.

In this work, topographic differences in computational sleep depth between healthy controls and obstructive sleep apnoea syndrome (OSAS) patients have been examined. Sleep depth estimation was based on continuous monitoring of the mean frequency of the EEG. During the experiments, all-night sleep EEG recordings of carefully age and gender matched sets of 16 healthy controls and 16 OSAS patients were compared on six electrode locations (Fp1-M2, Fp2-M1, C3-M2, C4-M1, O1-M2, and O2-M1). To optimise the diagnostic ability of the method, we examined the influence of 45 sets of adjustable analysis parameters on the ability of the method to show differences in computational sleep depth between the diagnostic groups. The results show clearly that although the visual scores for a set of epochs are the same for both clinical groups, computational sleep depth measure still shows deeper local sleep for healthy controls, both during NREM and REM sleep. Although the best achievable performance in different sleep stages is reached in different EEG derivations and with different parameter values, computation of sleep depth with 1-s output resolution in non-overlapping segments of 2s (400 samples) with maximum analysis band frequency of 20.5 Hz and 51-point moving median smoothing on Fp2-M1 or O1-M2 leads to near-optimal performance in deep sleep or wakefulness/light sleep, respectively.

Computer program for automated sleep depth estimation.

In this article, we present a new implementation of an amplitude-independent method for continuous-scale sleep depth estimation. Having been implemented as an add-on analysis module under commercially available biosignal recording and analysis software, it can be easily applied in clinical routine. The software gives the user full freedom to change all the analysis parameters inside theoretical limits. Computational sleep depth profiles produced by the presented software compare favourably with visual classifications. Future work will concentrate on systematic optimization of analysis parameters, further evaluation of the method with disturbed sleep and application of the method for automated adaptive sleep analysis.

Determination of dominant simulated spindle frequency with different methods.

Accurate analysis of EEG sleep spindle frequency is challenging. The frequency content of true sleep spindles is not known. Therefore, simulated spindle activity was studied in the present work. Five types of simulated test signals were designed, all containing a dominant spindle represented by a 13-Hz sine wave as such or with a waxing and waning pattern accompanied by a secondary spindle activity in three test signals. Background EEG was included in four test signals, modeled either as small additional sinusoids across the spindle frequency range or as filtered Gaussian noise segments. The purpose of this study was to investigate how accurately the dominant spindle frequency of 13 Hz could be resolved with different methods in the presence of the interfering waveforms. A matching pursuit (MP) based approach, discrete Fourier transform (DFT) with Hanning windowing with and without zero padding, Hankel total least squares (HTLS) and wavelet methods were compared in the analyses. MP method provided best overall performance, followed closely by DFT with zero padding. Comparative studies like this are important to decide the method of choice in clinical sleep EEG analysis.

Automatic detection of spiking events in EMFi sheet during sleep.

In this paper we present a new method for detection of spiking events caused by the increased respiratory resistance (IRR) from ballistocardiographic (BCG) data recorded with EMFi sheet. Spiking is a phenomenon where BCG wave complexes increase in amplitude during IRR. In this study data from six patients with a total of 1503 visually scored spiking events were studied. The algorithm monitors amplitude levels of BCG complexes and detects large relative increases. In this work 10 different variations of the algorithm were compared in order to find the best variation, which can cope with different recordings. The best variation of the algorithm was able to detect spiking events with 80% true positive and 19% false positive rates. The detection is not dependent on absolute waveform amplitudes and therefore does not require any recording-specific tuning prior to application. It is important to recognize spiking events in order to evaluate the severity of respiratory disturbance during sleep.

Anteroposterior difference in EEG sleep depth measure is reduced in apnea patients.

In the present work, mean frequencies of FFT amplitude spectra from six EEG derivations were used to provide a frontopolar, a central and an occipital sleep depth measure. Parameters quantifying the anteroposterior differences in these three sleep depth measures during the night were also developed. The method was applied to analysis of 30 all-night recordings from 15 healthy control subjects and 15 apnea patients. Control subjects showed larger differences in sleep depth between frontopolar and central positions than the apnea patients. The relatively reduced frontal sleep depth in apnea patients might reflect the disruption of the dynamic sleep process caused by apneas.

Automated frequency analysis of synchronous and diffuse sleep spindles.

BACKGROUND: Sleep spindles have different properties in different localizations in the cortex. OBJECTIVES: First main objective was to develop an amplitude-independent multi-channel spindle detection method. Secondly the method was applied to study the anteroposterior frequency differences of pure synchronous (visible bilaterally, either frontopolarly or centrally) and diffuse (visible bilaterally both frontopolarly and centrally) sleep spindles. METHODS: A previously presented spindle detector based on the fuzzy reasoning principle and a level detector were combined to form a multi-channel spindle detector. RESULTS: The spindle detector had a 76.17% true positive rate and 0.93% false-positive rate. Pure central spindles were faster and pure frontal spindles were slower than diffuse spindles measured simultaneously from both locations. CONCLUSIONS: The study of frequency relations of spindles might give new information about thalamocortical sleep spindle generating mechanisms.