AUTOMATED DETECTION OF ATRIAL FIBRILLATION ECG SIGNALS USING TWO STAGE VMD AND ATRIAL FIBRILLATION DIAGNOSIS INDEX

Atrial fibrillation (AF) is a common atrial arrhythmia occurring in clinical practice and can be diagnosed using electrocardiogram (ECG) signal. The conventional diagnostic features of ECG signal a...

[1]  Sheng Lu,et al.  Automatic Real Time Detection of Atrial Fibrillation , 2009, Annals of Biomedical Engineering.

[2]  Kevin Noronha,et al.  Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features , 2016, Comput. Biol. Medicine.

[3]  Oliver Faust,et al.  Analysis of cardiac signals using spatial filling index and time-frequency domain , 2004, Biomedical engineering online.

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

[5]  Xiangqian Ding,et al.  A Novel Method for Classification of ECG Arrhythmias Using Deep Belief Networks , 2016, Int. J. Comput. Intell. Appl..

[6]  T. Tamura,et al.  An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes , 2013, Computer methods in biomechanics and biomedical engineering.

[7]  U. Rajendra Acharya,et al.  Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network , 2017, Inf. Sci..

[8]  Padmavathi Kora,et al.  ECG-based atrial fibrillation detection using different orderings of Conjugate Symmetric–Complex Hadamard Transform , 2016 .

[9]  R. K. Tripathy,et al.  A Diagnostic System for Detection of Atrial and Ventricular Arrhythmia Episodes from Electrocardiogram , 2018 .

[10]  Rossitza Setchi,et al.  Feature selection using Joint Mutual Information Maximisation , 2015, Expert Syst. Appl..

[11]  U. Rajendra Acharya,et al.  Application of higher order statistics for atrial arrhythmia classification , 2013, Biomed. Signal Process. Control..

[12]  Leong Mei Yi,et al.  Study of heart rate variability signals at sitting and lying postures , 2005 .

[13]  U. Rajendra Acharya,et al.  Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study , 2017, Inf. Sci..

[14]  D. Levy,et al.  Independent risk factors for atrial fibrillation in a population-based cohort. The Framingham Heart Study. , 1994, JAMA.

[15]  U. Rajendra Acharya,et al.  Linear and nonlinear analysis of normal and CAD-affected heart rate signals , 2014, Comput. Methods Programs Biomed..

[16]  Samarendra Dandapat,et al.  Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition , 2016, Journal of Medical Systems.

[17]  U. Rajendra Acharya,et al.  A deep convolutional neural network model to classify heartbeats , 2017, Comput. Biol. Medicine.

[18]  J. Suri,et al.  Atherosclerotic risk stratification strategy for carotid arteries using texture-based features. , 2012, Ultrasound in medicine & biology.

[19]  Ki H. Chon,et al.  Atrial Fibrillation Detection Using an iPhone 4S , 2013, IEEE Transactions on Biomedical Engineering.

[20]  Giovanni Calcagnini,et al.  P-wave Variability and Atrial Fibrillation , 2016, Scientific Reports.

[21]  U. Rajendra Acharya,et al.  Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform , 2013, Knowl. Based Syst..

[22]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[23]  U. Rajendra Acharya,et al.  A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images , 2017, Comput. Biol. Medicine.

[24]  U. Acharya,et al.  Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters , 2011, Physiological measurement.

[25]  U. Rajendra Acharya,et al.  Automated detection of atrial fibrillation using Bayesian paradigm , 2013, Knowl. Based Syst..

[26]  L Glass,et al.  Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals , 2001, Medical and Biological Engineering and Computing.

[27]  Ayman M. Eldeib,et al.  Breast cancer classification using deep belief networks , 2016, Expert Syst. Appl..

[28]  U. RAJENDRA ACHARYA,et al.  Automated Diagnosis of Normal and Alcoholic EEG signals , 2012, Int. J. Neural Syst..

[29]  Samarendra Dandapat,et al.  Quantification of Diagnostic Information from Electrocardiogram Signal: A Review , 2015 .

[30]  U. RajendraAcharya Advances in cardiac signal processing , 2007 .

[31]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[32]  U. Rajendra Acharya,et al.  Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads , 2016, Knowl. Based Syst..

[33]  Chandan Chakraborty,et al.  AUTOMATED DETECTION OF ATRIAL FLUTTER AND FIBRILLATION USING ECG SIGNALS IN WAVELET FRAMEWORK , 2012 .

[34]  U. Rajendra Acharya,et al.  Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals , 2017, Inf. Sci..

[35]  U. Rajendra Acharya,et al.  Automated identification of normal and diabetes heart rate signals using nonlinear measures , 2013, Comput. Biol. Medicine.

[36]  Chao Huang,et al.  A Novel Method for Detection of the Transition Between Atrial Fibrillation and Sinus Rhythm , 2011, IEEE Transactions on Biomedical Engineering.

[37]  U. Rajendra Acharya,et al.  Current methods in electrocardiogram characterization , 2014, Comput. Biol. Medicine.

[38]  Samarendra Dandapat,et al.  Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction , 2015, IEEE Transactions on Biomedical Engineering.

[39]  Ahmet Mert,et al.  ECG feature extraction based on the bandwidth properties of variational mode decomposition , 2016, Physiological measurement.

[40]  U. Rajendra Acharya,et al.  Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index , 2017, Comput. Biol. Medicine.

[41]  Salim Lahmiri,et al.  Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. , 2014, Healthcare technology letters.

[42]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[43]  M. Ezekowitz,et al.  2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society. , 2014, Circulation.

[44]  Okko Johannes Räsänen,et al.  Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits , 2015, Comput. Speech Lang..

[45]  A. Sahakian,et al.  Diagnosis of atrial fibrillation from surface electrocardiograms based on computer-detected atrial activity. , 1992, Journal of electrocardiology.

[46]  E. Helfenbein,et al.  Improvements in atrial fibrillation detection for real-time monitoring. , 2009, Journal of electrocardiology.