动态时间归整
计算机科学
人工智能
模式识别(心理学)
人工神经网络
深度学习
核(代数)
深层神经网络
领域(数学)
缩小
机器学习
数学
组合数学
纯数学
程序设计语言
作者
Mohammad Ahmadi-Mobarakeh,Hoda Mohammadzade
标识
DOI:10.1109/icbme54433.2021.9750288
摘要
Using Time Series Classification (TSC) methods in the study of biological signals like ECG for detecting unusual behavior is one of the most important applications of this field. With this motivation, we used kernel layer(s), as a novel approach, at the beginning of the common deep neural networks. These kernels have been trained based on Dynamic Time Warping (DTW) distance minimization. This new method has tested on two ECG datasets from UCR datasets: ECG200 and ECG5000 to classifying them. We got 91% and 92.3% accuracy for these datasets respectively, which is the best accuracy for ECG200 against other deep and non-deep methods and is an acceptable rate for ECG5000. Beside these results, the best achievement is the very low training time and also simplicity of the proposed network compared to other networks.
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