计算机科学 ›› 2020, Vol. 47 ›› Issue (7): 118-124.doi: 10.11896/jsjkx.190600161
王文刀, 王润泽, 魏鑫磊, 漆云亮, 马义德
WANG Wen-dao, WANG Run-ze, WEI Xin-lei, QI Yun-liang, MA Yi-de
摘要: 针对日趋增长的心电图数据分析需求,提出了一种新的心电图分类算法。首先对原始数据进行截断固定长度、样本均衡、求取信号的瞬时频率和光谱熵等预处理操作,数据经过预处理后模型能够更好地从其中提取特征进行学习;在训练过程中采用两个双向LSTM(BILSIM)网络堆叠组成的模型,堆叠式的双向LSTM(BILSIM)模型是一种改进的循环神经网络模型,相较于卷积神经网络,循环神经网络更加适合用来处理像心电图这样的序列数据。该模型在Windows下的MATLAB2018b上进行训练和测试,CUDA版本为9.0,采用分类准确率作为衡量模型性能的指标在两个数据集上进行了测试,一个是2017年生理信号挑战赛的数据(下文简称2017数据集),该模型在此数据集上最终分类准确率为97.4%;另一个是2018年生理信号挑战赛的数据(下文简称2018数据集),最终的分类准确率为77.6%,并在所属的MATLAB组获得了第三名的成绩。该算法与传统LSTM网络的结果相比,在2017数据集上提升了5.6%的准确率,在2018数据集上提升了7.6%的准确率;与单层的双向LSTM网络的结果相比,在2017数据集上提升了4.2%的准确率,在2018数据集上提升了5.7%的准确率,这充分验证了该算法的可行性和优势。
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