Iwata A, Nagasaka Y, Suzumura N
Dept. of Electr. and Comput. Eng., Nagoya Inst. of Technol., Showa.
IEEE Eng Med Biol Mag. 1990;9(3):53-7. doi: 10.1109/51.59214.
A data-compression algorithm for digital Holter recording using artificial neural networks (ANNs) is described. A three-layer ANN that has a hidden layer with a few units is used to extract features of the ECG (electrocardiogram) waveform as a function of the activation levels of the hidden layer units. The number of output and input units is the same. The backpropagation algorithm is used for learning. The network is tuned with supervised signals that are the same as the input signals. One network (network 1) is used for data compression and another (network 2) is used for learning with current signals. Once the network is tuned, the common waveform features are encoded by the interconnecting weights of the network. The activation levels of the hidden units then express the respective features of the waveforms for each consecutive heartbeat.
描述了一种使用人工神经网络（ANN）进行数字动态心电图记录的数据压缩算法。一个具有少量单元的隐藏层的三层人工神经网络用于提取心电图（ECG）波形的特征，该特征是隐藏层单元激活水平的函数。输出单元和输入单元的数量相同。反向传播算法用于学习。该网络使用与输入信号相同的监督信号进行调谐。一个网络（网络1）用于数据压缩，另一个网络（网络2）用于利用当前信号进行学习。一旦网络被调谐，通用波形特征就由网络的互连权重进行编码。然后，隐藏单元的激活水平表示每个连续心跳的波形各自的特征。
