Alpo Värri

A simple format for exchange of digitized polygraphic recordings

A simple digital format supporting the technical aspects of exchange and storage of polygraphic signals has been specified. Implementation of the format is simple and independent of hard- or software environments. It allows for any local montages, transducers, prefiltering, sampling frequencies, etc. At present, 7 laboratories in various countries have used the format for exchanging sleep-wake recordings. These exchanges have made it possible to create a common database of sleep records, to compare the analysis algorithms local to the various laboratories to each other by applying these algorithms to identical signals, and to set up a computer-aided interlaboratory evaluation of manual and automatic analysis methods.

The SIESTA project polygraphic and clinical database

The SIESTA project had two major goals: developing new tools for analyzing computer-based sleep recordings and creating a reference database for sleep-related features. Basically, both goals have been reached, although validation and fine tuning of the sleep analyzer is still on-going. Investigations on the Web interface will be finished soon and a documentation of the database (including a CD-ROM with all test forms and all clinical, psychometric and actigraphic data as well as all R&K-scorings) will be published. Besides its scientific impact, the SIESTA project also emphasizes two other important aspects: the need of national and international cooperation between different experts and disciplines and the importance of standardized methods in scientific and clinical research.

Automatic identification of significant graphoelements in multichannel EEG recordings by adaptive segmentation and fuzzy clustering

A new approach to visual evaluation of long-term EEG recordings is proposed. The method is based on multichannel adaptive segmentation, subsequent feature extraction, automatic classification of the acquired segments by fuzzy cluster analysis (fuzzy c-means algorithm), and on the distinguishing of thus identified EEG segments by colour directly in the EEG record. The black and white variant of the described automatic system is presented. The method was evaluated by applying it to simulated artificial data and to real EEG recordings; some of the illustrative results are shown. In addition, the performance of this system is evaluated and the first experience with its application to routine EEG recordings is discussed.

A new method for measuring the ballistocardiogram using EMFi sensors in a normal chair

Ballistocardiography is a non-invasive technique for the assessment of cardiac function. We built a measurement setup to measure the ballistocardiogram from a normal chair using EMFi sensors. The ballistocardiogram is recorded from a subject sitting on the chair. The measured signal is amplified by a specially-designed charge amplifier and digitized by a circulation monitor. A PC provides a user interface for the measurement devices, records the data and displays the results. Impedancecardiography and ECG serve as reference measurements for the ballistocardiography. To test the system, one healthy 24-year-old male and one healthy 22-year-old female were measured. It is concluded that the ballistocardiogram waveforms described in the literature can be recognized from the EMFi signal measured from a normal chair.

Automatic sleep stage classification using two-channel electro-oculography.

An automatic method for the classification of wakefulness and sleep stages SREM, S1, S2 and SWS was developed based on our two previous studies. The method is based on a two-channel electro-oculography (EOG) referenced to the left mastoid (M1). Synchronous electroencephalographic (EEG) activity in S2 and SWS was detected by calculating cross-correlation and peak-to-peak amplitude difference in the 0.5-6 Hz band between the two EOG channels. An automatic slow eye-movement (SEM) estimation was used to indicate wakefulness, SREM and S1. Beta power 18-30 Hz and alpha power 8-12 Hz was also used for wakefulness detection. Synchronous 1.5-6 Hz EEG activity and absence of large eye movements was used for S1 separation from SREM. Simple smoothing rules were also applied. Sleep EEG, EOG and EMG were recorded from 265 subjects. The system was tuned using data from 132 training subjects and then applied to data from 131 validation subjects that were different to the training subjects. Cohens Kappa between the visual and the developed new automatic scoring in separating 30s wakefulness, SREM, S1, S2 and SWS epochs was substantial 0.62 with epoch by epoch agreement of 72%. With automatic subject specific alpha thresholds for offline applications results improved to 0.63 and 73%. The automatic method can be further developed and applied for ambulatory sleep recordings by using only four disposable, self-adhesive and self-applicable electrodes.

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.

Optimization of sigma amplitude threshold in sleep spindle detection

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.

Acquisition of biomedical signals databases

Aspects to consider when building a database are presented. The work is based on experiences from the SIESTA project which identified sleep disorders.

Standards for biomedical signal databases

This article presents some requirements imposed on data format specifications derived from different biosignal recording environments. This is followed by a review of four particularly interesting data formats and some notes about other specifications. Finally, the merits of different formats are discussed and some views about future developments are given.

An EMFi-film sensor based ballistocardiographic chair: performance and cycle extraction method

New sensor technologies open possibilities for measuring traditional biosignals in new innovative ways. This, together with the development of signal processing systems and their computing power, can sometimes give new life to old measurement techniques. Ballistocardiogram is one such technique, originally promising but quickly replaced by the now very popular electrocardiogram. A ballistocardiograph chair, designed to look like a normal office chair, was built and fitted with pressure sensitive EMFi-films. The films are connected via a charge amplifier to a medical bioamplifier. The system was accepted for medical use in Tampere University Hospital and patient measurements have been performed. The system is presented and its performance evaluated. A wireless version of the system is needed to hide the cabling from the user. This makes the chair indistinguishable from a normal office chair. Overview of first wireless prototype is given. To analyze recorded BCG, individual BCG cycles must be extracted from the signal containing respiration and movement artifacts. A method for this and results of its application are presented. The developed system can be used for BCG measurements and it is able to automatically extract individual BCG cycles, but it has some limitations which are presented in the paper.