Efficient Real-Time Lossless EMG Data Transmission to Monitor Pre-Term Delivery in a Medical Information System

Abstract

An estimated 15 million babies are born prematurely every year worldwide, and suffer from disabilities. Appropriate care of these pre-term babies immediately after birth through telemedicine monitoring is vital. However, problems associated with a limited bandwidth and network overload due to the excessive size of the electromyography (EMG) signal impede the practical application of such medical information systems. Therefore, this research proposes an EMG uterine monitoring transmission solution (EUMTS), a lossless efficient real-time EMG transmission solution that solves such problems through efficient EMG data lossless compression. EMG data samples obtained from the Physionet PhysioBank database were used. Solution performance comparisons were conducted using Lempel-Ziv Welch (LZW) and Huffman methods, in addition to related researches. The LZW and Huffman methods showed CRs of 1.87 and 1.90, respectively, compared to 3.61 for the proposed algorithm. This was relatively high compared to related researches, even when considering that those researches were lossy whereas the proposed research was lossless. The results also showed that the proposed algorithm contributes to a reduction in battery consumption by reducing the wake-up time by 1470.6 ms. Therefore, EUMTS will contribute to providing an efficient wireless transmission environment for the prediction of pre-term delivery, enabling immediate interventions by medical professionals. Another novel point of EUMTS is that it is a lossless algorithm, which will prevent any misjudgement by clinicians because the data will not be distorted. Pre-term babies may receive point-of-care immediately after birth, preventing exposure to the development of disabilities.

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

“…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].…”

Smart Healthcare

“…In [14], Cho, Lee, and Lee proposed to use real-time lossless EMG data to monitor pre-term delivery in a medical information system. Their main research contribution is to enable lossless efficient real-time EMG data transmission by compressing the data using a novel algorithm.…”

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