Analysis of atrial and ventricular premature contractions using the Short Time Fourier Transform with the window size fixed in the frequency domain

[1]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[2]  Carlos Mateo,et al.  Short-Time Fourier Transform with the Window Size Fixed in the Frequency Domain (STFT-FD): Implementation , 2018, SoftwareX.

[3]  B Gopakumaran,et al.  Automatic Detection of Conducted Premature Atrial Contractions to Predict Atrial Fibrillation in Patients after Cardiac Surgery , 2004 .

[4]  Yaguo Lei,et al.  Fast-varying AM-FM components extraction based on an adaptive STFT , 2012, Digit. Signal Process..

[5]  Prashant Parikh.  A Theory of Communication , 2010 .

[6]  Carlos Mateo,et al.  Bridging the gap between the short-time Fourier transform (STFT), wavelets, the constant-Q transform and multi-resolution STFT , 2020, Signal, Image and Video Processing.

[7]  Carlos Mateo,et al.  Short-time Fourier transform with the window size fixed in the frequency domain , 2017, Digit. Signal Process..

[8]  Zahide Elif Akin,et al.  A NEW ROBUST QRS DETECTION ALGORITHM IN ARRHYTHMIC ECG SIGNALS , 2018 .

[9]  Keun-Chang Kwak,et al.  Personal Identification Using a Robust Eigen ECG Network Based on Time-Frequency Representations of ECG Signals , 2019, IEEE Access.

[10]  Yakoub Bazi,et al.  Convolutional Neural Networks for Electrocardiogram Classification , 2018, Journal of Medical and Biological Engineering.

[11]  Adriana Maria Ciupe,et al.  Study of ECG signal processing using wavelet transforms , 2015, 2015 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE).

[12]  Soo-Chang Pei,et al.  STFT With Adaptive Window Width Based on the Chirp Rate , 2012, IEEE Transactions on Signal Processing.

[13]  George Manis,et al.  Heartbeat Time Series Classification With Support Vector Machines , 2009, IEEE Transactions on Information Technology in Biomedicine.

[14]  J. N. Watson,et al.  A comparison of continuous wavelet transform and modulus maxima analysis of characteristic ECG features , 2005, Computers in Cardiology, 2005.

[15]  Abraham T. Mathew,et al.  Fuzzy Clustered Probabilistic and Multi Layered Feed Forward Neural Networks for Electrocardiogram Arrhythmia Classification , 2011, Journal of Medical Systems.

[16]  Chee Peng Lim,et al.  Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models , 2015, Expert Syst. Appl..

[17]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[18]  Sabir Jacquir,et al.  Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT , 2016, Biomed. Signal Process. Control..

[19]  Di Wang,et al.  Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine , 2018, Comput. Biol. Medicine.

[20]  Volker Gnann,et al.  Signal Reconstruction from Multiresolution STFT Magnitudes with Mutual Initialization , 2012 .

[21]  U. Rajendra Acharya,et al.  ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform , 2013, Biomed. Signal Process. Control..

[22]  Ming-Jing Hwang,et al.  Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model , 2020, iScience.