Open AccessReal‐time lossless ECG compression for low‐power wearable medical devices based on adaptive region prediction
Author(s) -
Li Ke,
Pan Yun,
Chen Fangjian,
Cheng KwangTing,
Huan Ruohong
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.3058
Subject(s) - lossless compression , computer science , waveform , data compression , wearable computer , compression ratio , code (set theory) , wearable technology , power (physics) , power consumption , electronic engineering , real time computing , artificial intelligence , embedded system , engineering , telecommunications , physics , quantum mechanics , radar , programming language , set (abstract data type) , automotive engineering , internal combustion engine
A real‐time lossless compression technique for ECG signals, which benefits wearable medical devices with stringent low‐power requirements, is presented. The real‐time ECG waveform is automatically classified into four regions according to its fluctuation features and the most suitable prediction method is adaptively selected from several linear prediction methods for different regions. Further proposed is the use of a modified variable length code to encode the prediction difference for a simpler transmit format. Experimental results based on three publically available test databases show that the proposed method achieves a better compression ratio with a lower prediction difference than existing state‐of‐the‐art approaches. A very large‐scale integration implementation is also demonstrated which can be used as an intellectual property core with a core area of 25 809 μm 2 and which achieves a power consumption of 127 μW at 100 MHz in a 0.18 μm CMOS technology.