Zhang Bo, Zhao Jiasheng, Chen Xiao, Wu Jianhuang
Department of Ultrasound in Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
PLoS One. 2017 Oct 3;12(10):e0182500. doi: 10.1371/journal.pone.0182500. eCollection 2017.
Electrocardiogram (ECG) data analysis is of great significance to the diagnosis of cardiovascular disease. ECG compression should be processed in real time, and the data should be based on lossless compression and have high predictability. In terms of the real time aspect, short-time Fourier transformation is applied to the processing of signal wave for reducing computational time. For the lossless compression requirement, wavelet-transformation that is a coding algorithm can be used to avoid loss of data. In practice, compression is required to avoid storing redundant recording data that are not useful in the diagnosis platform. The obtained data can be preprocessed to remove noise by using wavelet transform, and then a multi-objective optimize neural network model is used to extract feature information. Compared with the existing traditional methods such as direct data processing method and transform method, our proposed compression model has self-learning ability to achieve high data compression ratio at 1:19 without losing important ECG information and compromising quality. Upon testing, we demonstrated that the proposed ECG data compression method based on multi-objective optimization neural network is effective and efficient in clinical practice.
心电图（ECG）数据分析对心血管疾病的诊断具有重要意义。ECG压缩应实时进行，数据应基于无损压缩且具有高预测性。在实时性方面，将短时傅里叶变换应用于信号波处理以减少计算时间。对于无损压缩要求，可使用作为编码算法的小波变换来避免数据丢失。在实际应用中，需要进行压缩以避免在诊断平台上存储无用的冗余记录数据。所获得的数据可通过小波变换进行预处理以去除噪声，然后使用多目标优化神经网络模型提取特征信息。与现有的传统方法如直接数据处理方法和变换方法相比，我们提出的压缩模型具有自学习能力，能够在不丢失重要ECG信息和不降低质量的情况下实现1:19的高数据压缩率。经测试，我们证明了基于多目标优化神经网络的ECG数据压缩方法在临床实践中是有效且高效的。
