An ECG data compression method via R-Peak detection and ASCII Character Encoding

TLDR: A software based effective ECG data compression algorithm is proposed and the data reconstruction algorithm has also been developed using the reversed logic and it is seen that data is reconstructed preserving the significant ECG signal morphology.

Abstract: Efficient and reliable electrocardiogram (ECG) compression system can increase the processing speed of real-time ECG transmission as well as reduce the amount of data storage in long-term ECG recording. In the present paper, a software based effective ECG data compression algorithm is proposed. The whole algorithm is written in C- platform. The algorithm is tested on various ECG data of all the 12 leads taken from PTB Diagnostic ECG Database (PTB-DB). In this compression methodology, all the R-Peaks are detected at first by differentiation technique and QRS regions are located. To achieve a strict lossless compression in QRS regions and a tolerable lossy compression in rest of the signal, two different compression algorithms have developed. In lossless compression method a difference array has been generated from the corresponding input ECG “Voltage” values and then those are multiplied by a considerably large integer number to convert them into integer. In the next step, theses integer numbers are grouped in both forward and reverse direction maintaining some logical criteria. Then all the grouped numbers along with sign bit and other necessary information (position of critical numbers, forward/reverse grouping etc.) are converted into their corresponding ASCII characters. Whereas in lossy area, first of all, the sampling frequency of the original ECG signal is reduced to one half and then, only the “Voltage” values are gathered from the corresponding input ECG data and those are amplified and grouped only in forward direction. Then all the grouped numbers along with sign bit and other necessary information are converted into their corresponding ASCII characters. It is observed that this proposed algorithm can reduce the file size significantly. The data reconstruction algorithm has also been developed using the reversed logic and it is seen that data is reconstructed preserving the significant ECG signal morphology.