NDLI: Data compression for implantable medical devices

Content Provider	IEEE Xplore Digital Library
Author	Koyrakh, L.A.
Copyright Year	2008
Description	Author affiliation: St. Jude Med., St. Paul, MN (Koyrakh, L.A.)
Abstract	Implantable devices have limited memory, computational and battery power resources, while collecting, processing and transmitting out information from potentially many sensors. These limitations require that information within the devices be efficiently compressed. Such data compression presents a challenging task, as it must provide high fidelity of the waveform reproduction and high compression ratios on limited size data frames. Also, it must efficiently run on ultra low power hardware, and allow flexible configuration, based on the type of data to be compressed. The new compression algorithm was implemented as a bit accurate Matlab simulation, consisting of the following major steps: 1. Integer wavelet transform. 2. Quantization coupled with filtration. Two selectable quantization schemes could be utilized based on the signal properties: linear and dead-zone. Data filtration is performed on bit boundaries, which simplifies hardware implementation. The filtration thresholds are made different in different wavelet sub-bands, controlled by a single parameter. 3. Original adaptive data encoding. Our approach only requires basic logical operations such as bit counters and shifts, and is highly optimized for implementation in implantable device hardware. For high reliability each compressed data frame contains all information needed for decompression. The algorithm was applied to data from the PhysioNet ECG compression test database. 40 ECG frames of 1024 samples from 4 patients were sampled at 250 Hz, 12 bit resolution, and compressed with distortions below 8%. Compression ratios were 9.3plusmn2.5, consistently exceeding 85% of the theoretical limit based on bit entropy for each individual data frame. It is concluded The compression algorithm is efficient on data collected by implantable devices, and could be used in various applications in both microprocessor and ASIC implementations, helping to reduce memory requirements and the battery energy spent on the information transmission to and from the implantable device.
Starting Page	417
Ending Page	420
File Size	84923
Page Count	4
File Format	PDF
ISBN	9781424437061
ISSN	02766547
DOI	10.1109/CIC.2008.4749067
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-14
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Computers in Cardiology(CinC)
Subject Keyword	Data compression Implantable biomedical devices Hardware Filtration Batteries Compression algorithms Quantization Electrocardiography Wavelet transforms Encoding
Content Type	Text
Resource Type	Article

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