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Title |
Neural Networks-Based Method for Electrocardiogram Classification |
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Author |
Maksym Kovalchuk, Viktoriia Kharchenko, Andrii Yavorskyi, Igor Bieda, and Taras Panchenko |
| Citation |
Vol. 23 No. 9 pp. 186-191 |
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Abstract |
Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches. |
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Keywords |
electrocardiogram, ECG, ECG classification, one-dimensional convolutional neural networks, 1D CNN, machine learning, artificial neural networks. |
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