| Summary: | nÃo hà === A tarefa mais importante em processamento de sinais de eletrocardiograma (ECG) à a determinaÃÃo exata do complexo de QRS, em particular, a detecÃÃo dos picos de onda R atravÃs de sistemas e anÃlises computadorizadas.
à essencial, especialmente, para uma medida correta da variabilidade do ritmo cardÃaco (HRV). Um grande obstÃculo a ser superado para uma detecÃÃo confiÃvel à a sensibilidade do eletrocardiograma a diversas fontes de distÃrbio, tais como, a interferÃncia à rede elÃtrica, os artefatos do movimento, flutuaÃÃo da linha base e o ruÃdo dos mÃsculos.
Este trabalho utiliza as propriedades matemÃticas da transformaÃÃo de Hilbert sobre wavelets para desenvolver um novo algoritmo capaz de diferenciar as ondas R das demais (P, Q, S, T e U) e facilitar a detecÃÃo dos complexos QRS. Uma taxa de detecÃÃo do complexo QRS de 99,92% Ã alcanÃada para a base de dados de arritmias do MIT-BIH. A tolerÃncia a ruÃdo do mÃtodo proposto foi tambÃm testada usando os registros padrÃo da base de dados MIT-BIH Noise Stress Test. A taxa da detecÃÃo do detector ficou aproximadamente 99,35% mesmo para as relaÃÃes sinal-ruÃdo (SNR) tÃo baixo quanto 6dB. === The most important task in the ECG signal processing is the accurate determina-tion of QRS complex, in particular, accurate detection of the R wave peaks, is essential in computer-based ECG analysis especially for a correct measurement of Heart Rate Variability (HRV). A great hurdle to be overcome in reliable detection is the sensibility of the electrocar-diogram to several disturbance sources such as powering source interference, movement arti-facts, baseline wandering and muscle noise. This study uses the Hilbert Transform pairs of wavelet bases for QRS detection. From the properties of these mathematical tools it was pos-sible to develop an algorithm which is able to differentiate the R waves from the others (P, Q, S, T and U waves).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 6dB.
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