Sabine Huffel

Signal recovery is one of the key techniques of compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one domain to formulate the L0 norm optimization. Recent investigation shows that some signals are sparse in multiple domains. To further improve the signal reconstruction performance, we can exploit this multi-sparsity to generate a new convex programming model. The latter is formulated with multiple sparsity constraints in multiple domains and the linear measurement fitting constraint. It improves signal recovery performance by additional a priori information. Since some EMG signals exhibit sparsity both in time and frequency domains, we take them as example in numerical experiments. Results show that the newly proposed method achieves better performance for multi-sparse signals.

In this paper a new method for muscle artifact removal in EEG is presented, based on Canonical Correlation Analysis (CCA) as a Blind Source Separation technique (BSS). This method is demonstrated on a synthetic data set. The method out-performed a low pass filter with different cutoff frequencies and an Independent Component Analysis (ICA) based technique for muscle artifact removal. The first preliminary results of a clinical study on 26 ictal EEGs of patients with refractory epilepsy illustrated that the removal of muscle artifact results in a better interpretation of the ictal EEG, leading to an earlier detection of the seizure onset and a better localization of the seizures onset zone. These findings make the current method indispensable for every Epilepsy Monitoring Unit.

Labetalol is a drug used in the treatment of hypertensive disorders of pregnancy (HDP). In a previous study we investigated the influence of the maternal use of labetalol on the cerebral autoregulation (CA) mechanism of neonates. In that study, we found that labetalol induces impaired CA during the first day of life, with CA returning to a normal status by the third day after birth. This effect was hypothesized to be caused by labetalol-induced vasodilation. However, no strong evidence for this claim was found. In this study we aim to find stronger evidence for the vasodilation effect caused by labetalol, by investigating its effect on the neurogenic mechanism (NM) involved in CA. The status of the NM was assessed by means of transfer function analysis between the low frequency content of the autonomic control activity (LFA), obtained by processing of the heart rate (HR), and the regional cerebral oxygen saturation (rScO₂). We found that neonates from mothers treated with labetalol presented a lower LFA and an impaired NM response during the first day of life, with values returning to normal by the end of the third day. These results reflect a vasodilation effect caused by labetalol, and indicate that the impaired CA observed in the previous study is caused by vasodilation.

The purpose of this paper is to investigate the potential and limitations of using multimodal sources of information coming from in vivo NMR and ex vivo NMR data for detecting brain tumors. Supervised pattern recognition methods, whose performance directly depends on the prior available observations used in building them, are proposed. We show that high resolution magic angle spinning (HR-MAS)

This work was supported by EU Programme 'Training and Mobility of Researchers' project (contract ERBFM-RXCT970160) entitled 'Advanced Signal Processing of Medical Magnetic Resonance Imaging and Spectroscopy' (first author), by US NSF grant CCR-9734290 (second ...

MRSI has shown potential in the diagnosis and prognosis of glioblastoma multiforme (GBM) brain tumors, but its use is limited by difficult data interpretation. When the analyzed MRSI data present more than two tissue patterns, conventional non-negative matrix factorization (NMF) implementation may lead to a non-robust estimation. The aim of this article is to introduce an effective approach for the differentiation of GBM tissue patterns using MRSI data. A hierarchical non-negative matrix factorization (hNMF) method that can blindly separate the most important spectral sources in short-TE ¹H MRSI data is proposed. This algorithm consists of several levels of NMF, where only two tissue patterns are computed at each level. The method is demonstrated on both simulated and in vivo short-TE ¹H MRSI data in patients with GBM. For the in vivo study, the accuracy of the recovered spectral sources was validated using expert knowledge. Results show that hNMF is able to accurately estimate the three tissue patterns present in the tumoral and peritumoral area of a GBM, i.e. normal, tumor and necrosis, thus providing additional useful information that can help in the diagnosis of GBM. Moreover, the hNMF results can be displayed as easily interpretable maps showing the contribution of each tissue pattern to each voxel.