The Design And Implementation Of Remote ECG Emergency System

In the modern medical system,“120” has evolved from being just a patient transporter to an integral part of the medical treatment process at emergency centers.However,due to limited medical conditions,patients in remote mountainous areas can hardly be transported by ambulance,and the emergency center is unable to promptly grasp the patient’s condition or provide timely assistance.Similarly,ambulances in big cities are well-equipped for rescue operations,but due to the lack of real-time information transmission and ineffective medical decision-making,the full potential of “120” has not been realized.Establishing an efficient,systematic,and large-scale remote pre-hospital first-aid system is thus a pressing concern for the entire first-aid system.This thesis is built upon the pre-existing capacity building project of a county’s prehospital first-aid system in Shanxi Province.The aim is to apply deep learning and modern information technology to achieve a comprehensive understanding of the patient’s condition on the ambulance as quickly as possible,determine the correct medical treatment measures,and prepare for targeted in-hospital rescue.In doing so,the system provides technical support for improving the quality of first aid.The patient’s characteristics can be synchronized to the hospital from the ambulance,and hospital experts can communicate in real-time,guiding the ambulance’s work.Upon receiving the basic patient information,the hospital can prepare for targeted rescue operations in advance,thereby saving precious time and improving the efficiency of first aid,ultimately ensuring the safety of patients’ lives.The main divisions of work in this thesis are as follows:(1)Achieve information integration and sharing during emergency treatment,thereby minimizing the problem of “information islands”Through the use of this system,the 120 emergency customer service can access the status and information of all emergency vehicles in the area,promptly upload patient information,and quickly dispatch emergency vehicles based on patient location information,allowing for expedient patient contact.First-aid responders can retrieve the patient’s electronic medical record through their entered identity information,and contact experts to provide treatment assistance based on the corresponding information.Hospital staff can also arrange for rescuers to be in place in advance based on the information provided prior to the patient’s arrival.In this process,patient information and emergency resources can be exchanged,thus avoiding the “information island” problem to a certain extent,and improving the overall efficiency of treatment.(2)Propose a bidirectional LSTM neural network to assist expert doctors in achieving efficient remote diagnosisIn this system,a bidirectional LSTM neural network is proposed to process and analyze electrocardiogram data during pre-hospital emergency medical procedures to provide auxiliary diagnostic information to experts.Simultaneously,experts can conduct real-time video consultations via a dedicated network and make medical decisions as early as possible based on the auxiliary diagnosis information.(3)Establish a high availability,high performance and easy to expand pre-hospital emergency system.This system incorporates modern information and deep learning technology to optimize the pre-hospital first-aid process.Through auxiliary diagnosis,information is seamlessly integrated and transferred to achieve an efficient connection between pre-hospital and in-hospital rescue processes,consequently enhancing rescue efficiency.Adopting the microservice concept,each service is dynamically registered and cancelled,thus reinforcing the system’s loose coupling and enabling high scalability.Furthermore,the system’s single-point deployment mode has been adjusted to cluster deployment mode,mitigating the risks of single-point failure,IO read/write bottleneck,storage space bottleneck,data security,and other related issues,ensuring the system’s high availability and performance.