Changes in health complaints after removal of amalgam fillings.

The aim of the present study was to investigate whether removal of all amalgam fillings was associated with long-term changes in health complaints in a group of patients who attributed subjective health complaints to amalgam fillings. Patients previously examined at the Norwegian Dental Biomaterials Adverse Reaction Unit were included in the study and assigned to a treatment group (n = 20) and a reference group (n = 20). Participants in the treatment group had all amalgam fillings replaced with other restorative materials. Follow-ups took place 3 months, 1 and 3 years after removal of all amalgam fillings. There was no intervention in the reference group. Subjective health complaints were measured by numeric rating scales in both groups. Analysis of covariance was used to compare changes in health complaints over time in the two groups. In the treatment group, there were significant reductions in intra-oral and general health complaints from inclusion into study to the 3-year follow-up. In the reference group, changes in the same period were not significant. Comparisons between the groups showed that reductions in intra-oral and general health complaints in the treatment group were significantly different from the changes in the reference group. The mechanisms behind this remain to be identified. Reduced exposure to dental amalgam, patient-centred treatment and follow-ups, and elimination of worry are factors that may have influenced the results.

Investigation of an automatic sleep stage classification by means of multiscorer hypnogram.

OBJECTIVES: Scoring sleep visually based on polysomnography is an important but time-consuming element of sleep medicine. Whereas computer software assists human experts in the assignment of sleep stages to polysomnogram epochs, their performance is usually insufficient. This study evaluates the possibility to fully automatize sleep staging considering the reliability of the sleep stages available from human expert sleep scorers. METHODS: We obtain features from EEG, ECG and respiratory signals of polysomnograms from ten healthy subjects. Using the sleep stages provided by three human experts, we evaluate the performance of linear discriminant analysis on the entire polysomnogram and only on epochs where the three experts agree in their sleep stage scoring. RESULTS: We show that in polysomnogram intervals, to which all three scorers assign the same sleep stage, our algorithm achieves 90% accuracy. This high rate of agreement with the human experts is accomplished with only a small set of three frequency features from the EEG. We increase the performance to 93% by including ECG and respiration features. In contrast, on intervals of ambiguous sleep stage, the sleep stage classification obtained from our algorithm, agrees with the human consensus scorer in approximately 61%. CONCLUSIONS: These findings suggest that machine classification is highly consistent with human sleep staging and that error in the algorithm's assignments is rather a problem of lack of well-defined criteria for human experts to judge certain polysomnogram epochs than an insufficiency of computational procedures.

Comparison of different methods for the evaluation of treatment effects from the sleep EEG of patients with major depression.

In healthy subjects, sleep has a typical structure of three to five cyclic transitions between different sleep states. In major depression, this regular pattern is often destroyed but can be reestablished during successful treatment. The differences between healthy and abnormal sleep are generally assessed in a time-consuming process, which consists of determining the nightly variations of the sleep states (the hypnogram) based on visual inspection of the electroencephalogram (EEG), electrooculogram, and electromyogram. In this study, three different methods of sleep EEG analysis (spectrum, outlier, and recurrence analysis) have been examined with regard to their ability to extract information about treatment effects in patients with major depression. Our data suggest that improved sleep patterns during treatment with antidepressant medication can be identified with an appropriate analysis of the EEG. By comparing different methods, we have found that many treatment effects identified by spectrum analysis can be reproduced by the much simpler technique of outlier analysis. Finally, the cyclic structure of sleep and its modification by antidepressant treatment is best illustrated by a non-linear approach, the so-called recurrence method.