基于心冲击图和BP神经网络的心率异常分类研究

Abnormal Heart Rate Classification Based on Ballistocardiogram and BP Neural Network

心率变异性(Heart rate variability,HRV)被广泛用于临床自主神经系统评估和心率异常分类,传统的HRV分析基于心电图(Electrocardiogram,ECG)、光容积图(Photoplethysmography,PPG)和远程光容积图(Remote PPG,RPPG),这些方法存在诸多不足:(1)ECG检测需在皮肤涂抹刺激性的耦合剂并附加电极,不宜长期监测且ECG设备价格昂贵;(2)PPG和RPPG测量时存在环境光学噪声,以及肤色不同形成的个体差异性较大;(3)ECG和PPG检测属于接触式,容易带给患者不适感。基于以上不足,提出了一种基于心冲击图(Ballistocardiogram,BCG)的HRV分析方法,该方法降低了传统设备用于HRV分析的成本,利用非接触检测减轻了患者不适感,独特的检测原理避免了个体差异性问题,在长期心血管疾病检测中起着至关重要的作用。实验中采用逆传播(Back propagation,BP)神经网络模型对心率异常进行预测分类,准确率达到80%,表明了该方法的先进性和可靠性。

Heart rate variability(HRV)is widely used in clinical autonomic nervous system assessment and classification of abnormal heart rate.Traditional HRV analysis is based on electrocardiogram(ECG),photoplethysmography(PPG)and remote PPG(RPPG).However,these methods have the following disadvantages:(1)The detection of ECG requires the application of irritating coupling agent on the skin and additional electrodes,which is not suitable for long-term monitoring,and the ECG equipment is expensive;(2)there is ambient optical noise in the PPG and RPPG measurement,and individual difference due to skin color is obvious;(3)the detections of ECG and PPG belong to contact type,which can easily bring discomfort to patients.Based on the shortcomings of the above methods,a HRV analysis method based on ballistocardiogram(BCG)is proposed.It reduces the cost of traditional equipment for HRV analysis,and uses non-contact detection to alleviate the discomfort of patients.The unique detection principle avoids the problem of individual differences,which plays a vital role in long-term cardiovascular disease prediction.In the experiment,the model of back propagation(BP)neural network is used to predict and classify abnormal heart rate with an accuracy rate of 80%,showing the advancement and reliability of the proposed method.