Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification.

[1]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[2]  G. Moody,et al.  QRS morphology representation and noise estimation using the Karhunen-Loeve transform , 1989, [1989] Proceedings. Computers in Cardiology.

[3]  Stefan Hadjitodorov,et al.  A fuzzy version of the K -NN method , 1992 .

[4]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[5]  G. Carrault,et al.  Comparing wavelet transforms for recognizing cardiac patterns , 1995 .

[6]  F.M. Ham,et al.  Classification of cardiac arrhythmias using fuzzy ARTMAP , 1996, IEEE Transactions on Biomedical Engineering.

[7]  A. Mocholi,et al.  Previous identification of QRS onset and offset is not essential for classifying QRS complexes in a single lead , 1997, Computers in Cardiology 1997.

[8]  W.J. Tompkins,et al.  A patient-adaptable ECG beat classifier using a mixture of experts approach , 1997, IEEE Transactions on Biomedical Engineering.

[9]  Michael G. Strintzis,et al.  ECG pattern recognition and classification using non-linear transformations and neural networks: A review , 1998, Int. J. Medical Informatics.

[10]  I.K. Duskalov,et al.  Developments in ECG acquisition, preprocessing, parameter measurement, and recording , 1998, IEEE Engineering in Medicine and Biology Magazine.

[11]  I I Christov,et al.  Electrocardiogram signal preprocessing for automatic detection of QRS boundaries. , 1999, Medical engineering & physics.

[12]  H Al-Nashash.  Cardiac arrhythmia classification using neural networks. , 2000, Technology and health care : official journal of the European Society for Engineering and Medicine.

[13]  Carsten Peterson,et al.  Clustering ECG complexes using Hermite functions and self-organizing maps , 2000, IEEE Trans. Biomed. Eng..

[14]  J. Moraes,et al.  A real time QRS complex classification method using Mahalanobis distance , 2002, Computers in Cardiology.

[15]  G. Bortolan,et al.  Pattern recognition and optimal parameter selection in premature ventricular contraction classification , 2004, Computers in Cardiology, 2004.

[16]  G. Bortolan,et al.  Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks , 2004, Physiological measurement.

[17]  Philip de Chazal,et al.  Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[18]  Karen O. Egiazarian,et al.  Feature extraction for heartbeat classification using independent component analysis and matching pursuits , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[19]  G. Bortolan,et al.  Comparison of four methods for premature ventricular contraction and normal beat clustering , 2005, Computers in Cardiology, 2005.

[20]  G Bortolan,et al.  Premature ventricular contraction classification by the Kth nearest-neighbours rule , 2005, Physiological measurement.

[21]  P. Laguna,et al.  Adaptive estimation of QRS complex wave features of ECG signal by the hermite model , 2007, Medical and Biological Engineering and Computing.