An optimized compression algorithm for real-time ECG data transmission in wireless network of medical information systems

Cho, Gyoun-Yon; Lee, Seo-Joon; Lee, Tae-Ro

doi:10.1007/s10916-014-0161-7

Cited by 26 publications

(12 citation statements)

References 23 publications

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“…Several lossy compression methods have been reported in the literature 14–22 . These lossy compression methods are strongly embedded in lossless compression methods due to the risk of potential distortion 23 .…”

Section: Discussionmentioning

confidence: 99%

Efficient ECG Compression and QRS Detection for E-Health Applications

Elgendi

Mohamed

Ward

2017

Sci Rep

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Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.

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Section: Discussionmentioning

confidence: 99%

Efficient ECG Compression and QRS Detection for E-Health Applications

Elgendi

Mohamed

Ward

2017

Sci Rep

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“…Digital vital signal compression has been well established by our research team, from very redundant sequence signals such as Electrocardiography (ECG) [21] to much less diverse (non-redundant) sequence signals such as Electromyography EMG [22]. Redundant data such as ECG has been the primary target for signal processing researchers because of its high redundancy [23,24].…”

Section: Data Compressionmentioning

confidence: 99%

BAQALC: Blockchain Applied Lossless Efficient Transmission of DNA Sequencing Data for Next Generation Medical Informatics

Lee¹,

Cho

Ikeno

et al. 2018

Applied Sciences

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Due to the development of high-throughput DNA sequencing technology, genome-sequencing costs have been significantly reduced, which has led to a number of revolutionary advances in the genetics industry. However, the problem is that compared to the decrease in time and cost needed for DNA sequencing, the management of such large volumes of data is still an issue. Therefore, this research proposes Blockchain Applied FASTQ and FASTA Lossless Compression (BAQALC), a lossless compression algorithm that allows for the efficient transmission and storage of the immense amounts of DNA sequence data that are being generated by Next Generation Sequencing (NGS). Also, security and reliability issues exist in public sequence databases. For methods, compression ratio comparisons were determined for genetic biomarkers corresponding to the five diseases with the highest mortality rates according to the World Health Organization. The results showed an average compression ratio of approximately 12 for all the genetic datasets used. BAQALC performed especially well for lung cancer genetic markers, with a compression ratio of 17.02. BAQALC performed not only comparatively higher than widely used compression algorithms, but also higher than algorithms described in previously published research. The proposed solution is envisioned to contribute to providing an efficient and secure transmission and storage platform for next-generation medical informatics based on smart devices for both researchers and healthcare users.

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“…World Health Organization predicts that chronic diseases will become the most expensive problem faced by current health care systems and sees the integration of prevention into healthcare as the main solution In order to monitor patients in a long term basis several systems have been developed and several products have been produced in the recent years. These systems and products aim at providing real-time feedback information about the patient's health condition [4,5,6,7,8]. The receivers of this 1 corresponding author: Dr. Iosif Mporas, email: imporas@upatras.gr 1 information are the patients themselves, a medical center or the supervising physician of the patient.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients

et al. 2015

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New generation of healthcare is represented by wearable health monitoring systems, which provide real-time monitoring of patient's physiological parameters. It is expected that continuous ambulatory monitoring of vital signals will improve treatment of patients and enable proactive personal health management. In this paper, we present the implementation of a multimodal real-time system for epilepsy management. The proposed methodology is based on a data streaming architecture and efficient management of a big flow of physiological parameters. The performance of this architecture is examined for varying spatial resolution of the recorded data.

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An optimized compression algorithm for real-time ECG data transmission in wireless network of medical information systems

Cited by 26 publications

References 23 publications

Efficient ECG Compression and QRS Detection for E-Health Applications

Efficient ECG Compression and QRS Detection for E-Health Applications

BAQALC: Blockchain Applied Lossless Efficient Transmission of DNA Sequencing Data for Next Generation Medical Informatics

Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients

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