FPGA design and verification for lossless ECG coding compression

Chen, Wenjie; Lu, Chih‐Cheng; Jiang, Yihong; Fang, Hong-Wen

doi:10.1109/isne.2017.7968728

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…Various works have been produced on lossless ECG compression. In Reference 26, a conventional delta pulse code module was used to achieve the lossless compression. To accomplish lossless compression dependent on the attributes of the QRS wave of the ECG signal and K‐means clustering strategy, the author, 27 utilized the Huffman coding.…”

Section: Methodsmentioning

confidence: 99%

An efficient priority‐based convolutional auto‐encoder approach for electrocardiogram signal compression in Internet of Things based healthcare system

Mahendran

Parthasarathy

Parthasarathy³

et al. 2020

Trans Emerging Tel Tech

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Due to advancements in healthcare monitoring systems, the Internet of Things concepts are proficiently utilized in the medical field to detect and diagnose the physical health problems. The compression of more substantial medical information is a significant issue that requires ample data storage space and takes longer transmission time. Though several compression algorithms are actualized in past cases, there is an absence of an upgraded approach to achieve improved signal compression without influencing signal quality. Hence a proficient signal compression algorithm is proposed in our work to provide an enhanced electrocardiogram (ECG) signal compression without any data loss and to acquire increased compression ratio (CR) and zero construction error. In this proposed approach, the input ECG signal dataset from the MIT‐BIH arrhythmia database gets influenced by noise because of the electrical measuring gadget. Hence, preprocessing is done by the proposed multi‐scoop notch filter (MSNF) to denoise this signal by removing the specified noise frequency range of around (1‐50) Hz. This proposed MSNF is designed with adaptiveness that has achieved the enhanced denoising by adjusting the notch frequency. In addition, to extricate the sophisticated ECG signal features, Fast Fourier Transform is being utilized, and that performs and decomposes the signal elegantly and obtains characteristics of the signal in the frequency domain. After feature extraction, optimal signal compression is performed by our proposed priority‐based convolutional auto‐encoder (PCAE) that provides better compression with almost zero reconstruction error by encoding the signals into lower‐dimensional vectors in convolutional layers which are again reconstructed using a decoding approach. The experimental results are then assessed using the performance metrics that include signal to noise ratio (SNR), CR, and percentage root‐mean‐square difference (PRD). The attained results are 1.83% as average PRD value, average SNR is about 33 dB, and average CR is about 35.2% whereas the traditional CAE approach has average values of 2.05% PRD, 23.45 dB SNR, and 32.2% CR.

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Section: Methodsmentioning

confidence: 99%

An efficient priority‐based convolutional auto‐encoder approach for electrocardiogram signal compression in Internet of Things based healthcare system

Mahendran

Parthasarathy

Parthasarathy³

et al. 2020

Trans Emerging Tel Tech

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“…In [11], the research presents a simple structure compression approach for ECG signal. The compression utilize delta coding with a separation and rotation mechanism to provide a reduction in values variation of the ECG signal.…”

Section: ) Compression Implementationmentioning

confidence: 99%

Review: Recent Directions in ECG-FPGA Researches

Ibrahim

Hamad

Abed

et al. 2019

JUBES

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The last few years witnessed an increased interest in utilizing field programmable gate array (FPGA) for a variety of applications. This utilizing derived mostly by the advances in the FPGA flexible resource configuration, increased speed, relatively low cost and low energy consumption. The introduction of FPGA in medicine and health care field aim generally to replace costly and usually bigger medical monitoring and diagnostic equipment with much smaller and possibly portable systems based on FPGA that make use of the design flexibility of FPGA. Many recent researches focus on FPGA systems to deal with the well-known yet very important electrocardiogram (ECG) signal aspects to provide acceleration and improvement in the performance as well as finding and proposing new ideas for such implementations. The recent directions in ECG-FPGA are introduced in this paper.

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