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