A new mathematical based QRS detector using continuous wavelet transform
Computers and Electrical Engineering
Identification of QRS complexes in 12-lead electrocardiogram
Expert Systems with Applications: An International Journal
ECG analysis using nonlinear PCA neural networks for ischemiadetection
IEEE Transactions on Signal Processing
Self-organizing QRS-wave recognition in ECG using neural networks
IEEE Transactions on Neural Networks
Predicting cardiac autonomic neuropathy category for diabetic data with missing values
Computers in Biology and Medicine
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This paper illustrates a method for time-plane feature extraction from digitized ECG sample using statistical approach. The algorithm detects the position and magnitude of the QRS complex, P and T wave for a single lead ECG dataset. The processing is broadly based on relative comparison of magnitude and slopes of ECG samples. Then the baseline modulation in the dataset is removed. The R-peak detection and baseline modulation is tested MIT-BIH arrhythmia database as well as 12-lead datasets in MIT-PTB database (PTBDB) and available under Physionet. The overall accuracy obtained is more than 99%.