Abstract
Long-term electrocardiogram (ECG) monitoring requires high-ratio lossless compression techniques to reduce data transmission energy and data storage capacity. In this paper, we have proposed a high-ratio ECG compression system with low computational complexity. Firstly, as the morphologies of the ECG change over time, we divide the signal of each heartbeat cycle into two regions. To achieve high prediction accuracy, a 1 st order linear predictor and a combination of the template predictor and 3 rd order linear predictor are applied in the two regions respectively. Secondly, we introduce a context-based error modeling module to the system, which cancels the statistical bias of the prediction algorithm and further improves the prediction accuracy. Thirdly, we modify the Golomb-Rice encoding algorithm to adaptively encode the prediction errors, while preserving a code space for packaging the information that is necessary for prediction. We evaluate the proposed system by using the MIT-BIH Arrhythmia Database (ARRDB). The experimental results show that with memory requirements as low as 444 to 14556 total variables this system achieves a compression ratio (CR) from 2.975 to 3.040, suggesting that it is highly applicable to both the low-power design and the cloud.
Highlights
Electrocardiogram (ECG) indicates the electrical activity of the heart and it is the most commonly used method to monitor the heartbeat
This study proposes a lossless ECG compression algorithm based on dual-mode prediction with context error modeling
compression ratio (CR) is used as the evaluation criteria for each ECG recording, which is calculated according to CR = Bo
Summary
Electrocardiogram (ECG) indicates the electrical activity of the heart and it is the most commonly used method to monitor the heartbeat. With wireless and wearable healthcare devices, long-term ECG data can be recorded continuously for monitoring and diagnosis. Collecting such a large amount of data requires excessive transmission energy or storage capacity, which significantly increases the cost of long-term ECG applications [1], [2]. An effective and efficient data compression method for ECG signals is required. ECG compression methods include lossless compression and lossy compression. The reconstructed ECG can be exactly the same as the original ECG, which is generally more useful for cardiac disease diagnosis. Because the lossy compression discards some morphological
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