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

Li, Ké; Pan, Yun; Chen, Fangjian; Cheng, Kwang-Ting; Huan, Ruohong

doi:10.1049/el.2014.3058

Cited by 41 publications

(25 citation statements)

References 5 publications

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“…Additionally, the R peak is the basis of the detection of other features. We used the slope threshold detection method for QRS recognition [28], of which the block diagram is presented in Figure 10. Searching the local minimum points forwards and backwards from the R wave, we could find the adjacent Q and S points, respectively.…”

Section: Methodsmentioning

confidence: 99%

Optimized Electrode Locations for Wearable Single-Lead ECG Monitoring Devices: A Case Study Using WFEES Modules Based on the LANS Method

Zhu

Pan

Wu³

et al. 2019

Sensors

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Body surface potential mapping (BSPM) is a valuable tool for research regarding electrocardiograms (ECG). However, the BSPM system is limited by its large number of electrodes and wires, long installation time, and high computational complexity. In this paper, we designed a wearable four-electrode electrocardiogram-sensor (WFEES) module that measures six-channel ECGs simultaneously for ECG investigation. To reduce the testing lead number and the measurement complexity, we further proposed a method, the layered (A, N) square-based (LANS) method, to optimize the ECG acquisition and analysis process using WFEES modules for different applications. Moreover, we presented a case study of electrode location optimization for wearable single-lead ECG monitoring devices using WFEES modules with the LANS method. In this study, 102 sets of single-lead ECG data from 19 healthy subjects were analyzed. The signal-to-noise ratio of ECG, as well as the mean and coefficient of variation of QRS amplitude, was derived among different channels to determine the optimal electrode locations. The results showed that a single-lead electrode pair should be placed on the left chest above the electrode location of standard precordial leads V1 to V4. Additionally, the best orientation was the principal diagonal as the direction of the heart’s electrical axis.

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Section: Methodsmentioning

confidence: 99%

Optimized Electrode Locations for Wearable Single-Lead ECG Monitoring Devices: A Case Study Using WFEES Modules Based on the LANS Method

Zhu

Pan

Wu³

et al. 2019

Sensors

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show abstract

“…c (x [n]) is the category index of the n th point. Q (∆x [n]) is the binarized differences of the n th point, and the formula is given in (8).…”

Section: B Context-based Error Modelingmentioning

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%

“…Conventionally, the lossless ECG compression technique first predicts or transforms the signal to reduce the signal entropy, then entropy encodes the signal to remove redundant bits [4]. To date, various lossless ECG compression methods have been proposed, including algorithms such as data pulse code mod-ulation (DPCM) + Golomb-Rice encoding [5], adaptive linear predictor + modified Huffman encoding [6], [7], adaptive linear predictor + variable-length encoding [8], short term linear predictor + fixed-length encoding [3], and adaptive linear predictor + Golomb-Rice encoding [9]. These algorithms are relatively simple, but they do not utilize sufficient ECG morphologies hence causing poor CRs.…”

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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“…However, low frequency ultra low power electronic device have critical applications, e.g. in medical electronics [29]- [31]. To this end, the proposed technique is well suited for such applications owing to their low power requirements (Table II and Section V).…”

Section: High Frequency Operationsmentioning

confidence: 99%

Novel techniques for memristive multifunction logic design

et al. 2019

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We present novel techniques for realising reliable low overhead logic functions and more complex systems based on the switching characteristics of memristors. Firstly, we show that memristive circuits have inherent properties for realising multiple valued MIN-MAX operations over the post algebra. We then present an efficient hybrid 1T-4M logic architecture for dual XOR/AND and XNOR/OR functionality, which can be seamlessly integrated with the existing CMOS technology. Although memristors are usually considered to operate at lower frequencies, however, recent advances in technology show their potentiality at high frequencies. To this end, we also explore the effects of high frequencies on their performance and thereby propose reliable high frequency design techniques based on our 1T-4M architectures. Experimental results, based on the design of full adders and multipliers over GF, show that the proposed designs require significantly lower power and overhead while maintaining reliable performance at low as well as at high frequencies compared to the existing techniques.

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

Cited by 41 publications

References 5 publications

Optimized Electrode Locations for Wearable Single-Lead ECG Monitoring Devices: A Case Study Using WFEES Modules Based on the LANS Method

Optimized Electrode Locations for Wearable Single-Lead ECG Monitoring Devices: A Case Study Using WFEES Modules Based on the LANS Method

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

Novel techniques for memristive multifunction logic design

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