Regressive Model Representation and Quantization Factor Prediction of ECG Data Compression

• Main Authors: ZHENG,DAO-HAN, 鄭道涵
• Other Authors: Hung,King-Chu
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/bnxf8z

碩士 === 國立高雄第一科技大學 === 電腦與通訊工程系碩士班 === 105 === In addition to Taiwan, Japan、South Korea have entered the aging society, and the national health、medical aspects of more and more attention. Disease prevention and monitoring will be the future trend. Electrocardiography(ECG) is one of the key projects in which the signal requires a long record and large amount of information. The popularity and development of wearing devices, ECG compression system has become an important issue. Including low power, device volume and data compression for the three major challenges. This architecture requires a large number of divisions, and the cost of the divider is too high. So ECG compression algorithm can not be directly converted into a hardware chip. This paper will optimize the quantization factor prediction part and successfully convert it into hardware architecture. The system is divided into three blocks : wavelet conversion, quantization and coding. In the compression process, after the completion of the first part of the wavelet transform, the signal into the quantification step. Because of ECG has medical and monitoring purposes, so its quality control is very important in the quantitative system.Therefore, in the software algorithm into a hardware architecture must also achieve the default quality management. In which predict the quantization factor operation, we need to calculate the quantization factor divided by SPRD2. In order to avoid the division, so we first define the quantization factor for the X axis, SPRD2 for the Y axis. And then use the coordinate axes to record all the quantization factors and SPRD2 at PRD( percentage rms difference , PRD) = 2%. According to the SPRD2 range to cut the classification, and apply the statistical regression analysis in each block. And produce the corresponding equation. This equation only contains multiplication and addition and can directly calculate the two parameters of the division.This method can solve the cost of the divider and avoid the storage of large amounts of data. The improved method of this study has been used 48 kinds of ECG database in the Matlab platform for testing, and all the signals can cross the default PRD = 2% quality control. Finally, we also propose the basic hardware architecture and synthetic data for predicting the quantization factor. Key words: ECG, data compression, PRD, regression analysis, quantization factor