## 1 Introduction  
An estimated 15 million babies are born prematurely (pre-term birth) every year worldwide, which is more than one in ten [1]. The World Health Organization (WHO) has indicated that almost one million children die annually, due to complications associated with pre-term birth. Even if the babies survive, they are likely to face a lifetime of disability (learning disabilities, visual disabilities, hearing problems, etc.), which is difficult to resolve using medical treatment.  
Globally, prematurity is the leading cause of death in children under the age of five years. The fact that preterm birth rates are increasing in almost all countries is disturbing [2]. There is a growing need for a solution to this problem, as the prevention of pre-term birth is extremely difficult because it is both spontaneous and unpredictable.  
One feasible solution, also strongly recommended by WHO, is to provide essential care at the \"Golden Time\" during the postnatal period. The administration of antibiotics during this time could prevent infections developing in newborns. The appropriate timing for this can be guaranteed using information technology (IT) [3]. By combining health services and IT, uterine parameters can be monitored for signs of pre-term symptoms.  
Recent studies of a uterine electromyography (EMG) signal focused on the prediction or detection of pre-term birth [4; 5]. These technologies facilitate the remote monitoring of preterm incidents. Furthermore, a hardware feature exists to support such preterm monitoring. A solution [6] issued by Principe proposed a preterm monitoring system and method that monitors the EMG signals of the pregnant mother.  
However, the problem of the current features of these platforms is that, due to the massive size of the EMG signal [7], the network bandwidth is constantly overloaded. Hardware and software that support EMG transmission are also overwhelmed, because it is recommended that maternal patients are monitored for 40 weeks (at least 12 weeks) during the gestational period. This results in problems such as rapid battery consumption and overheating. To solve this, compression of the transmitted data is required. This would allow more patients to be supported using a limited bandwidth [8], resulting in a safe EMG signal transmission environment for transmitting maternal patient's signal.  
Another problem regarding the transmission of EMG signals is data loss in wireless transmission. There are two cases of data loss; one is natural error, and the other is the loss that occurs from lossy transmission techniques. If data is compressed, it can be transmitted as fast as possible and can be rapidly recovered. However, it is equally important to apply lossless techniques because an EMG signal is a vital sign of the patient's state, and any distortion resulting from data loss may critically affect clinicians' decisions.  
Accordingly, this research proposes a real-time wireless lossless EMG data transmission solution termed an EMG uterine monitoring transmission solution (EUMTS), to support the systems for monitoring pregnant women for early symptoms of pre-term labor. Our prior research algorithm regarding an electrocardiography (ECG) was modified and applied to EMG signals, which yielded a high performance result. The significant point of this is that EMG signal compression performance has been lower than expected in other researches thus far, because of its extreme irregularity, and was rarely touched. The proposed research handled this field and produced comparatively high results, even without any data loss. The proposed solution is envisioned to contribute to providing a seamless network platform for preterm telemedicine, facilitating the appropriate care of pre-term babies prior to exposure to infection and contamination. This is a unique contribution.