Efficient Lossless Compression Scheme for Multi-channel ECG Signal

Tsai, Tsung‐Han; Tsai, Fong-Lin

doi:10.1109/icassp.2019.8683836

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…[11]- [14] also achieve relatively high CR. However, the K-means cluster of Zhou's method [11] needs to square each point when matching templates, and the Huffman codebook in Zhou's method contains 2048 items the compress the ECG signal of the ARRDB (which has 11-bit resolution), more than the variable number of the proposed S system; the Takagi-Sugeno Fuzzy Neural Network in [12] contains many multiply-accumulate operations when predicting each point; and both [13] and [14] need integer division operations. So the computational complexity of [11]- [14] are higher than that of the proposed method.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Zhou [11] K-means cluster Huffman encoding 2.93 ab Chua and Fang [5] Discrete pulse code modulation + Error modeling Golomb-Rice encoding 2.38 Chen and Wang [6] Adaptive linear predictor Two-stage Huffman encoding 2.43 Luo et al [7] Adaptive linear predictor Two-stage Huffman encoding 2.53 Li et al [8] Adaptive linear predictor Modified variable-length encoding 2.67 Deepu and Lian [3] Short term linear predictor Fixed-length encoding 2.28 Tseng et al [12] Takagi-Sugeno fuzzy neural network Arithmetic encoding 2.96 a Tsai and Kuo [9] Adaptive linear predictor Golomb-Rice encoding 2.835 Tsai and Tsai [13] Adaptive linear predictor Golomb-Rice encoding 2.89 Rzepka [14] Selective a These two references used 12-bit as the resolution of the ARRDB. We set the resolution to 11-bit and recalculated the CR for comparison.…”

Section: 068mentioning

confidence: 99%

“…Zhou [11] applied k-means cluster predictor + Huffman encoding, and Tseng et al [12] applied Takagi-Sugeno fuzzy neural network + arithmetic encoding, but they require multiply-accumulate operation many times when predicting each point. Tsai and Tsai [13] applied adaptive linear predictor + Golomb-Rice encoding, and Rzepka [14] applied selective and multichannel linear predictor + asymmetric numeral systems encoding, but they all require integer division operation hence increasing the computational complexity. Therefore, the methods of [10]- [14] are difficult to be implemented on wearable ECG monitoring devices.…”

Section: Introductionmentioning

confidence: 99%

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A Lossless Electrocardiogram Compression System Based on Dual-Mode Prediction and Error Modeling

Jia

et al. 2020

IEEE Access

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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.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: 068mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Lossless Electrocardiogram Compression System Based on Dual-Mode Prediction and Error Modeling

Jia

et al. 2020

IEEE Access

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“…Tsai and Kuo [3] proposed the adaptive linear prediction with content-adaptive Golomb-Rice coding. Tsai and Tsai [4] proposed the multi-channel adaptive linear prediction with a modified Golomb-Rice coding method, and Tsai et al [5] also implemented the method in [4] on Very Large-Scale Integration (VLSI). This work compressed the 12-lead ECG signal by using four reference leads to calculate the other eight leads of the signals.…”

Section: Introductionmentioning

confidence: 99%

Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction

Bannajak

Theera-Umpon

Auephanwiriyakul

2023

IJERPH

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In this paper, we propose a lossless electrocardiogram (ECG) compression method using a prediction error-based adaptive linear prediction technique. This method combines the adaptive linear prediction, which minimizes the prediction error in the ECG signal prediction, and the modified Golomb–Rice coding, which encodes the prediction error to the binary code as the compressed data. We used the PTB Diagnostic ECG database, the European ST-T database, and the MIT-BIH Arrhythmia database for the evaluation and achieved the average compression ratios for single-lead ECG signals of 3.16, 3.75, and 3.52, respectively, despite different signal acquisition setup in each database. As the prediction order is very crucial for this particular problem, we also investigate the validity of the popular linear prediction coefficients that are generally used in ECG compression by determining the prediction coefficients from the three databases using the autocorrelation method. The findings are in agreement with the previous works in that the second-order linear prediction is suitable for the ECG compression application.

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“…Zhang, R proposed a new three-phase power quality data compression method based on wavelet transform, which, combined with Lempel-Ziv-Welch (LZW) coding, achieves a good compression effect on power quality signal compression [11]. Tsai, T proposed a multi-channel lossless ECG compression algorithm that uses exponentially weighted multi-channel linear prediction and adaptive Golomb-Rice coding [12]. Alam, S proposed a DPCM-based threshold data compression technology for real-time remote monitoring applications, which uses simple calculation and a high compression ratio [13].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Lossless Compression Method for Periodic Signals Based on Adaptive Dictionary Predictive Coding

et al. 2020

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This paper reports on an efficient lossless compression method for periodic signals based on adaptive dictionary predictive coding. Some previous methods for data compression, such as difference pulse coding (DPCM), discrete cosine transform (DCT), lifting wavelet transform (LWT) and KL transform (KLT), lack a suitable transformation method to make these data less redundant and better compressed. A new predictive coding approach, basing on the adaptive dictionary, is proposed to improve the compression ratio of the periodic signal. The main criterion of lossless compression is the compression ratio (CR). In order to verify the effectiveness of the adaptive dictionary predictive coding for periodic signal compression, different transform coding technologies, including DPCM, 2-D DCT, and 2-D LWT, are compared. The results obtained prove that the adaptive dictionary predictive coding can effectively improve data compression efficiency compared with traditional transform coding technology.

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Efficient Lossless Compression Scheme for Multi-channel ECG Signal

Cited by 7 publications

References 9 publications

A Lossless Electrocardiogram Compression System Based on Dual-Mode Prediction and Error Modeling

A Lossless Electrocardiogram Compression System Based on Dual-Mode Prediction and Error Modeling

Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction

An Efficient Lossless Compression Method for Periodic Signals Based on Adaptive Dictionary Predictive Coding

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