Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia

Brenyo A, Aktas MK (2014) Review of complementary and alternative medical treatment of arrhythmias. Am J Cardiol 113(5):897–903

Gravina R, Fortino G (2016) Automatic methods for the detection of accelerative cardiac defense response. IEEE Trans Affect Comput 7(3):286–298

Tahir S, Raja MM, Razzaq N, Mirza A, Khan WZ, Kim SW, Zikria YB (2021) Extended Kalman Filter-Based power line interference canceller for electrocardiogram signal. Big Data. https://doi.org/10.1089/big.2021.0043

Uyar A, Gurgen F (2007) Arrhythmia classification using serial fusion of support vector machines and logistic regression. In: 2007 4th IEEE workshop on intelligent data acquisition and advanced computing systems: technology and applications. IEEE

Llamedo M, Martínez JP (2010) Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans Biomed Eng 58(3):616–625

Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

Zhang Y et al (2018) PEA: Parallel electrocardiogram-based authentication for smart healthcare systems. J Netw Comput Appl 117:10–16

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(7):1196–1206

Tateno K, Glass L (2001) Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals. Med Biol Eng Compu 39(6):664–671

Lin C-C, Yang C-M (2014) Heartbeat classification using normalized RR intervals and morphological features. Mathe Probl Eng 2014

Zhou Q (2009) The electrocardiogram classification research on electrocardiogram RR interval variation. In: 2009 second international symposium on computational intelligence and design. vol 2. IEEE

Kutlu Y, Kuntalp D (2012) Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients. Comput Methods Programs Biomed 105(3):257–267

Das MK, Ghosh DK, Ari S (2013) Electrocardiogram (ECG) signal classification using s-transform, genetic algorithm and neural network. In: 2013 IEEE 1st international conference on condition assessment techniques in electrical systems (CATCON). IEEE

Uslu E, Bilgin G (2012) Exploiting locality based Fourier transform for ECG signal diagnosis. Int Conf Appl Electr, 323–326.

Raj S, Ray KC (2017) ECG signal analysis using DCT-based DOST and PSO optimized SVM. IEEE Trans Instrum Meas 66(3):470–478

Gumaei A, Al-Rakhami M, Hassan MM, Alamri A, Alhussein M, Razzaque MA, Fortino G (2020) A deep learning-based driver distraction identification framework over edge cloud. Neural Comput Appl, 1–16

Piccialli F, Di Somma V, Giampaolo F, Cuomo S, Fortino G (2021) A survey on deep learning in medicine: Why, how and when? Inf Fus 66:111–137

Liang W, Zhang D, Lei X, Tang M, Li K, Zomaya A (2020) Circuit copyright blockchain: blockchain-based homomorphic encryption for IP circuit protection. In: IEEE transactions on emerging topics in computing, https://doi.org/10.1109/TETC.2020.2993032

Ravì D et al (2016) Deep learning for health informatics. IEEE J Biomed Health informatics 21(1):4–21

Selvanambi R, Natarajan J, Karuppiah M, Islam SH, Hassan MM, Fortino G (2020) Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization. Neural Comput Appl 32(9):4373–4386

Lu W, Hou H, Chu J (2018) Feature fusion for imbalanced ECG data analysis. Biomed Signal Process Control 41:152–160

Li Z, Zhou D, Wan L et al (2020) Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram. J Electrocardiol 58:105–112

Mondejarguerra VM et al. (2019) Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomed Signal Process Control

Kiranyaz S, Ince T, Gabbouj M (2015) Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng 63(3):664–675

Golrizkhatami Z, Acan A (2018) ECG classification using three-level fusion of different feature descriptors. Expert Syst Appl 114:54–64

Ye C, Vijaya Kumar BVK, Coimbra MT (2012) Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans Biomed Eng 59(10):2930–2941

Prasad GK, Sahambi JS (2003) Classification of ECG arrhythmias using multi-resolution analysis and neural networks. IEEE Region 10 Conf, 227–231

Kim M, Ko H, Pan SB (2019) A study on user recognition using 2D ECG based on ensemble of deep convolutional neural networks. J Ambient Intell Human Comput, 1–9

Ortin S et al. (2019) Automated real-time method for ventricular heartbeat classification. Comput Methods Programs Biomed, 1–8

Martis RJ, Acharya UR, Min LC (2013) ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed Signal Process Control 8(5):437–448

Strobach P (1995) Single section least squares adaptive notch filter. IEEE Trans Signal Process 43(8):2007–2010

Łȩski J, Henzel N (2005) ECG baseline wander and powerline interference reduction using nonlinear filter bank. Signal Process 85(4):781–793

Barros AK, Mansour A, Ohnishi N (1998) Removing artifacts from electrocardiographic signals using independent components analysis. Neurocomputing 22(1):173–186

Ziarani AK, Konrad A (2002) A nonlinear adaptive method of elimination of power line interference in ECG signals. IEEE Trans Biomed Eng 49(6):540–547

Donoho DL, Johnstone IM (1995) Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 90(432):1200–1224

Haykin S (2002) Adaptive Filter Theory

Martis RJ et al (2014) Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation. Biomed siGnal Process Control 13:295–305

LeCun Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

Scherer D, Müller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: International conference on artificial neural networks. Springer, Berlin, Heidelberg

Ciresan DC et al. (2011) Flexible, high performance convolutional neural networks for image classification. In: Twenty-Second international joint conference on artificial intelligence

Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. bmvc. vol 1. No. 3

Oh SL et al (2018) Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med 102:278–287

Duda, RO, Hart PE, Stork DG (2004) Pattern classification. Pattern classification. Wiley

Wang J-S et al (2013) ECG arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing 116:38–45

Shoohi LM, Saud JH (2020) Dcgan for handling imbalanced malaria dataset based on over-sampling technique and using cnn. Medico Legal Update 20(1):1079–1085

Vapnik V (1998) Statistical learning theory. Wiley, New York

Zubair, M, Kim J, Yoon C (2016) An automated ECG beat classification system using convolutional neural networks. In: 2016 6th international conference on IT convergence and security (ICITCS). IEEE

Park J, Lee K, Kang K (2013) Arrhythmia detection from heartbeat using k-nearest neighbor classifier. In: 2013 IEEE international conference on bioinformatics and biomedicine. IEEE

Acharya UR et al (2017) A deep convolutional neural network model to classify heartbeats. Computers in Biol Med 89:389–396

Xia Y et al (2018) "An automatic cardiac arrhythmia classification system with wearable electrocardiogram. IEEE Access 6:16529–16538

Zhang Z et al (2014) Heartbeat classification using disease-specific feature selection. Comput Biol Med 46(2014):79–89

Ince T, Kiranyaz S (2009) Gabbouj M (2009) A generic and robust system for automated patient-specific classification of ECG signals. IEEE Trans Biomed Eng 56(5):1415–1426

Zikria YB, Afzal MK, Kim SW, Marin A, Guizani M (2020) Deep learning for intelligent IoT: Opportunities, challenges and solutions. Deep learning for intelligent IoT: Opportunities, challenges and solutions, Computer Communications, vol 164, pp 50–53

Qadri YA, Nauman A, Zikria YB, Vasilakos AV, Kim SW (2020) The future of healthcare internet of things: a survey of emerging technologies. IEEE Commun Surv Tutor 22(2):1121–1167

Islam SM, Lloret J, Zikria YB (2021) Internet of Things (IoT)-based wireless health: enabling technologies and applications. Electr J 10(2):148

Bhavsar KA, Singla J, Al-Otaibi YD, Song O, Zikria YB et al (2021) Medical diagnosis using machine learning: a statistical review. Comput Mater Continua 67(1):107–125

Andreoli A, Gravina R, Giannantonio R, Pierleoni P, Fortino G (2010) SPINE-HRV: A BSN-based toolkit for heart rate variability analysis in the time-domain. Wearable and autonomous biomedical devices and systems for smart environment. Springer, Berlin, Heidelberg, 369–389

Fortino G, Parisi D, Pirrone V, Di Fatta G (2014) BodyCloud: a SaaS approach for community body sensor networks. Futur Gener Comput Syst 35:62–79

Fortino G, Galzarano S, Gravina R, Li W (2015) A framework for collaborative computing and multi-sensor data fusion in body sensor networks. Inf Fus 22:50–70

Gravina R, Alinia P, Ghasemzadeh H, Fortino G (2017) Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges. Inf Fus 35:68–80