【摘要】 目的对ECG信号进行实时采集和分析,利用模拟滤波器和信号放大器,构建心脏异常信号采集及分类系统。方法采用小波分析的ARM10E处理器,检测信号的形态和QRS波群复合波,基于Poincare支持向量机,从训练数据集中提取特征集,构建心脏疾病分类器,给出临床诊断分类模型。结果该装置有效改善特征提取效果,提高总体分类的准确率,便于获取和诊断心脏疾病。结论便携式ECG设备能够捕获异常信号的疑似波形,建立和评估高质量信号,减少患者的在线等待时间,利于早期诊断与识别心脏疾病。更多还原

【Abstract】 Objective To collect and analyze the ECG signal in real time, the analog filter and the signal amplifier were used to construct the abnormal signal acquisition and classification system. Methods The ARM10 E processor was used to detect the signal shape and QRS complex wave. Based on the Poincare support vector machine, the feature set was extracted from the training data set to construct the heart disease classifier, and the clinical classification model was given. Results The device effectively reduces computational complexity, improves processor speed, real-time acquisition and diagnoses heart disease. Conclusion Portable ECG devices can capture suspected waveforms of abnormal signals, establish and evaluate high quality signals, reduce patient on-line waiting time, and facilitate early diagnosis and recognition of heart disease.更多还原