VLSI implementation of a lossless ECG encoder design with fuzzy decision and two-stage Huffman coding for wireless body sensor network

Luo, Guei-An; Chen, Shih-Lun; Lin, Ting-Lan

doi:10.1109/icics.2013.6782955

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Average CR [3] ALP + Context-based Golomb-Rice Coding 2.835 [4] ALP + Golomb-Rice Coding 2.89 [6] SSLMS ALP + Savitzky-Golay Filter 2.15 [7] ALP + Fixed-length Coding 2.25 [8] LP + Burrow-Wheeler Inversion Coding 4.24 [9] Fuzzy-based ALP + 2-stage Huffman Coding 2.53 [10] Dual-mode LP + Error Modeling + Golomb-Rice Coding 2.975-3.040 [11] Takagi-Sugeno fuzzy NN Predictive Coding 3.22 [12] Wavelet-based Vector Quantization 3.031 [14] RAKE Coding Algorithm 2.67 [15] K-means Cluster + Differential Coding 2.49 (12-bit Length) Proposed Prediction Error-based ALP + Golomb-Rice Coding 3.525…”

Section: Ref Compression Methodsmentioning

confidence: 99%

“…Arnavut [8] introduced the Burrow-Wheeler transform with the autoregressive (AR) model, the Move-to-front coder, and the Inversion ranks coder with the linear prediction compression method. Luo et al [9] proposed the adaptive linear prediction based on a fuzzy decision with 2-stage Huffman coding implemented on VLSI. Jia et al [10] proposed the dual-mode linear prediction compression method with context-based error modeling and modified Golomb-Rice coding.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Ref Compression Methodsmentioning

confidence: 99%

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 recent years, numerous lossless encoding algorithms have been proposed and tested on ECG signals. In one study [5], an adaptive linear prediction and two-stage Huffman coding approach was used to compress ECG signals. Another study [6] used an adaptive region prediction and variable length coding method.…”

Section: Introductionmentioning

confidence: 99%

Lossless real-time signal encoding for two-channel signals: A case study on ECG

Kromka,

Kovac,

Saliga

2023

Proceedings of the 26th IMEKO TC4 International Symposium and 24th International Workshop on ADC/DAC Modelling and Testing

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This paper introduces a new encoding algorithm for the lossless, fast, and memory-efficient compression of two-channel signals. The proposed algorithm was evaluated using Electrocardiogram (ECG) signals. The results indicate that it can effectively compress ECG signals with a favorable compression ratio (CR), without relying on complex predictors, dictionaries, detectors, or additional encoding methods. By employing this method, an average CR of 1.98 was achieved, with a minimum CR of 1.54 and a maximum CR of 2.36. Moreover, it is assumed that the algorithm has the potential to enhance the achieved compression in Compressed Sensing (CS) systems.

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Lossless ECG Encoder with Multi-Channel Adaptive Fuzzy Predictor and Enhanced Entropy Encoding in Wireless Body Sensor Networks

Principe

Arada

Abad

et al. 2021

J. Phys.: Conf. Ser.

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With the advancement of wireless technology, Wireless Body Sensor Networks, such as Electrocardiograms (ECGs) will serve as state-of-the-art method for electronic-healthcare systems and applications. Like other digital communications, however, ECGs highlight power consumption as the main design constraint and bottleneck as it affects device lifespan and data accuracy. Hence, power reduction and power management techniques and schemes have been developed to eliminate this constraint such as hardware optimization, source and channel coding, signal conditioning, and resolution control. This paper proposes a lossless ECG encoder that combines existing data compression techniques specifically the adaptive fuzzy predictor based on fuzzy decision making and an enhanced entropy encoding that utilizes algorithms in both run length encoding (RLE) and incremental prefix encoding. Simulation results that the proposed scheme outperforms the entropy encoding using Huffman, RLE, and predictive encoding schemes in compression ratio (CR), with the enhanced entropy encoding leading the pre-existing compression techniques by 24.0907 on RLE and 23.6580 on Huffman.

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VLSI implementation of a lossless ECG encoder design with fuzzy decision and two-stage Huffman coding for wireless body sensor network

Cited by 6 publications

References 7 publications

Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction

Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction

Lossless real-time signal encoding for two-channel signals: A case study on ECG

Lossless ECG Encoder with Multi-Channel Adaptive Fuzzy Predictor and Enhanced Entropy Encoding in Wireless Body Sensor Networks

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