Fully parametric sleep staging compatible with the classical criteria.

We present an open system for sleep staging, based explicitly on the criteria used in visual EEG analysis. Slow waves, theta and alpha waves, sleep spindles and K-complexes are parameterized in terms of time duration, amplitude, and frequency of each waveform by means of the matching pursuit algorithm. It allows the detection of these structures using mostly the criteria from visual EEG analysis. For example, within this framework for the first time we compute directly the relative duration of slow waves, which is a basic parameter in recognition of deep sleep stages. Performance of the system is evaluated on 20 polysomnographic recordings, scored by experienced encephalographers. Seven recordings were scored by more than one expert. Proposed system gives concordance with visual staging close to the inter-expert concordance. The algorithm is implemented in a user-friendly software system for display and analysis of polysomnographic recordings, freely available with complete source code from http://signalml.org/svarog.html .

Explicit parameterization of sleep EEG transients.

Adaptive time-frequency approximations, implemented via the matching pursuit algorithm, offer description of local signals structures in terms of their time occurrence and width, frequency and amplitude. This allows to construct explicit filters for finding EEG waveforms, known from the visual analysis, in the matching pursuit decomposition of signals. In such a way detectors of relevant structures of both transient and oscillatory nature can be constructed in the space of physically meaningful parameters. This study presents evaluation of changes of power and frequency of sleep spindles and delta waves, related to the depth of the sleep, which were previously assessed in a qualitative way. We confirm quantitatively the decrease of frequencies of sleep spindles and delta waves with the depth of the sleep.

High resolution parametric description of slow wave sleep.

We propose a new framework for quantitative analysis of sleep EEG, compatible with the traditional analysis, based upon adaptive time-frequency approximation of signals. Using a high resolution description of EEG rhythms and transients in terms of their time occurrence and width, frequency and amplitude, we present a detailed detection and parameterization of delta waves, including also the time occupied by each delta wave-a parameter inaccessible directly by previously applied signal processing methods. To validate the proposed parameterization, we construct a simple detector of sleep stages 3 and 4, based explicitly upon the classical criteria related to delta waves. To properly compare its performance to the inter-expert agreements and other expert systems, we sort out and discuss the methodology of reporting concordance in this context. Since the proposed parameterization proves to be compatible with the visual analysis of EEG, we can derive new variables for quantitative analysis of EEG patterns recognized for decades. As examples, we present a continuous description of delta waves and sleep spindles in the overnight sleep, and compare results to the traditional FFT-based estimates.

[Therapeutic drug monitoring in depression--new perspectives]. / Terapia monitorowana depresji--nowe perspektywy.

UNLABELLED: 34 patients with diagnosis of depressive episode (ICD-10) were treated for 8 weeks with tricyclic antidepressants (TCA) 22 patients were treated with clomipramine 75-175 mg daily, 11 with imipramine 75-150 mg and 1 with amitriptyline 150 mg. Following parameters were analysed: plasma concentration (FPIA, HPLC), pharmaco-EEG (spectrum power for delta, theta, alfa1, 2, beta 1, 2, 3 by the use of FFT), clinical improvement (HAMD, HARS, SGI, SERS). 50% reduction in HAMD was regarded as improvement. RESULTS: No relationship between mental state and plasma concentration of TCA was found, initial results in SERS were prognostic for the course of treatment, pharmaco-EEG was typical for antidepressants after two weeks of treatment and reflects clinical improvement and stabilization of plasma concentration. Comparing the plasma concentration of TCA measured by the use of FPIA and HPLC method may be useful for the implementation of monitoring therapy.