An Efficient ECG Lossless Compression System for Embedded Platforms with Telemedicine Applications

Tsung Han Tsai, Wei Ting Kuo

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


This paper presents a method for wireless ECG compression and zero lossless decompression using a combination of three different techniques in order to increase storage space while reducing transmission time. The first technique used in the proposed algorithm is an adaptive linear prediction; it achieves high sensitivity and positive prediction. The second technique is content-adaptive Golomb-Rice coding, used with a window size to encode the residual of prediction error. The third technique is the use of a suitable packing format; this enables the real-time decoding process. The proposed algorithm is evaluated and verified using over 48 recordings from the MIT-BIH arrhythmia database, and it shown to be able to achieve a lossless bit compression rate of 2.83× in Lead V1 and 2.77× in Lead V2. The proposed algorithm shows better performance results in comparison to previous lossless ECG compression studies in real time; it can be used in data transmission methods for superior biomedical signals for bounded bandwidth across e-health devices. The overall compression system is also built with an ARM M4 processor, which ensures high accuracy performance and consistent results in the timing operation of the system.

Original languageEnglish
Article number8418357
Pages (from-to)42207-42215
Number of pages9
JournalIEEE Access
StatePublished - 21 Jul 2018


  • Electro-cardiogram (ECG)
  • Golomb-Rice coding
  • healthcare monitoring
  • lossless data compression
  • telemedicine
  • wearable devices


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