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Biomedical subjects

Sari-Leena Himanen

Publications and source records attributed to Sari-Leena Himanen.

At least 19 recordsLinked to original sources
PubMed2006

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.

Adult↗
PubMed2006

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.

Adult↗
PubMed2006

Sleep deprivation and hormone therapy in postmenopausal women.

BACKGROUND AND PURPOSE: Sleep complaints increase after menopause, but literature on the effect of postmenopausal hormone therapy (HT) on sleep is controversial. The purpose of this study was to determine the effect of ageing and HT on sleep quality, assessed using polysomnography, and on the accuracy of the subjective estimation of sleep quality in women before and after sleep deprivation. PATIENTS AND METHODS: Twenty postmenopausal women (aged 58-72 years) were recruited: 10 HT-users and 10 non-HT-users. Eleven young women (aged 20-26 years) served as controls. Polysomnography and subjective sleep quality were measured on four consecutive nights: adaptation, baseline, 40-h sleep deprivation and recovery. RESULTS: Although the postmenopausal women slept worse than the controls at baseline, and in particular during the recovery night, their recovery response to sleep deprivation was well preserved. At baseline, HT-users had a shorter latency to rapid eye movement (REM) (P=0.043), with fewer awakenings from slow wave sleep (SWS) (P=0.029) but more from REM (P=0.033) than non-HT-users. During recovery, the HT-users had more stage 2 sleep (P=0.048) and less slow wave activity (SWA) in the first non-rapid eye movement (NREM) sleep episode (P=0.021) than the non-HT-users. The poor correlation between subjective and objective sleep quality at baseline became significant during recovery. CONCLUSIONS: Although sleep in postmenopausal women was worse than in young controls, the recovery response following sleep deprivation was relatively well preserved. HT offered no significant advantage to sleep at baseline and slightly weakened the recovery response to prolonged wakefulness.

Adult↗
PubMed2006

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.

Adult↗
PubMed2006

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.

Adult↗
PubMed2006

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.

Electronic Data Processing↗
PubMed2006

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.

Algorithms↗
PubMed2005

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.

Algorithms↗
PubMed2005

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.

Adult↗
PubMed2005

An E-health solution for automatic sleep classification according to Rechtschaffen and Kales: validation study of the Somnolyzer 24 x 7 utilizing the Siesta database.

To date, the only standard for the classification of sleep-EEG recordings that has found worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even though several attempts have been made to automate the classification process, so far no method has been published that has proven its validity in a study including a sufficiently large number of controls and patients of all adult age ranges. The present paper describes the development and optimization of an automatic classification system that is based on one central EEG channel, two EOG channels and one chin EMG channel. It adheres to the decision rules for visual scoring as closely as possible and includes a structured quality control procedure by a human expert. The final system (Somnolyzer 24 x 7) consists of a raw data quality check, a feature extraction algorithm (density and intensity of sleep/wake-related patterns such as sleep spindles, delta waves, SEMs and REMs), a feature matrix plausibility check, a classifier designed as an expert system, a rule-based smoothing procedure for the start and the end of stages REM, and finally a statistical comparison to age- and sex-matched normal healthy controls (Siesta Spot Report). The expert system considers different prior probabilities of stage changes depending on the preceding sleep stage, the occurrence of a movement arousal and the position of the epoch within the NREM/REM sleep cycles. Moreover, results obtained with and without using the chin EMG signal are combined. The Siesta polysomnographic database (590 recordings in both normal healthy subjects aged 20-95 years and patients suffering from organic or nonorganic sleep disorders) was split into two halves, which were randomly assigned to a training and a validation set, respectively. The final validation revealed an overall epoch-by-epoch agreement of 80% (Cohen's kappa: 0.72) between the Somnolyzer 24 x 7 and the human expert scoring, as compared with an inter-rater reliability of 77% (Cohen's kappa: 0.68) between two human experts scoring the same dataset. Two Somnolyzer 24 x 7 analyses (including a structured quality control by two human experts) revealed an inter-rater reliability close to 1 (Cohen's kappa: 0.991), which confirmed that the variability induced by the quality control procedure, whereby approximately 1% of the epochs (in 9.5% of the recordings) are changed, can definitely be neglected. Thus, the validation study proved the high reliability and validity of the Somnolyzer 24 x 7 and demonstrated its applicability in clinical routine and sleep studies.

Adult↗
PubMed2005

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.

Adult↗
PubMed2004

Automatic quantification of light sleep shows differences between apnea patients and healthy subjects.

A fully automatic method to quantify sleep depth during the night was developed in the present work. The method was tested using 20 all-night recordings from 10 healthy control subjects and 10 sleep apnea patients. The results showed statistically significant differences in sleep depth between control subjects and sleep apnea patients. The overall sleep was lighter in apnea patients than in healthy control subjects, most likely indicating a disturbed sleep caused by apneas. The automatic parameters presented provide a method to quantify the light sleep and could in the future possibly be used in clinical sleep studies and follow-up of treatment.

Adult↗
PubMed2004

Visual assessment of selected high amplitude frontopolar slow waves of sleep: differences between healthy subjects and apnea patients.

Slow wave sequences with individually defined amplitude criterion were selected manually from frontopolar sleep EEG of eight healthy control subjects and eight patients with sleep apnea syndrome. Healthy subjects had clearly more time occupied by slow wave sequences in all night sleep. Closer examination revealed that the difference in the amount of slow wave sequence time between the groups was statistically significant only in the first NREM sleep episode. In other words, healthy subjects had more time with slow wave sequences in the first NREM sleep episode, where sleep pressure is supposed to be highest. The lower amount of slow wave sequences in the apnea patients might reflect the fragmented sleep of the patients, inhibiting cortical synchronization.

Adult↗
PubMed2003

Spindle frequency remains slow in sleep apnea patients throughout the night.

BACKGROUND: Our previous data suggested that in normal sleep the frequency of individual sleep spindles would be related to sleep depth and possibly to sleep pressure. Thus far spindle frequency patterns in patients with sleep disorders have not been studied. It would be expected that the spindle frequencies might be affected by sleep fragmentation disturbing the sleep process. METHODS: Twelve apnea patients with age- and sex-matched control subjects were studied with whole-night sleep recordings. Sleep spindles were visually selected and their frequency was determined by spectral analysis. RESULTS: Sleep spindles of the patients were in general slower than in the control subjects. As in our previous study, the frequency of the spindles in the middle-part of the non-rapid eye movement (NREM) sleep episodes increased towards the end of the night in the control group, whereas in the patient group no such increase was found. CONCLUSIONS: The slow spindle frequencies in apnea patients could indicate disturbed sleep and altered neural mechanisms in the structures regulating sleep spindle activity.

Adult↗
PubMed2003

Sleep depth oscillations: an aspect to consider in automatic sleep analysis.

The automatic sleep analysis aims at providing an accurate description of sleep process. We found that there exist so far poorly known sleep depth oscillations constantly. Quantitative analysis of these oscillations was done in this work via a mean frequency measure and FFT. Overall charasteristics of these oscillations were studied, focusing on the waves with period times of 5-150 s. These sleep depth oscillations have a relatively large amplitude and they should be considered in future sleep analysis systems. The results of this study give directions to automated sleep analysis regarding optimal estimation of sleep depth.

Adult↗
PubMed2003

Whole-body impedance recording--a practical method for the diagnosis of sleep apnoea.

The aim of this study was to evaluate the utility of whole-body impedance cardiography (ICGWB) in sleep studies, particularly in sleep apnoea detection. A comparison between simultaneous whole night ICGWB and standard polysomnographic recordings were made in 14 patients with a clinical suspicion of obstructive sleep apnoea, a mean age of 46 years (range 30-63 years) and a mean BMI of 29 kg m-2 (25-47). Obstructive apnoeas, central apnoeas and hypopnoeas all caused characteristic patterns in the ICGWB tracing. For an apnoea-hypopnoea index (AHI) > 15 events h-1, the sensitivity of ICGWB was 89% and the specificity 80%. In conclusion, ICGWB signal includes valuable physiological information that can be effectively used for the detection of sleep apnoea episodes. The method seems promising in cases where the multichannel polysomnography is not applicable or when ICGWB is used for haemodynamic monitoring in seriously ill and postoperative patients.

Adult↗
PubMed2003

Occurrence of periodic sleep spindles within and across non-REM sleep episodes.

Sleep spindles have been reported to occur both as single events and periodically in sequences. However, there is no systematic description about the occurrence of spindles in sequences in relation to time of night. The aim of the present study was to examine the temporal occurrence of periodic sleep spindles during the night. Sleep spindles of 19 healthy subjects were selected visually. A minimum of three consecutive spindles was required to form a spindle sequence. A 5-second upper time interval limit was applied as the longest duration between spindles belonging to a spindle sequence. The number of spindles and time occupied by spindle sequences increased from the first to the fourth non-REM (NREM) sleep episode. Within NREM sleep episodes, the number of spindles and spindle sequences dominated at the beginning. In the first two NREM sleep episodes with high slow-wave activity (SWA), there were few spindle sequences and they decreased with increasing SWA. In the third and fourth NREM sleep episode with less SWA, there were more spindle sequences and they were more evenly distributed. It is possible that in the first NREM sleep episodes, hyperpolarization of the thalamocortical cells deepens so rapidly that the NREM sleep level, where spindle sequences arise, is passed and spindle sequences are not formed. Spindle sequences could be regarded as markers of the evolution of the NREM sleep process and their lack or excess in relation to time of night and NREM sleep episode can hopefully be used to indicate changes in brain mechanisms behind NREM sleep.

Adult↗