低成本电子心音采集与分析系统的设计与实现

发布时间：2018-06-16 01:36

本文选题：心音听诊 + 性能测评　；参考：《东北大学》2014年硕士论文

【摘要】：心音信号是人体重要的生理信号之一,心脏或者心血管疾病所表现出的病理现象会比较早地体现在心音信号上,如波形的畸变以及心杂音的出现等,因此分析心音信号能较早地发现心脏疾病的征兆。同时分析心音信号可以了解心脏的健康状况,评估心脏功能状态,辅助心脏疾病的诊断,具有重要的临床价值。而随着世界范围里心血管疾病的增加,对于心音信号的分析研究能一定程度上提高人们健康水平,并且具有一定的经济意义。伴随中国乃至世界人口平均寿命的增长和老龄化人群数目的激增,以及人们对提高健康水平的诉求,家庭健康监护的作用日益突出。家庭健康监护系统可以采集体温、脉搏、呼吸、血压、血氧、心肺音等人体的生理信号,这些生命体征的变化都反映出人体是否患病以及患者病情的好转或者恶化。而随着科技的进步,作为组成家庭健康监护系统的各种生理信号采集仪器逐渐向小型化、低功耗、便捷化方向发展。这一需求可以利用移动医疗很好地解决。移动医疗主要指利用智能手机随时随地采集用户的生理数据,然后通过传输以及医生端的处理分析,进而方便医生与患者交流病情。本文基于家庭监护以及移动医疗理念,设计并实现了低成本电子心音采集及分析系统。本系统有两种心音采集方式,第一种是通过外接MIC口将心音信号送入到PC机中实现采集,采集得到的信号可以播放,便于直接听诊。本文利用MIC口采集得到的心音信号对本系统进行了性能测评,通过主、客观的测评对比发现本系统在低成本的情况下有着良好的性能。第二种是通过蓝牙将数据传输给智能终端(手机)。智能终端上运行基于Android系统开发的客户端心音采集及分析程序,可实时绘制心音图(PCG),并且经过其初步的分析能提供给用户心率以及判断是否正常。然后用户可以通过智能终端将心音数据传输给医生端,医生端对心音信号进行预处理后利用S变换时频分析方法提取心音特征,最后结合支持向量机(SVM)机器学习方法进行心音信号的模式分类进而进行辅助诊断。
[Abstract]:The heart sound signal is one of the important physiological signals of human body. The pathological phenomenon of heart or cardiovascular disease will be reflected in the heart sound signal earlier, such as the distortion of the waveform and the appearance of the heart murmur, etc. Therefore, early detection of symptoms of heart disease can be achieved by analyzing cardiac sound signals. At the same time, the analysis of heart sound signal can understand the state of heart health, evaluate the state of heart function and assist the diagnosis of heart disease, which has important clinical value. With the increase of cardiovascular disease in the world, the analysis and research of heart sound signal can improve the health level of people to some extent, and have certain economic significance. With the increase of the average life expectancy of the population in China and the world as well as the rapid increase of the number of the aging population and the demands of people to improve their health level, the role of family health monitoring is becoming increasingly prominent. Family health monitoring system can collect body temperature, pulse, respiration, blood pressure, blood oxygen, cardiopulmonary sound and other physiological signals. With the progress of science and technology, various physiological signal acquisition instruments, which constitute the family health monitoring system, are gradually becoming miniaturized, low power consumption and easy to develop. This need can be well addressed using mobile medicine. Mobile medicine mainly refers to the use of smart phones to collect the physiological data of users at any time and anywhere, and then through the transmission and analysis of the doctor side, so as to facilitate the doctors and patients to communicate the disease. Based on the concept of family monitoring and mobile medicine, a low-cost electronic heart sound acquisition and analysis system is designed and implemented in this paper. There are two kinds of heart sound acquisition methods in this system. The first way is to transmit the heart sound signal to the PC through the external MIC port, and the acquired signal can be played, which is convenient for direct auscultation. In this paper, the performance of the system is evaluated by using the heart sound signal collected from MIC port. Through subjective and objective evaluation and comparison, it is found that the system has good performance in the case of low cost. The second is to transmit data to a smart terminal via Bluetooth. The intelligent terminal runs the client heart sound acquisition and analysis program based on the Android system. It can draw the heart sound map in real time and can provide the heart rate to the user and judge whether the heart rate is normal or not through its preliminary analysis. Then the user can transmit the heart sound data to the doctor through the intelligent terminal. After preprocessing the heart sound signal, the doctor can extract the heart sound feature by using the S transform time-frequency analysis method. Finally, the pattern classification of heart sound signal is carried out with support vector machine (SVM) machine learning method, and then assistant diagnosis is carried out.
【学位授予单位】：东北大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：R540.4;TN911.7

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