Uterine EMG Signals Spectral Analysis for Pre-Term Birth Prediction

A methodology for prediction of pre-term births is presented in this paper. The methodology is based on the analysis of EHG signals and data mining techniques. Initially, spectral and non-linear characteristics of the EHG are extracted, forming a pattern that is used to train a classifier to discriminate between term and pre-term cases. The method has been tested using a benchmark EHG database, and the obtained results indicate its effectiveness in accurate pre-term/term labour prediction

Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks

The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%

Constrained K-Means Classification

Classification-via-clustering (CvC) is a widely used method, using a clustering procedure to perform classification tasks. In this paper, a novel K-Means-based CvC algorithm is presented, analysed and evaluated. Two additional techniques are employed to reduce the effects of the limitations of K-Means. A hypercube of constraints is defined for each centroid and weights are acquired for each attribute of each class, for the use of a weighted Euclidean distance as a similarity criterion in the clustering procedure. Experiments are made with 42 well–known classification datasets. The experimental results demonstrate that the proposed algorithm outperforms CvC with simple K-Means

Linkage of cystic fibrosis to the proα2(I) collagen gene, COL1A2, on chromosome 7

A linkage has been detected between the locus for cystic fibrosis (CF) and the proα2(I) collagen gene (COL1A2) which is located in the region q21.3→q22.1 of chromosome 7. Based on the combined linkage data derived from 50 informative two-generation nuclear families collected in Canada and Denmark, the distance between COL1A2 and CF is estimated to be 19 centiMorgans. Close lilnkage has also been detected between COL1A2 and the DNA market D7S15 (formerly D0CRI-917) and the serum enzyme activity marker paraoxonase (PON), both of which have previously been found linked to CF. The results of the two-oint and three-point linkage analyses indicate that the most probable order of these four genetic loci is COL1A2-D7S15-PON-CF.published_or_final_versio

Mouse Chromosome 11

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46996/1/335_2004_Article_BF00648429.pd