Abstract
Ischemic heart disease and stroke are the world’s highest killers. These diseases have continued the foremost cause of death globally in the last 15 years. An electrocardiogram (ECG or EKG) is a record of the electrical activity of the heart over a period of time, represented by one-dimensional data. In this paper, different methods such as Wavelet Transform will be applied to the ECG signal to increase accuracy on arrhythmia detection. The main arrhythmia disorders studied in this work were the following: atrial premature and supraventricular beat, the fusion of ventricular and normal beat, isolated QRS-like artifact, ventricular escape beat, and premature ventricular contraction. The annotation on each sample was made by certified cardiologists and each database was uploaded on Physionet (Research Resource for Complex Physiologic Signals). The input data used was 2D images instead of classical time-series data to demonstrate that the presented system can be used to identify with success different arrhythmias directly on images. In other words, a similar algorithm can be used in mobile devices such as smartphones.
The test accuracy obtained demonstrates the efficiency of the system that could be applied to an electrocardiogram to easily detect any specific arrhythmia disorders.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rajput KS, Wibowo S, Hao C, Majmudar M (2019) On arrhythmia detection by deep learning and multidimensional representation arXiv:1904.00138v4
Parvaneh S, Rubin J, Babaeizadeh S, Xu-Wilson M (2019) Cardiac arrhythmia detection using deep learning: a review. J Electrocardiol
Schiff SJ, Aldroubi A, Unser M, Sato S (1994) Fast wavelet transformation of EEG. Electroencephalogr Clin Neurophysiol 91(1994):442–455
Addison PS (2005) Wavelet transforms and the ECG: a review. Physiol Meas 26:R155–R199
Gupta A, Huerta EA, Zhao Z, Moussa I (2019) Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms. arXiv:1912.07618v1
Mousavi S, Fotoohinasab A, Afghah F (2019) Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks. arXiv:1909.11791v1
Barmase S, Das S, Mukhopadhyay S (2013) Wavelet Transform-Based Analysis of QRS complex in ECG Signals. arXiv:1311.6460
Kiranyaz S, Ince T, Gabbouj M (2016) Real- time patient-specific ECG classification by 1-d convolutional neural networks. IEEE Trans Biomed Eng 63(3):664–675
Yu, S-N, Chen Y-H (2007) Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recogn Lett 28(10):1142–1150
Moody GB, Mark RG (2001) The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol
Li Y, Pang Y, Wang K, Li X (2020) Toward Improving ECG biometric identification using cascaded convolutional neural networks. Neurocomput J
Kingma DP, Ba JL (2017) Adam: a method for stochastic optimization. arXiv:1412.6980v9
Jero SE, Ramu P, Ramakrishnan S (2015) ECG steganography using curvelet transform. Biomed Signal Process Control 22:161–169
Bansal A, Joshil R (2017) Portable out-of-hospital electrocardiography: a review of current technologies. J Arrythmiahttps://doi.org/10.1002/joa3.12035
Moody GB, Mark RG (2001) The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol 20(3):45–50. PMID: 11446209
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jozefowicz R, Jia Y, Kaiser L, Kudlur M, Levenberg J, Mané D, Schuster M, Monga R, Moore S, Murray D, Olah C, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: Large-scale machine learning on heterogeneous systems. tensorflow.org
Purwins H, Li B, Virtanen T, Schlüter J, Chang S, Sainath T (2019) Deep learning for audio signal processing. J Sel. Top. Signal Process. 13(2):206–219
Unser M, Aldroubi A (1996) A review of wavelets in biomedical applications. Proc IEEE 84(4)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bratu, DV., Zolya, MA., Moraru, SA. (2022). A Different View on Artificial Intelligence Applications for Cardiac Arrhythmia Detection and Classification. In: Auer, M.E., Bhimavaram, K.R., Yue, XG. (eds) Online Engineering and Society 4.0. REV 2021. Lecture Notes in Networks and Systems, vol 298. Springer, Cham. https://doi.org/10.1007/978-3-030-82529-4_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-82529-4_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82528-7
Online ISBN: 978-3-030-82529-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)