Real-Time Analysis and Processing of Cardiogram Signals
DOI:
https://doi.org/10.17721/1812-5409.2021/1.14Keywords:
random forest, electrocardiogram, ECG, healthcare, health monitoring, real-time ECG analysis, ECG features, effective data processingAbstract
Analysis of Electrocardiogram (ECG) signals is an important task to save and enhance human life because a major cause of death is heart disease and the consequences. In many cases, early diagnostics of such problems can save and prolong life.
In this work, we develop and present an approach to the real-time detection of Atrial Fibrillation (AF) Arrhythmia, which is a common cardiac arrhythmia affecting a large number of people. Being undetected, it develops into chronic disability or even early mortality. At the same time, This disease is hard to diagnose, especially in its early stage. A real-time automatic and non-invasive effective detection is needed to help diagnose this kind of health problem early. In-time medical intervention can save human life. ECG as a record of the heart electrical activity is widely used for detecting different heart disabilities. At the same time, AF is hard to detect due to its non-regular nature, and also because the performance of detection models depends largely on the quality of data and careful feature engineering.
The research is based on the dataset from PhysioNet Computing in Cardiology Challenge 2017. It contains 8528 single-lead ECG recordings of short-term heart rhythms (9-61 sec.). Our method and the trained model reach the known state-of-the-art results in this field, but, at the same time, it is much less computationally intensive, and, thus, less power consumptive to be implemented in an embedded device.
Pages of the article in the issue: 108 - 113
Language of the article: Ukrainian
References
PANCHENKO, T.V., BUDICHENKO, V.O. (2016): Real-time Health Monitoring via ECG Analysis, “Artificial Intelligence”, No. 4 (74), pp. 98-100.
CLIFFORD, G.D., LIU, Ch., MOODY, B., LEHMAN, L.H., SILVA, I., LI, Q., JOHNSON, A.E., MARK, R.G. (2017): AF Classification from a Short Single Lead ECG Recording: The PhysioNet – Computing in Cardiology Challenge 2017, “Computing in Cardiology”, pp.1-4, doi: 10.22489/CinC.2017.065-469.
GOLDBERGER, A., AMARAL, L., GLASS, L., HAUSDORFF, J., IVANOV, P.C., MARK, R., MIETUS, J.E., MOODY, G.B., PENG, C.K., STANLEY, H.E. (2000): PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals, # 101 (23), pp.e215–e220, https://physionet.org/content/challenge-2017
CHEN, D., LI, D., XU, X., YANG, R., NG, S.-K. (2021): Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG, 10 p., https://arxiv.org/pdf/2101.07535.pdf
HAMILTON, P. (2002): Open source ECG analysis, “Computers in Cardiology”, pp. 101-104, doi: 10.1109/CIC.2002.1166717.
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