A novel ECG signal compression method using spindle convolutional auto-encoder

Fei Wang; Qiming Ma; Wenhan Liu; Sheng Chang; Hao Wang; Jin He; Qijun Huang

doi:10.1016/j.cmpb.2019.03.019

A novel ECG signal compression method using spindle convolutional auto-encoder

Comput Methods Programs Biomed. 2019 Jul:175:139-150. doi: 10.1016/j.cmpb.2019.03.019. Epub 2019 Apr 17.

Authors

Fei Wang¹, Qiming Ma², Wenhan Liu¹, Sheng Chang¹, Hao Wang¹, Jin He¹, Qijun Huang³

Affiliations

¹ School of Physics and Technology, Wuhan University, Wuhan 430072, China.
² Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: maqiming@mail.iee.ac.cn.
³ School of Physics and Technology, Wuhan University, Wuhan 430072, China. Electronic address: huangqj@whu.edu.cn.

PMID: 31104703
DOI: 10.1016/j.cmpb.2019.03.019

Abstract

Background and objectives: With rapid development of telehealth system and cloud platform, traditional 12-ECG signals with high resolution generate heavy burdens in data storage and transmission. This problem is increasingly addressed with various ECG compression methods. The important objective of compression method is to achieve a high-ratio and quality guaranteed compression. Consequently, to achieve this objective, this work presents a deep-learning-based spindle convolutional auto-encoder. The spindle structure achieves the high-ratio compression by reducing the dimension and guarantees the quality by increasing the dimension and end-to-end framework.

Methods: The spindle convolutional auto-encoder provides a high-ratio and quality-guaranteed ECG compression. It is composed of two parts as convolutional encoder and convolutional decoder with functional layers. By convolutional operation, the local information can be extracted. The spindle structure is increasing dimension in first few layers to obtain sufficient information to guarantee compression quality. And it is reducing dimension in last few layers to merge the information into a code for high-ratio compression. Meanwhile, the end-to-end framework is to obtain the optimum encoding for compression to improve the reconstruction performance.

Results: Compression performance is validated with records from MIT-BIH database. The proposed method achieves high compression ratio of 106.45 and low percentage root mean square difference of 8.00%. Compared with basic convolutional auto-encoder, the spindle structure improves the compression quality with lower losses.

Conclusions: The spindle convolutional auto-encoder performs a high-ratio and quality-guaranteed compression. It can be considered as a promising compression technique used in tele-transmission and data storage.

Keywords: Biomedical signal processing; Electrocardiogram (ECG); Signal compression; Spindle convolutional auto-encoder (SCAE); Telehealth.

MeSH terms

Algorithms
Data Compression / methods*
Deep Learning*
Electrocardiography*
Humans
Models, Statistical
Signal Processing, Computer-Assisted*
Software
Stochastic Processes
Telemedicine