Paulo Csar Cortez

A QRS detection based on hilbert transform and wavelet bases

In this study, the Hillbert transform pairs of wavelet bases is used for QRS detection. From the properties of these mathematical tools, it was possible to develop an algorithm which is able to differentiate the R waves from the others (P, Q, S, T and U waves). The choice of a wavelet base was done after observing its frequency response in order to achieve a passband filter containing the characteristic frequencies of the QRS complex. The RR interval duration signal is obtained from the ECG, then it is calculated the cardiac rhythm and done the automatic discrimination between normal and pathological heart activity. The performance of the algorithm was verified using the records MIT-BIH arrhythmia and normal databases. A QRS detection rate of 99.92% was achieved against MIT-BIH arrhythmia database. The noise tolerance of the proposed method was also tested using standard records from the MIT-BIH noise stress test database. The detection rate of the detector remains about 99.35% even for signal-to-noise ratios (SNR) as low as 6 dB

Análise de Desempenho da Entropia Aproximada (ApEn) na Análise da Variabilidade da Frequência Cardíaca (VFC)

This work evaluates the performance of a nonli-near indicator in front of noise in the analysis of Heart Rate Variability (HRV) due to the presence of artifacts. For this purpose, we select approximate entropy (ApEn). Firstly, we use artificial RR time series and compare the error differences between the computed values before and after inserting ran-dom false-positive and false-negative beats. Afterwards, we use ambulatory recordings of 18 healthy subjects from the MIT-BIH database and compare the error difference between the computed values mantaining and excluding artifacts. We have found that the average errors, both in artificial RR time series and ambulatory recordings, vary inversely proportional to the metric value of ApEn for each time series without artifacts.

Monitoração Fetal Eletrônica por meio de Cardiotocografias utilizando um Sistema baseado em Inteligência Computacional

The accurate analysis of the fetal heart rate (FHR) and its correlation with uterine contractions (UC) allows the diagnostic and the anticipation of many problems related to fetal distress and the preservation of his life. This paper presents the results of an hibrid system based on a set of deterministic rules and fuzzy inference system developed to analyze FHR and UC signals collected by cardiotocography (CTG) exams. The studied variables are basal FHR, short and long term FHR variability, transitory accelerations and decelerations, these lasts classified by their type and number of ocurrencies. The system output is a first level diagnostic based on those input variables. The system is developed using the Matlab version 7 script language. The project also supports a multi-institutional agreement between Brazil and Germany, among the UFC - Universidade Federal do Ceara, the TUM - Technische Universitat Munchen, and the Trium Analysis Online GmbH. The system validation methodology was based on the knowledge of Brazilian and German obstetricians. The results achieved low false positive rates, classifying a high percentage of normal exams as normal and none suspicious or pathological exams as normal ones. These results allow the projection of the refinement of the proposed system with new input variables and further analysis of other obstetricians.