Classification of QRS pattern by an associative memory model

Lin, Kang‐Ping; Chang, Wei-Ching

doi:10.1109/iembs.1989.96573

Cited by 5 publications

(1 citation statement)

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“…The use of artificial neural networks (ANN) for ECG classification has been addressed by several researchers. The classification of QRS patterns by an associative memory model was discussed in [2]. A 3-layer feed-forward network to classify ECGs based on several time-domain features was reported in [3].…”

Section: Introductionmentioning

confidence: 99%

Identification of supraventricular and ventricular arrhythmias using a combination of three neural networks

Chi

Jabri

[1991] Proceedings Computers in Cardiology

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Section: Introductionmentioning

confidence: 99%

Identification of supraventricular and ventricular arrhythmias using a combination of three neural networks

Chi

Jabri

[1991] Proceedings Computers in Cardiology

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Neural-network-based adaptive matched filtering for QRS detection

Xue

Tompkins

1992

IEEE Trans. Biomed. Eng.

378

127

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We have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. We use an ANN adaptive whitening filter to model the lower frequencies of the ECG which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. We developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. Using this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5%, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.

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Classification of cardiac arrhythmias using fuzzy ARTMAP

Ham

Han

1996

IEEE Trans. Biomed. Eng.

132

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We have investigated the QRS complex, extracted from electrocardiogram (ECG) data, using fuzzy adaptive resonance theory mapping (ARTMAP) to classify cardiac arrhythmias. Two different conditions have been analyzed: normal and abnormal premature ventricular contraction (PVC). Based on MIT/BIH database annotations, cardiac beats for normal and abnormal QRS complexes were extracted from this database, scaled, and Hamming windowed, after bandpass filtering, to yield a sequence of 100 samples for each ORS segment. From each of these sequences, two linear predictive coding (LPC) coefficients were generated using Burg's maximum entropy method. The two LPC coefficients, along with the mean-square value of the QRS complex segment, were utilized as features for each condition to train and test a fuzzy ARTMAP neural network for classification of normal and abnormal PVC conditions. The test results show that the fuzzy ARTMAP neural network can classify cardiac arrhythmias with greater than 99% specificity and 97% sensitivity.

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Classification of QRS pattern by an associative memory model

Cited by 5 publications

References 1 publication

Identification of supraventricular and ventricular arrhythmias using a combination of three neural networks

Identification of supraventricular and ventricular arrhythmias using a combination of three neural networks

Neural-network-based adaptive matched filtering for QRS detection

Classification of cardiac arrhythmias using fuzzy ARTMAP

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