Detection of ventricular arrhythmia using hybrid time–frequency-based features and deep neural network

Zipes DP, Wellens HJ (1998) Sudden cardiac death. Circulation 98:2334–2351

Scapigliati A, Ristagno G, Cavaliere F (2013) The best timing for defibrillation in shockable cardiac arrest. Anestesiology 79(1):92–101

Macallan DC, Bell JA, Braddick M, Endersby K, Rizzo-Naudi J (1990) The electrocardiogram in general practice: its use and its interpretation. J R Soc Med 83(9):559–562

Khazaei M, Raeisi K, Goshvarpour A, Ahmadzadeh M (2018) Early detection of sudden cardiac death using nonlinear analysis of heart rate variability. Biocybern Biomed Eng 38(4):931–940

Liew R (2011) Electrocardiogram based predictors of sudden cardiac death in patients with coronary artery disease. Clin Cardiol 34(8):466–473

Pietrasik G, Zarba W (2012) QRS fragmentation: diagnostic and prognostic significance. Cardiol J 19(2):114–121

de Chazal P, O’Dwyer M, Reilly RB (2004) Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 51:1196–1206

Rakshit M, Das S (2017) An efficient wavelet-based automated R-peaks detection method using Hilbert transform. Biocybern Biomed Eng 37(3):566–577

Zijlstra JA, Bekkers LE, Hulleman M (2017) Automated external defibrillator and operator performance in out-of-hospital cardiac arrest. Resuscitation 118:140–146

Linker DT (2016) Accurate, automated detection of atrial fibrillation in ambulatory recordings. Cardiovasc Eng Technol 7:182–189

Monalisa M, Sahoo S, Biswal P, Sabut S (2018) Efficient classification of ventricular arrhythmias using feature selection and C4.5 classifier. Biomed Signal Process Control 44:200–208

Sansone M, Fusco R, Pepino A, Sansone C (2013) Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review. J Healthc Eng 4(4):465–504

Hejč J, Vítek M, Ronzhina M et al (2015) A wavelet-based ECG delineation method: adaptation to an experimental electrograms with manifested global ischemia. Cardiovasc Eng Technol 6:364–375

Maji U, Mitra M, Pal S (2017) Characterization of cardiac arrhythmias by variational mode decomposition technique. Biocybern Biomed Eng 37(3):3578–3589

Lahmiri S (2014) Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. Healthc Technol Lett 1(3):104–109

Anas EA, Lee SY, Hasan MK (2011) Exploiting correlation of ECG with certain EMD functions for discrimination of ventricular fibrillation. Comput Biol Med 41:110–114

Alonso-Atienza F, Morgado E, Fernández-Martínez L, García-Alberola A, Rojo-Álvarez JL (2014) Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng 61:832–840

Li Q, Rajagopalan C, Clifford GD (2014) Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans Biomed Eng 61:1607–1613

Panda R, Jain S, Tripathy RK, Acharya UR (2020) Detection of shockable ventricular cardiac arrhythmias from ECG signals using FFREWT filter-bank and deep convolutional neural network. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2020.103939

Alwan Y, Cvetkovic Z, Curtis MJ (2018) Methods for improved discrimination between ventricular fibrillation and tachycardia. IEEE Trans Biomed Eng 65(10):2143–2151

Hannun AY, Rajpurkar P, Haghpanahi M et al (2019) Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med 25(3):65–69. https://doi.org/10.1038/s41591-018-0268-3

Akansu AN, Haddad RA (1992) Multiresolution signal decomposition: transforms, subbands, and wavelets. Academic Press, Boston

Lin H-Y, Liang SY, Ho YL, Lin YH, Ma HP (2014) Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals. Innov Res BioMed Eng 35(6):351–361

Mandic DP, Rehman N, Wu Z, Huang NE (2013) Empirical mode decomposition-based time-frequency analysis of multivariate signals: the power of adaptive data analysis. IEEE Signal Process Mag 30(6):74–86

Xiong H, Zheng C, Liu J, Song L (2019) ECG signal in-band noise de-noising base on EMD. J Circuits Syst Comput 28(1):1950017

Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544

Singh P, Pradhan G (2018) Variational mode decomposition-based ECG denoising using non-local means and wavelet domain filtering. Australas Phys Eng Sci Med 41(4):891–904

Dreyfus SE (1990) Artificial neural networks, back propagation, and the Kelley-Bryson gradient procedure. J Guid Control Dyn 13(5):926–928

Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

Elola A, Aramendi E, Irusta U, Picón A, Alonso E, Owens P, Idris A (2019) Deep neural networks for ECG-based pulse detection during out-of-hospital cardiac arrest. Entropy 21:305–325

Acharya UR, Fujita H, Oh SL, Tan RS, Acharya UR (2018) Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network. Fut Gener Comput Syst 79(3):952–959

Sharma M, Singh S, Kumar A et al (2019) Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG features. Comput Biol Med 115:103446

Swapna G, Soman KP, Vinayakumar R (2018) Automated detection of cardiac arrhythmia using deep learning techniques’. Procedia Comput Sci 132:1192–1201

Xu Y, Wang D, Zhang W, Ping P, Feng L (2018) Detection of ventricular tachycardia and fibrillation using adaptive variational mode decomposition and boosted-CART classifier. Biomed Signal Process Control 39:219–229

Zhen-Xing Z, Tian XW, Lim JS (2010) Real-time algorithm for a mobile cardiac monitoring system to detect life-threatening arrhythmias. Proc Int Conf Comput Autom Eng 4:232–236

Xie HB, Gao ZM, Liu H (2011) Classification of ventricular tachycardia and fibrillation using fuzzy similarity-based approximate entropy. Expert Syst Appl 38:3973–3981

Yıldırım O, Pławiak P, Tan RS, Acharya UR (2018) Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med 102:411–420

Fokkenrood S, Leijdekkers P, Gay V (2007) Ventricular tachycardia/fibrillation detection algorithm for 24/7 personal wireless heart monitoring. In: Int. conf. on smart homes and health telematics, pp 110–120

Xiong Z, Nash MP, Cheng E, Fedorov VV, Stiles MK, Zhao J (2018) ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network. Physiol Meas 39(9):094006

Hanbay K (2019) Deep neural network based approach for ECG classification using hybrid differential features and active learning. IET Signal Proc 13(2):165–175

Asgharzadeh-Bonab A, Amirani MC, Mehri A (2020) Spectral entropy and deep convolutional neural network for ECG beat classification. Biocybern Biomed Eng 40(2):691–700

Sannino G, Pietro GD (2018) A deep learning approach for ECG-based heartbeat classification for arrhythmia detection. Fut Gener Comput Syst 86:446–455

Mathews SM, Kambhamettu C, Barner KE (2018) A novel application of deep learning for single-lead ECG classification. Comput Biol Med 99:53–62

Picon A, Irusta U, Álvarez-Gila A, Aramendi E, Alonso-Atienza F, Figuera C, Ayala U, Garrote E, Wik L, Kramer-Johansen J, Eftestøl T (2019) Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia. PLoS ONE 14:e0216756

Amann A, Tratnig R, Unterkofler K (2005) Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators. Biomed Eng Online 4(60):1–23

Li Q, Rajagopalan C, Clifford GD (2014) A machine learning approach to multi-level ECG signal quality classification. Comput Methods Prog Biomed 117(3):435–447

Yu SJ, Koh P, Kwon H, Kimg DS, Kim HK (2016) Hurst parameter based anomaly detection for intrusion detection system. In: Int con comp info technology, pp 234–240

Mohanty M, Biswal P, Sabut S (2019) Ventricular tachycardia and fibrillation detection using DWT and decision tree classifier. J Mech Med Biol 19(1):1–23

Watson JN, Addison PS, Clegg GR, Holzer M, Sterz F, Robertson CE (2004) A novel wavelet transform based analysis reveals hidden structure in ventricular fibrillation. Resuscitation 43(2):121–127

Karegowda AG, Manjunath AS, Jayaram MA (2010) Comparative study of attribute selection using gain ratio and correlation based feature selection. Int J Inf Technol Knowl Manag 2(2):271–277

Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv: 1505.00853

Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. In: Int. conf. learning representations, pp 1–15