NDLI: Real-time lossless ECG compression for low-power wearable medical devices based on adaptive region prediction

Content Provider	IET Digital Library
Author	Li, Ke Pan, Yun Chen, Fangjian Cheng, Kwang Ting Huan, Ruohong
Abstract	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 μm2 and which achieves a power consumption of 127 μW at 100 MHz in a 0.18 μm CMOS technology.
Starting Page	1904
Ending Page	1906
Page Count	3
ISSN	00135194
Volume Number	50
e-ISSN	1350911X
Issue Number	Issue 25, Dec (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/50/25
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2014.3058
Journal	Electronics Letters
Publisher Date	2014-12-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Region Prediction Bioelectric Signal Biology And Medical Computing Biomedical Electronics Brownian Motion CMOS Integrated Circuit CMOS Technology Compression Ratio Digital Signal Processing ECG Signal Electrical Activity in Neurophysiological Processes Electrocardiography Electrodiagnostics And Other Electrical Measurement Technique Encoding Feature Extraction Fluctuation Feature Fluctuation Phenomena Fluctuations Frequency 100 MHz Function Theory And Analysis Intellectual Property Core Large-scale Integration Implementation Linear Prediction Method Low-power Wearable Medical Device Medical Signal Processing Modified Variable Length Code Power 127 MuW Power Consumption Prediction Difference Encoding Publically Available Test Database Random Processes Real-time ECG Waveform Classification Real-time Lossless ECG Compression Signal Classification Signal Processing And Detection Simpler Transmit Format Size 0.18 Mum Stringent Low-power Requirements Waveform Analysis
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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