CAJ | 학술논문

目的：睡眠监测是睡眠质量分析中重要的环节，但目前的睡眠监测系统复杂而且难以携带。本文提出基于支持向量机的便携式睡眠监测系统，以方便地实时监控睡眠。方法该系统硬件部分由服务器和用户端设备构成，其中用户端设备负责数据采集和数据传输，服务器端负责数据分析及相关的资源管理。睡眠分析软件采用支持向量机（ support vector machines ， SVM）作为分析算法，在提取特征值的基础上，以有向无环图作为多分类策略分析得到睡眠的时相。结果对于患者的睡眠脑电实验表明分析正确率高，所需的分析时间短。结论该系统用户端设备体积小，方便携带，分析正确率高，实时性好，在睡眠监测领域具有良好的应用前景。
목적：수면감측시수면질량분석중중요적배절，단목전적수면감측계통복잡이차난이휴대。본문제출기우지지향량궤적편휴식수면감측계통，이방편지실시감공수면。방법해계통경건부분유복무기화용호단설비구성，기중용호단설비부책수거채집화수거전수，복무기단부책수거분석급상관적자원관리。수면분석연건채용지지향량궤（ support vector machines ， SVM）작위분석산법，재제취특정치적기출상，이유향무배도작위다분류책략분석득도수면적시상。결과대우환자적수면뇌전실험표명분석정학솔고，소수적분석시간단。결론해계통용호단설비체적소，방편휴대，분석정학솔고，실시성호，재수면감측영역구유량호적응용전경。
Objective Sleep monitoring is an important part of the analysis of sleep quality , yet the sleep monitoring system available now is complex and cumbersome .A portable sleep monitoring system based on support vector machines ( SVM) is proposed in this paper with great convenience and efficiency .Methods The system’ s hardware consists of the server and the user equipment .The user equipment with high portability is used for data acquisition and data transmission . The server is used for data analysis and resource maintenance.SVM is adopted as the automatic sleep analysis algorithm in the server .Based on extracted features, sleep stages are got with directed acyclic graph as the multi-classification method.Results The research results based on patient EEG analysis show that the system can reach a high accuracy rate and take short analysis time average analysis time of 1.45 seconds.Conclusions The compact user equipment is highly portable , and it can feedback the correct result to the users in real time , thus confirming that the design has a promising future in sleep monitoring .