Mixed bio-signal lossless data compressor for portable brain-heart monitoring systems

Chua, Ericson; Fang, Wai-Chi

doi:10.1109/tce.2011.5735512

Cited by 83 publications

(68 citation statements)

References 10 publications

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“…Our method shows slightly lower CR than that of [12] and higher than [1,11,13], as presented in Table III. Complicated coding methods such as Golomb-Rice Coding and Huffman coding are used in [1,11,12], resulting in high hardware cost. While the adaptive length encoder proposed in this work is much simpler and efficient.…”

Section: Experiments Resultsmentioning

confidence: 81%

“…Linear predicting is a useful method for lossless compression [1,9,11]. The main idea is to predict the current sampled data from a linear combination of past samples.…”

Section: Linear Predictormentioning

confidence: 99%

“…Device for such applications has a strict demand for energy efficiency, as it must be small and wireless for portability, which leads to limited battery supply. Since the wireless transceiver dominates the system power for wearable sensor devices, researches have been focused on reducing transmission power, such as by data compression [1]. Another solution is to extract vital information out of ECG signals, such as QRS complex [2].…”

Section: Introductionmentioning

confidence: 99%

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A dual-mode ECG processor with difference-insensitive QRS detection and lossless compression

Luo

Chen

Xiang

et al. 2017

IEICE Electron. Express

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This work presents a low power dual-mode electrocardiogram (ECG) processor with QRS detection and ECG signal lossless compression. An adaptive difference-insensitive filter is proposed to eliminate redundant information in signal for QRS detection, which helps minimize calculation power. It is also adopted as a noise estimator and used to improve the noise tolerance of the detector. Furthermore, in compression mode, linear predictor with a novel adaptive length encoder is applied to the ECG data lossless compression, achieving a compression ratio (CR) of 2.42 with 1.06 K gate count. Implemented in 40 nm CMOS technology, the processor has a total core area of 6806 µm 2 , with 36 nW power consumption in detection mode and 7.3 nW in compression mode under a supply voltage of 0.5 V.

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Section: Experiments Resultsmentioning

confidence: 81%

“…Linear predicting is a useful method for lossless compression [1,9,11]. The main idea is to predict the current sampled data from a linear combination of past samples.…”

Section: Linear Predictormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A dual-mode ECG processor with difference-insensitive QRS detection and lossless compression

Luo

Chen

Xiang

et al. 2017

IEICE Electron. Express

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show abstract

“…For instance this is the case of IoT devices developed for biomedical and health-related signals where it is necessary to ensure that medically important details are not lost causing errors in medical diagnosis [6].…”

Section: Introduction Compression Techniques Are Widely Used In Inmentioning

confidence: 99%

RAKE: A simple and efficient lossless compression algorithm for the Internet of Things

Campobello

Segreto

Zanafi

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper we propose a new lossless compression algorithm suitable for Internet of Things (IoT). The proposed algorithm, named RAKE, is based only on elementary counting operations and has low memory requirements, and therefore it can be easily implemented in low-cost and low-speed microcontrollers as those used in IoT devices. Despite its simplicity, simulation results show that, in the case of sparse sequences, the proposed algorithm outperforms well-known lossless compression algorithms such as rar, gzip and bzip2. Moreover, in the case of real-world data, RAKE achieves higher compression ratios as even compared to IoT-specific lossless compression algorithms.

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“…In the research field of bio-information such as electrocardiogram and neurorecording, data compression has been researched to cope with the increase of recoded data resolution and the number of electrodes [19], [20], [21]. For the compression of stimulation data for artificial vision systems, some different points are focused: lossless and real-time operation.…”

Section: Introductionmentioning

confidence: 99%

A Low-Energy Application Specific Instruction-Set Processor towards a Low-Computational Lossless Compression Method for Stimuli Position Data of Artificial Vision Systems

Sugiura

Imai

et al. 2017

Journal of Information Processing

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This paper proposes a novel data compression method for artificial vision systems and its low-energy implementation in order to reduce energy consumption in a wireless communication subsystem. The artificial vision systems are one of the methods for realizing visual prosthesis by controlling stimulus to visual nerves, and they consist of an inner stimulating unit and an outer image processing unit. The outer unit transmits information regarding stimulation to the inner unit via wireless communication, which occupies a large portion of the whole energy consumption. Reducing traffic in wireless communication is important to prevent damage caused by extra heat dissipation of the inner unit, which leads to excess energy consumption. The proposed compression method marks a higher compression ratio than the conventional compression methods by taking advantage of the analyses of stimuli position data, which is dominant in traffic. The proposed method is implemented as an application-domain specific instruction-set processor to achieve both configurability of stimulation control and compression efficiency. The evaluation results show that the proposed implementation reduces energy consumption by about 87% and 62% in the compression and decompression process, respectively. These results indicate that the proposed method can expect to reduce energy consumption in a wireless communication receiver dramatically.

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Mixed bio-signal lossless data compressor for portable brain-heart monitoring systems

Cited by 83 publications

References 10 publications

A dual-mode ECG processor with difference-insensitive QRS detection and lossless compression

A dual-mode ECG processor with difference-insensitive QRS detection and lossless compression

RAKE: A simple and efficient lossless compression algorithm for the Internet of Things

A Low-Energy Application Specific Instruction-Set Processor towards a Low-Computational Lossless Compression Method for Stimuli Position Data of Artificial Vision Systems

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