Discrete Wavelet Transform In Automatic ECG Signal Analysis

-The paper summarizes author's investigations in the field of ECG signal analysis, focused on QRS complex detection. Presented algorithm is based on the one described in [1]. Data containing electrocardiography signal (ECG) is processed by Discrete Wavelet Transform (DWT), computed at selected, characteristic scales, where QRS complex spectrum energy is the largest. For each transient present in the input signal coefficients of DWT produce localized extremes at several consecutive scales. This property is used in detection process. As characteristic scales are analyzed, the QRS waves can be found. Aligned positions of local extremes at characteristic scales determine the instant of QRS complex in the input signal. Only one QRS complex is detected per an iteration in the author's solution. This simplifies introducing: data blocks of variable length and threshold function of variable level. Each iteration besides QRS detection result ends with R-R interval instant and averaged value computation, R-R interval value dependent buffer length setup, threshold function update and data pointer update for the following iteration (which provide for necessary data overlapping). QRS detection fundamental parameters of the presented algorithm were calculated. These are: sensitivity: 99.86%; positive predictivity value: 99.91 % and additionally: error rate: 0.23 %; specificity: 99.91 % and reliability: 99.77%.