Methods of extracting electrocardiograms from electronic signals and images in the Python environment
1
Karaganda State Technical University
2
Al-Farabi Kazakh National University
3
Karaganda State University named after Academician E. A. Buketov
Submission date: 2020-05-07
Final revision date: 2020-08-14
Acceptance date: 2020-08-14
Online publication date: 2020-08-19
Publication date: 2020-09-02
Diagnostyka 2020;21(3):95-101
KEYWORDS
TOPICS
ABSTRACT
High-quality signal processing of an electrocardiogram (ECG) is an urgent problem in present day diagnostics for revealing dangerous signs of cardiovascular diseases and arrhythmias in patients. The used methods and programs of signal analysis and classification work with the arrays of points for mathematical modeling that must be extracted from an image or recording of an electrocardiogram. The aim of this work is developing a method of extracting images of ECG signals into a one-dimensional array. An algorithm is proposed based on sequential color processing operations and improving the image quality, masking and building a one-dimensional array of points using Python tools and libraries with open access. The results of testing samples from the MIT/BIH database and comparing images before and after processing show that the signal extraction accuracy is approximately 95 %. In addition, the presented application design is simple and easy to use. The proposed program for analyzing and processing the ECG data has a great potential in the future for the development of more complex software applications for automatic analyzing the data and determining arrhythmias or other pathologies.
FUNDING
The work has been carried out as a part of a grant in 2018-2020 for the project AR05132044 “Development of a Hardware-medical Complex for Assessing the Psycho-physiological Parameters of a Person”.
REFERENCES (25)
1.
Bhirud B, Pachghare VK. Arrhythmia Detection Using ECG Signal: A Survey. In Proceeding of the International Conference on Computational Science and Applications, Springer, Singapore; 2020, 329-341.
2.
Trayanova NA, Boyle PM, Nikolov PP. Personalized imaging and modeling strategies for arrhythmia prevention and therapy. Current opinion in biomedical engineering 2018; 5: 21-28.
3.
Chatha SR, Chahal CAA, Westwood M, Khanji M. Arrhythmia and sarcoidosis: the role of myocardial tissue characterization to guide diagnosis and management. European Heart Journal-Cardiovascular Imaging 2020; 21(3): 344-344. http://dx.doi.org/10.1093/ehjc....
4.
Abdelazim IA, Bekmukhambetov Y, Aringazina R, Shikanova S, Amer OO, Zhurabekova G.,. Otessin MA, Astrakhanov AR. The outcome of hypertensive disorders with pregnancy. Journal of Family Medicine and Primary Care 2020; 9(3): 1678.
5.
Raj S, Maurya K, Ray KC. A knowledge-based real time embedded platform for arrhythmia beat classification. Biomedical Engineering Letters 2015; 5(4): 271-280. https://dx.doi.org/10.1007/s13....
6.
Ye C, Kumar BV, Coimbra MT. Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Transactions on Biomedical Engineering 2012; 59(10): 2930-2941. https://dx.doi.org/10.1109/TBM....
7.
Zhang Z, Dong J, Luo X, Choi KS, Wu X. Heartbeat classification using disease-specific feature selection. Computers in biology and medicine 2014; 46: 79-89. https://dx.doi.org/10.1142/S02....
8.
Yıldırım Ö, Pławiak P, Tan RS, Acharya UR. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Computers in biology and medicine 2018; 102: 411-420. https://dx.doi.org/10.1016/j.c....
9.
Patil V, Patil SN. Robust system for patient specific classification of ECG signal using PCA and Neural Network. International Research Journal of Engineering and Technology 2017; 4: 1400-1404.
10.
Raj S, Ray KC. ECG signal analysis using DCT-based DOST and PSO optimized SVM. IEEE Transactions on instrumentation and measurement 2017;66(3):470-478. https://dx.doi.org/10.1109/TIM....
11.
D’Aloia M, Longo A, Rizzi M. Noisy ECG signal analysis for automatic peak detection. Information 2019;10(2):35. https://dx.doi.org/10.3390/inf....
12.
Wasimuddin M, Elleithy K, Abuzneid A, Faezipour M, Abuzaghleh O. ECG Signal Analysis Using 2-D Image Classification with Convolutional Neural Network. In 2019 International Conference on Computational Science and Computational Intelligence (CSCI) IEEE. December; 2019, 949-954.
13.
Barmpoutis P, Dimitropoulos K, Apostolidis A, Grammalidis N. Multi-lead ECG signal analysis for myocardial infarction detection and localization through the mapping of Grassmannian and Euclidean features into a common Hilbert space. Biomedical Signal Processing and Control 2019; 52: 111-119. https://dx.doi.org/10.1016/j.b....
14.
Kaur I, Rajni R, Marwaha A. ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform. Journal of the Institution of Engineers (India): Series B 2016; 97: 499–507. https://dx.doi.org/10.1007/s40....
15.
Agarwal S, Krishnamoorthy V, Pratihr S. ECG signal analysis using wavelet coherence and s-transform for classification of cardiovascular diseases. 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur; 2016, 2765-2770. http://dx.doi.org/10.1109/ICAC....
16.
Vulaj Z, Draganić A, Brajović M, Orović I. A tool for ECG signal analysis using standard and optimized Hermite transform. In 2017 6th Mediterranean Conference on Embedded Computing (MECO). IEEE. June; 2017, 1-4.
17.
Sadr N, de Chazal P. Non-invasive Diagnosis of Sleep Apnoea Using ECG and Respiratory Bands. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. July; 2019, 1609-1612.
18.
Bazan V, Marchlinski FE. Usefulness of the 12‐Lead ECG to Identify Epicardial Ventricular Substrate and Epicardial Ventricular Tachycardia Site of Origin. Cardiac Mapping 2019, 1028-1049.
19.
Nelli F. Python data analytics: with pandas, numpy, and matplotlib. Apress; 2018.
20.
Lebanon G, El-Geish M. Visualizing Data in R and Python. In: Computing with Data. Springer, Cham; 2018.
21.
Hoyer S, Hamman J. Xarray: ND labeled arrays and datasets in Python. Journal of Open Research Software 2017; 5(1). https://dx.doi.org/10.5334/jor....
22.
Wan X, Song H, Luo L, Li Z, Sheng G, Jiang X. Pattern recognition of partial discharge image based on one-dimensional convolutional neural network. In 2018 Condition Monitoring and Diagnosis (CMD). IEEE. September; 2018; 1-4.
23.
Soto MA, Ramírez JA, Thévenaz L. Reaching millikelvin resolution in Raman distributed temperature sensing using image processing. In Sixth European Workshop on Optical Fibre Sensors International Society for Optics and Photonics. May 2016; 9916: 99162A.
24.
Han Y, Wang S, Wei J, Song C. Hole filling algorithm for image array of one-dimensional integrated imaging. In Optoelectronic Imaging and Multimedia Technology VI International Society for Optics and Photonics. November 2019; 11187: 111870K.
25.
Khamdamov U, Zaynidinov H. Parallel algorithms for bitmap image processing based on daubechies wavelets. In 2018 10th International Conference on Communication Software and Networks (ICCSN) IEEE. July; 2018, 537-541.
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