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首页> 外文会议>International Conference on Artificial Intelligence IC-AI'2000 Vol.3, Jun 26-29, 2000, Las Vegas, Nevada, USA >Cascaded Neural Network For Classification of Artficially Modeled ECG Beats Using Error Signal
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Cascaded Neural Network For Classification of Artficially Modeled ECG Beats Using Error Signal

机译：级联神经网络用于使用误差信号对人为建模的ECG搏动进行分类

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摘要

This paper proposes a potential cascaded neural network for ECG Holter system beat classification. The system works in real time and is capable of recognizing up to 20 artificial QRS templates. The parallelism of neural network increases the efficiency of computations. An error signal derived from differences between predictor and testing signal is used in the classification. A neural network is used to generate linear predictions for signals. Another neural network generates error signals measured between predictions taken from, first neural network and testing signal. A third neural network does the classifications utilizing the error signals instead of complex raw signal. Three Her-mite functions are used in generating testing signals with, and without noise. Proper thresholding for the error signals are essential for classifier immunity. The result is a compact, online, efficient, and hardware realizable signals classifier that uses minimal compressed error signal.
机译：本文提出了一种潜在的级联神经网络，用于心电图动态心电图心跳分类。该系统实时工作，能够识别多达20个人工QRS模板。神经网络的并行性提高了计算效率。从预测信号和测试信号之间的差异得出的误差信号将用于分类。神经网络用于生成信号的线性预测。另一个神经网络生成在从第一神经网络获得的预测和测试信号之间测得的误差信号。第三神经网络利用误差信号而不是复杂的原始信号进行分类。三个Her-mite功能用于生成有噪声和无噪声的测试信号。正确的错误信号阈值对于分类器抗扰性至关重要。结果是使用最小的压缩误差信号的紧凑，在线，高效且可硬件实现的信号分类器。

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