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It has been shown in previous studies on sleep electroencephalogram (EEG) that spindles are slower in the beginning of the night fastening towards the end of the night. Corresponding findings have been obtained by spectral analysis. The present study was based on our preliminary observation that slower spindles are found in the middle of the nonrapid eye movement (NREM) sleep episodes as compared with the beginning or the end of the episodes. Eight healthy female and six male subjects were studied. Sleep spindles were visually selected and spindle frequencies calculated for 11 analysis points in each NREM sleep episode. The median spindle frequencies formed a clear U‐shape within NREM sleep episodes with an initial decrease and final increase. The decrease was statistically significant within the first four NREM sleep episodes. It is possible that the spindle frequency pattern could be used to reveal variations in sleep depth within sleep stage 2. In animal studies the spindle frequency has been found to be associated to the duration of the hyperpolarization‐rebound sequences of the thalamocortical cells. If it is assumed that the same cellular mechanisms are responsible for spindle frequencies in humans then the study of variations in spindle frequency could be used to examine the NREM sleep process in humans.

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.

In the present work, gender differences in sleep spindle topography were examined in 40 subjects. Their median age was 32 years (range 22–49 years). Spindles were detected from 3,306,060 s of visually scored stage 2 sleep EEG by a previously validated automatic fuzzy detector at 1-second intervals. A total of 271,168 spindles were found from the six EEG channels analyzed. Females showed a significantly higher percentage of spindles in the left frontal channel than males (Fp1-A2; p = 0.026). To confirm that this difference was gender and not age related, the subjects were divided into two age groups. No significant differences in spindle activity of the frontal channels were found between the groups. However, the interindividual spindle variability seemed to be at least as large as that stemming from gender.

The development of computerized sleep analysis has been very much technology-driven by both mathematical tools and available hardware but, additionally and unfortunately, by the almost-30-year-old standard used for manual sleep stage scoring of paper recordings. There are no technical restrictions in terms of computing power, storage space, and costs anymore. However, the standards of visual sleep stage scoring have proven insufficient and ambiguous, and their utilization evidently provides misleading and erroneous information. The low temporal resolution provided by the one-page epoch, the crude division of the sleep processes into a few discrete stages, and the total ignorance of spatial information are the major drawbacks. It is meaningless to try to improve the computerised systems if the algorithms are based on erroneous concepts. Instead, the focus should be changed to studies dealing with the identification and modelling of true biological sleep-related processes. This work cannot be performed without the successful application of computerized methods, some of which have been used in related fields but have not yet been applied to sleep studies. It is extremely important that basic findings are confirmed with a wide variety of methods in several laboratories. The use of predetermined, fixed criteria for methods, waveforms, and states too early is scientifically erroneous and hazardous. Instead standards should describe the minimum requirements for the recording and analysis of the signals in terms of sampling rate, dynamic range, linearity, and documentation of the methods used. With the development of better technology, these standards ought to be constantly reevaluated and modified. The development toward more open commercial digital systems, including standardized programming methods and data formats, would have great positive impact to the field. These trends have long been established in many other fields of industry.