Visual and spectral analysis of sleep EEG in acute hemispheric stroke

BACKGROUND: Reports on the effects of focal hemispheric damage on sleep EEG are rare and contradictory. PATIENTS AND METHODS: Twenty patients (mean age +/- SD 53 +/- 14 years) with a first acute hemispheric stroke and no sleep apnea were studied. Stroke severity [National Institute of Health Stroke Scale (NIHSS)], volume (diffusion-weighted brain MRI), and short-term outcome (Rankin score) were assessed. Within the first 8 days after stroke onset, 1-3 sleep EEG recordings per patient were performed. Sleep scoring and spectral analysis were based on the central derivation of the healthy hemisphere. Data were compared with those of 10 age-matched and gender-matched hospitalized controls with no brain damage and no sleep apnea. RESULTS: Stroke patients had higher amounts of wakefulness after sleep onset (112 +/- 53 min vs. 60 +/- 38 min, p < 0.05) and a lower sleep efficiency (76 +/- 10% vs. 86 +/- 8%, p < 0.05) than controls. Time spent in slow-wave sleep (SWS) and rapid eye movement (REM) sleep and total sleep time were lower in stroke patients, but differences were not significant. A positive correlation was found between the amount of SWS and stroke volume (r = 0.79). The slow-wave activity (SWA) ratio NREM sleep/wakefulness was lower in patients than in controls (p < 0.05), and correlated with NIHSS (r = -0.47). CONCLUSION: Acute hemispheric stroke is accompanied by alterations of sleep EEG over the healthy hemisphere that correlate with stroke volume and outcome. The increased SWA during wakefulness and SWS over the healthy hemisphere contralaterally to large strokes may reflect neuronal hypometabolism induced transhemispherically (diaschisis).

Issues of spectral quality in clinical 1H-magnetic resonance spectroscopy and a gallery of artifacts

In spite of the facts that magnetic resonance spectroscopy (MRS) is applied as clinical tool in non-specialized institutions and that semi-automatic acquisition and processing tools can be used to produce quantitative information from MRS exams without expert information, issues of spectral quality and quality assessment are neglected in the literature of MR spectroscopy. Even worse, there is no consensus among experts on concepts or detailed criteria of quality assessment for MR spectra. Furthermore, artifacts are not at all conspicuous in MRS and can easily be taken for true, interpretable features. This article aims to increase interest in issues of spectral quality and quality assessment, to start a larger debate on generally accepted criteria that spectra must fulfil to be clinically and scientifically acceptable, and to provide a sample gallery of artifacts, which can be used to raise awareness for potential pitfalls in MRS.

Structural changes of the retina after conventional laser photocoagulation and selective retina treatment (SRT) in spectral domain OCT

Spectral domain optical coherence tomography (SD-OCT) in patients can deliver retinal cross-sectional images with high resolution. This may allow the evaluation of the extent of damage to the retinal pigment epithelium (RPE) and the neurosensory retina after laser treatment. This article aims to investigate the value of SD-OCT in comparing laser lesions produced by conventional laser photocoagulation and selective retina treatment (SRT).

Content Classification of Multimedia Documents using Partitions of Low-Level Features

Audio-visual documents obtained from German TV news are classified according to the IPTC topic categorization scheme. To this end usual text classification techniques are adapted to speech, video, and non-speech audio. For each of the three modalities word analogues are generated: sequences of syllables for speech, “video words” based on low level color features (color moments, color correlogram and color wavelet), and “audio words” based on low-level spectral features (spectral envelope and spectral flatness) for non-speech audio. Such audio and video words provide a means to represent the different modalities in a uniform way. The frequencies of the word analogues represent audio-visual documents: the standard bag-of-words approach. Support vector machines are used for supervised classification in a 1 vs. n setting. Classification based on speech outperforms all other single modalities. Combining speech with non-speech audio improves classification. Classification is further improved by supplementing speech and non-speech audio with video words. Optimal F-scores range between 62% and 94% corresponding to 50% - 84% above chance. The optimal combination of modalities depends on the category to be recognized. The construction of audio and video words from low-level features provide a good basis for the integration of speech, non-speech audio and video.