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首页> 外文会议>2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing >Deep learning algorithm for arrhythmia detection
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Deep learning algorithm for arrhythmia detection

机译：心律失常检测的深度学习算法

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摘要

Most of cardiovascular disorders or diseases can be prevented, but death continues to rise due to improper treatment because of misdiagnose. One of cardiovascular diseases is Arrhythmia. It is sometimes difficult to observe electrocardiogram (ECG) recording for Arrhythmia detection. Therefore, it needs a good learning method to be applied in the computer as a way to help the detection of Arrhythmia. There is a powerful approach in Machine Learning, named Deep Learning. It starts to be widely used for Speech Recognition, Bioinformatics, Computer Vision, and many others. This research used the Deep Learning to classify the Arrhythmia data. We compared the result to other popular machine learning algorithm, such as Naive Bayes, K-Nearest Neighbor, Artificial Neural Network, and Support Vector Machine. Our experiment showed that Deep Learning algorithm achieved the best accuracy, which was 76,51%.
机译：可以预防大多数心血管疾病或疾病，但是由于误诊导致治疗不当，死亡率继续上升。心律失常是心血管疾病之一。有时很难观察到心电图（ECG）记录以进行心律失常检测。因此，需要一种良好的学习方法在计算机中应用，以帮助检测心律失常。机器学习中有一种功能强大的方法，称为深度学习。它开始广泛用于语音识别，生物信息学，计算机视觉和许多其他领域。该研究使用深度学习对心律失常数据进行分类。我们将结果与其他流行的机器学习算法进行了比较，例如朴素贝叶斯，K最近邻，人工神经网络和支持向量机。我们的实验表明，深度学习算法达到了最高的准确性，达到了76.51％。

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