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Nan Fang Yi Ke Da Xue Xue Bao. 2021 Feb 20; 41(2): 279–284.
PMCID: PMC7905255

Language: Chinese | English

一种新的胎心率信号压缩方法——卷积编解码网络

A new lossy compression method for fetal heart rate signals—Convolutional Codec Network

阙 与清

南方医科大学生物医学工程学院,广东 广州 510515, College of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China

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陈 定科

南方医科大学生物医学工程学院,广东 广州 510515, College of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China

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童 蕾

广东机电职业技术学院,广东 广州 510550, Guangdong Vocational College of Mechanical and Electrical Technology, Guangzhou 510550, China

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陈 超敏

南方医科大学生物医学工程学院,广东 广州 510515, College of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China 南方医科大学生物医学工程学院,广东 广州 510515, College of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China 广东机电职业技术学院,广东 广州 510550, Guangdong Vocational College of Mechanical and Electrical Technology, Guangzhou 510550, China - T q

一般来说,压缩比越低,重构误差越小,压缩方法越有效。然而,本研究是关于物联网的数据压缩,希望减少数据传输的时间和能量消耗。因此,有必要考虑算法时间和节省的传输时间,以及内存的损失。

2. 结果

由于不同的网络结构会有不同的压缩比,不同的压缩比会有不同的重构误差和不同的算法时间,因此本研究需选择合适的压缩比,以确保重构误差和重构时间是最优的,测试了不同压缩比的结果( 表 1 )。

1

不同压缩比的结果

Results of compression at different compression ratios

CR(%) RE Algorithm times(s) Time saved by transmission (s)
86.7 0.54 1.8 1.3
86.7 0.54 1.8 1.3
24.77 0.24 2.1 3.4
24.17 0.12 2.3 5.1
12.07 0.03 2.4 7.7
6.00 0.19 2.6 8.4

当压缩值相对较高时,由于卷积层的数量相对较少,算法的运行时间较短,但重构误差相对较高。随着压缩比的降低,算法运行时间增加,但数据传输时间节省更多,重构误差也显著降低。因此,本文选择压缩比为 equation M5 的网络结构。通过对压缩网络的训练,在训练集中经过10个epoch后,重建信号与原始信号的归一化误差为0.02。对于测试集数据,重构信号与原始信号的归一化误差为0.03。

本研究给出了测试集中的原始数据和重构数据( 图 7 ),结果显示重构信号与原始信号虽然几乎相同,有细微的差别,但有用的形态学特征信息完全保留。从压缩比和重构相似度来看,本研究的压缩网络表现良好。然而,这种方法是否有利于物联网中的数据传输,还需要比较算法所损失的时间和传输所节省的时间。在训练过程中,每个epoch算法持续时间为13.2 s,测试集包含1/4的数据,运行时间为2.4 s,它低于数据传输时间。一开始,每个数据长度为3000,字节数为12 000,数据传输时间为10.2 s。经过压缩,数据长度为362,字节数为2896,数据传输时间为2.5 s。因此,数据传输时间节省了7.7 s,大大低于算法的运行时间。

An external file that holds a picture, illustration, etc. Object name is nfykdxxb-41-2-279-7.jpg

原始数据和重构数据

Original data and reconstructed data.

3. 讨论

物联网系统中,数据传输是最重要也是最耗时的。在基于物联网的医疗系统中,我们希望数据能够实时传输,设备的耐久力更强,也就是能量损失更少。无损压缩方法可以保证解压缩后的数据与原始数据相同,但压缩比非常高,这与我们在传感器系统中使用数据压缩的初衷不一致。有研究对心电信号或其他生理信号提出了一些有损压缩方法,但很少用于胎儿心率信号,且主要是基于小波变换、压缩感知等方法 [ 11 - 12 ] 。基于变换的压缩技术的缺点是需要先将原始数据转换成一组系数,然后遵循熵编码步骤对系数进行编码,以获得可接受的压缩率。基于压缩感知的方法对信号的稀疏性有要求,而且信号的噪声分量对压缩影响非常大,算法计算复杂度很高。对于小波变换的方法,缺点主要是自适应能力弱,小波基的选择以及量化阈值的选择对压缩性能影响大,计算复杂度也较高。

有研究对于人体一维的生理信号数据的压缩进行探讨 [ 21 - 26 ] ,这些方法是基于小波变换或者压缩感知,均取得了不错的压缩效果,但算法的运行时间比较长,即使缩短了数据的传输时间,却增加了数据的解压时间。而本文提出的方法在保证较小的重构误差时,有较低的压缩率,并且算法的时间也较低,对于物联网数据实时性有很大的提高。

本文提出的基于卷积网络的压缩方法,由于卷积有强大的表征学习能力,所以该方法的自适应能力强,而且可以通过内部参数的调节使重构误差达到很小。此外,该方法训练完成后在测试集上的的压缩和解压时间短、压缩率低,可以有效的减轻物联网数据传输和存储压力。该方法需要足够的数据量,数据量增大,模型的复杂度也会增大,所以训练时间也会增加。但训练完模型后,测试集不需要消耗更多的时间。该方法改变压缩率是通过改变网络的卷积层和pooling层的层数来调节的。当网络层数低时,它的压缩率高,自适应学习能力弱,所以导致重构误差也很高。随着网络层数的增加,它的压缩率降低的同时,网络的拟合能力强,因此重构误差也随之降低。但是网络比较深,数据量不算很大时,再增加网络层数会发生过拟合。所以我们需要选取合适的网络层数,即合适的压缩率。

综上所述,本文提出了一种基于卷积编解码网络的数据压缩方法,包括编码和解码两个模块,通过最小化原始数据与重构数据之间的误差来训练模型。该方法压缩胎儿心率信号的压缩比为12.07%,测试集的重构误差可达0.03,说明压缩方法是有效的。此外,该算法时间消耗小,可以减少物联网中数据的传输时间,从而可以实时获取胎儿的状态。

Biography

阙与清,硕士,E-mail: moc.qq@4913674561

Funding Statement

国家重点研发计划项目(2019YFC0118805,2019YFC0120103)

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