基于信号质量评估和卡尔曼滤波的心率估计算法

目的：研究基于多导重症监护导联心电分析的逐搏心率估计算法。方法：通过开放源码的QRS搏动识别算法计算逐搏心率，通过分析信号的波形特征、统计特性和相互关系导出反映信号质量好坏的信号质量指数（signal quahty index，SQI）。应用基于信号质量指数调节的卡尔曼滤波方法进行心率估计，通过残差值和信号质量指数的大小来动态调节卡尔曼滤波器的状态变量，获得逐搏心率的最佳估计。应用美国麻省理工学院多参数智能重症监护数据库Ⅱ中437例病人的6000多小时高质量数据和人为添加真实心电干扰的数据进行算法评价。结果：与直接心率估计及采样保持等算法比较，本算法在严重干扰存在时，心率估计的均方根误差最小。结论：基于信号质量评估和卡尔曼滤波的心率估计算法在严重干扰存在时仍能提供精确的心率估计。

Heart Rate Estimation Algorithm Based on Signal Quality Estimation and Kalman Filter

Objective: To develop a robust heart rate (HR) estimation method based on the HR estimates derived from multiple electrocardiogram (ECG) leads from intensive care patients. Methods: Heart rate was obtained by an open source QRS detection algorithm. Physiological signal quality indices (SQI) were obtained by analyzing the morphological and statistical characteristics of each waveform and their relationships. Robust HR estimation was obtained by a Kalman filter based upon the SQI adjustment. The state of Kalman filter could be adjusted by the innovation of Kalman filter and the SQI of ECG data. This method was evaluated using more than 6000 hours of simultaneously acquired ECG from a 437 patient subset of the Multi-Parameter Intelligent Monitoring for Intensive Care II database and adding real ECG noise. Results: Compared with other heart rate estimation methods such as the direct estimation or sample and hold method, this method has the smallest root mean square error (rMSE) of heart rate estimation even when high levels of persistent noise occur. Conclusion: This algorithm provides an accurate HR estimation even in the presence of high levels of persistent noise and artifact.