Season is related to the slow wave and sigma activity of infants and toddlers.

OBJECTIVE/BACKGROUND: Slow wave activity (SWA) and sigma frequency activity (SFA) are hallmarks of NREM sleep EEG and important indicators of neural plasticity, development of the central nervous system, and cognition. However, little is known about the factors that modulate these sleep EEG activities, especially in small children. PATIENTS/METHODS: We analyzed the power spectral densities of SWA (1-4 Hz) and SFA range (10-15 Hz) from six EEG derivations of 56 infants (8 months) and 60 toddlers (24 months) during their all-night sleep and during the first and the last half of night sleep. The spectral values were compared between the four seasons. RESULTS: In the spring group of infants, compared with the darker seasons, SFA was lower in the centro-occipital EEG derivations during both halves of the night. The SWA findings of the infants were restricted to the last half of the night (SWA2) and frontally, where SWA2 was higher during winter than spring. The toddlers presented less frontal SWA2 during winter compared with autumn. Both age groups showed a reduction in both SWA and SFA towards the last half of the night. CONCLUSIONS: The sleep EEG spectral power densities are more often associated with seasons in infants' SFA range. The results might stem from seasonally changing light exposure, but the exact mechanism warrants further study. Moreover, contrary to the adult-like increment of SFA, the SFA at both ages was lower at the last part of the night sleep. This suggests different regulation of spindle activity in infants and toddlers.

Attitudes towards genetic testing and information: does parenthood shape the views?

This study examines how parents of pediatric patients might differ in their views and attitudes towards genetic technology and information when compared to adult patients. There is surprisingly little evidence on how parents compare to other parts of population in their attitudes. Previous empirical studies often relate health-related preferences and attitudes to factors such as age, education, and income instead of parental status, thus evading comparison of parents to others as health-related decision makers. Findings related to the parental status can be useful when implementing genetic technology in clinical practice. We conducted a survey of views on genetic technology and information for groups of adult neurology patients (n = 68) and parents of pediatric neurology patients (n = 31) to shed some light on this issue. In addition to our own survey instrument, we conducted other surveys to gain insight on psychosocial factors that might affect these attitudes. The results suggest that parents are more concerned about their children's genetic risk factors when compared to the attitudes of adult patients about their own risk. For both groups, negative emotional state was associated with more concerns towards genetic information. Our study provides insights on how parental views might affect the acceptance of genetic technology and information.

The Correlation between Intraoperative Microelectrode Recording and 3-Tesla MRI in Patients Undergoing ANT-DBS for Refractory Epilepsy.

BACKGROUND: Deep brain stimulation (DBS) of the anterior nucleus of the thalamus) (ANT) has been suggested as a treatment option in refractory epilepsy. The targeting of ANT is especially challenging due to its poor visualization in commonly used MRI sequences, lack of easily observable symptom relief during surgery and high degree of anatomical variation between individuals. OBJECTIVES: To study whether intraoperative microelectrode recording (MER), a method widely used in movement disorder surgery, provides clinically relevant information during the ANT-DBS implantation procedure. METHODS: A total of 186 MER samples from 5 patients and 10 thalami obtained from ANT-DBS surgery for refractory epilepsy were analyzed with respect to the signal characteristics and location in 3-tesla (3T) MRI STIR (short T1 inversion recovery) images. The location of each MER sample was calculated relative to visible borders of the ANT after correction of the sample locations according to the position of the final DBS electrode in postoperative CT-MRI fusion images. RESULTS: We found that the lateral aspect of the ANT lacked spiking activity consistent with the presence of white matter. The spike frequency in samples correlating with location at the ANT showed significantly lower spike frequency compared to samples correlating with location at the ventral anterior nucleus (median 3.0 and 7.0 spikes/2 s; p < 0.05), but spike bursts were morphologically similar in appearance. Trajectories entering the dorsomedial nucleus according to 3T MRI STIR images showed a yet different firing pattern with more low-amplitude regular activity. CONCLUSIONS: Our data suggest that MER provides clinically relevant information during implantation surgery by demonstrating both nucleus-specific neuronal firing patterns and white matter laminae between different nuclear groups.

Measurement of characteristic prompt gamma rays emitted from oxygen and carbon in tissue-equivalent samples during proton beam irradiation.

The purpose of this work was to characterize how prompt gamma (PG) emission from tissue changes as a function of carbon and oxygen concentration, and to assess the feasibility of determining elemental concentration in tissues irradiated with proton beams. For this study, four tissue-equivalent water-sucrose samples with differing densities and concentrations of carbon, hydrogen, and oxygen were irradiated with a 48 MeV proton pencil beam. The PG spectrum emitted from each sample was measured using a high-purity germanium detector, and the absolute detection efficiency of the detector, average beam current, and delivered dose distribution were also measured. Changes to the total PG emission from (12)C (4.44 MeV) and (16)O (6.13 MeV) per incident proton and per Gray of absorbed dose were characterized as a function of carbon and oxygen concentration in the sample. The intensity of the 4.44 MeV PG emission per incident proton was found to be nearly constant for all samples regardless of their carbon concentration. However, we found that the 6.13 MeV PG emission increased linearly with the total amount (in grams) of oxygen irradiated in the sample. From the measured PG data, we determined that 1.64 × 10(7) oxygen PGs were emitted per gram of oxygen irradiated per Gray of absorbed dose delivered with a 48 MeV proton beam. These results indicate that the 6.13 MeV PG emission from (16)O is proportional to the concentration of oxygen in tissue irradiated with proton beams, showing that it is possible to determine the concentration of oxygen within tissues irradiated with proton beams by measuring (16)O PG emission.

Identification of deep sleep and awake with computational EEG measures.

The objective of the present work was to examine identification of deep sleep and awake with computational analysis of sleep EEG traces from central brain regions. All-night EEG traces from a total of 56 male subjects, 22 healthy control subjects and 34 age-matched apnea patients, were examined. A spectral mean frequency measure, a Hilbert transform based EEG amplitude and a correlation coefficient method were compared. The EEG amplitude provided a good identification of deep sleep, reaching 86.25% but was relatively poor in the identification of wakefulness, reaching 39.06%. Mean frequency provided a relatively good identification of deep sleep and awake, reaching 84.66% and 77.67%, respectively, while the correlation coefficient produced the lowest results of 37.89% and 44.43%. Optimal threshold values for deep sleep and awake identification were determined as 4.20 and 9.76 Hz, respectively, for the mean frequency measure. Mean frequency measure can be used to provide overall context information about sleep depth for automated sleep EEG analysis methods.

Increased respiratory effort during sleep is non-invasively detected with movement sensor.

INTRODUCTION: Measuring breathing effort during sleep with an oesophageal pressure sensor remains technically challenging and has not become routine practice. The aim of the present work was to investigate whether increased thoracic pressure during sleep can be detected with the Emfit movement sensor. Experimental data suggest that increased respiratory efforts with the intrathoracic pressure variation induce high-frequency spikes in the Emfit signal, but this has not been systematically examined. METHODS: Polysomnography, oesophageal pressure and Emfit signal were recorded in 32 patients with suspected sleep-disordered breathing. Increased respiratory effort was defined as oesophageal pressure below -8 cmH(2)O during inspiration. The epochs of normal breathing, periodic breathing patterns and sustained spiking labelled as increased respiratory resistance (IRR) were defined on the Emfit signal according to established rules. RESULTS: Compared to normal breathing, the proportion of increased respiratory effort was higher during all periodic breathing with spiking. The highest proportion (18-23%) occurred during IRR, which is characterised by sustained spiking. CONCLUSION: The Emfit movement sensor is a non-invasive alternative to the oesophageal pressure sensor in the assessment of the respiratory effort during sleep. In particular, the Emfit sensor enhances detection of non-apnoeic sleep-disordered breathing, the significance of which should not be ignored.

Diffusion tensor imaging of the brain in a healthy adult population: Normative values and measurement reproducibility at 3 T and 1.5 T.

BACKGROUND: Diffusion tensor imaging (DTI) is an increasingly used method for investigation of brain white matter integrity in both research and clinical applications. Familiarity with normal variation of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values and measurement reproducibility is essential when DTI measurements are interpreted in clinical patients. PURPOSE: To establish normal values for FA and ADC in a healthy adult population at 1.5 T and 3 T MRI based on region of interest (ROI) analysis, and to study the inter- and intra-observer reproducibility of the measurements. MATERIAL AND METHODS: Forty healthy volunteers (26 women, 14 men, mean age 38.3, SD 11.6 years) underwent conventional MRI and DTI of the brain, 30 with 3 T and 10 with 1.5 T clinical scanners. ROI-based measurements for FA and ADC values were performed in five different anatomic locations of each hemisphere and in three locations within the corpus callosum. Mean values for FA and ADC for each region were calculated. Inter-observer variation of ROI measurements was evaluated by comparing the results of the two observers, intra-observer variation by repeated measurement of 10 subjects by both observers. RESULTS: The FA values varied considerably between different regions. The highest values were found in the genu and splenium of the corpus callosum and the lowest in the corona radiata, respectively. In general, ADC values showed less variation; the highest values were found in the body of the corpus callosum and the lowest in the corona radiata. The reproducibility of both inter- and intra-observer measurements also varied regionally. The highest agreement was found for the corpus callosum and the lowest for the corona radiata and centrum semiovale. CONCLUSION: In a normal adult population FA and ADC values of the brain white matter show regional variation. The repeatability of the ROI measurements also varies regionally. This regional variability must be acknowledged when these measurements are interpreted in clinical patients.

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.

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.

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.

Fuzzy detection of EEG alpha without amplitude thresholding.

Intelligent automated systems are needed to assist the tedious visual analysis of polygraphic recordings. Most systems need detection of different electroencephalogram (EEG) waveforms. The problem in automated detection of alpha activity is the large inter-individual variability of its amplitude and duration. In this work, a fuzzy reasoning based method for the detection of alpha activity was designed and tested using a total of 32 recordings from seven different subjects. Intelligence of the method was distributed to features extracted and the way they were combined. The ranges of the fuzzy rules were determined based on feature statistics. The advantage of the detector is that no alpha amplitude threshold needs to be selected. The performance of the alpha detector was assessed with receiver operating characteristic (ROC) curves. When the true positive rate was 94.2%, the false positive rate was 9.2%, which indicates good performance in sleep EEG analysis.