New method for QRS-wave recognition in ECG using MART neural network

ECG Signal Analysis

“…The QRS template used for matched filtering is updated by an ANN recognition algorithm, which provides better adaptation to signal changes. A new method, employing multichannel adaptive resonance theory (MART) neural network is described in [60] for efficient QRS detection. An FIR filter based on Keiser window is adopted for removal of PLI and BLW from the ECG.…”

1D neural network design to detect cardiac arrhythmias

“…They are characterized by determining a mathematical representation for each neuron by assigning a convolutional operation [5] (the signal convolution is based on a mathematical process that "fuses" two signals to transform them into a new signal, which can extract characteristics from it [6]), in order to generate an artificial intelligence algorithm, which requires a good signal processing that allows extracting characteristics from it, such as frequency response, the wallet transforms among others [7][8]. However, in some cases have been designed multichannel pattern networks (MART) that allow more than one input of the processed signal [9]. Some studies seek to classify rhythms that have an almost defined pattern with symmetry functions to extract patterns [10] or characteristics of the ECG signal such as duration, amplitude, gradients, among others [11], also seeking the classification of sinus arrhythmias or ventricular arrhythmias from individual analysis [12][13], however, other networks seek to establish a significant difference between a specific wave compared to normal waves (sinus rhythms), such as efficiently detecting a blockage or ischemic episodes [14][15] and premature ventricular and atrial onset [16]; The most common rhythm is atrial fibrillation and therefore it is considered important to establish classification and prediction algorithms for it [17] as Artis, Mark and Moody did, using the Markov model for the implementation of a neural network based on the Back-Propagation algorithm and trained with signals obtained from the MIT-BIH database, present in Physionet® [18][19].…”