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

Mouse Chromosome 11

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

CARDIAC ARRHYTHMIA CLASSIFICATION USING SUPPORT VECTOR MACHINES

Abstract: A method for automatic arrhythmic beat classification is proposed. The method is based in the analysis of the RR interval signal, extracted from ECG recordings. Classification is made using support vector machines methodology to formulate a quadratic programming problem, subject to simple constraints, which is solved using the BOXCQP method. Four types of cardiac rhythms beats are classified: (1) beats belonging to ventricular flutter/fibrillation episodes, (2) premature ventricular contractions, (3) normal sinus rhythm and (4) beats belonging to 2 o heart block episodes. The method is evaluated using the ECG recordings from the MIT-BIH arrhythmia database and results are presented