Aboulezz Ezz, Skoric James, D'Mello Yannick, Hakim Siddiqui, Clairmonte Nathan, Lortie Michel, Plant David V
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2638-2641. doi: 10.1109/EMBC44109.2020.9175255.
Remote health monitoring is a widely discussed topic due to its potential to improve quality and delivery of medical treatment and the global increase in cardiovascular diseases.
Seismocardiography and Gyrocardiography have been shown to provide reliable heart rate information. A simple and efficient setup was developed for the monitoring of mechanical signals at the sternum. An algorithm based in autocorrelation was run on subjects with different orientations in order to detect heart rate.
Subjects performed several tests where both SCG and GCG were recorded using an inertial measurement unit, a Raspberry Pi and a BIOPAC acquisition system. A total of 2335 cardiac cycles were obtained from 5 subjects. Heart rate was determined on a per second basis and compared with an electrocardiography (ECG) reference by correlation coefficients. Ensemble averages were used to visualize differences in VCG morphology.
Heart rate estimation obtained from VCG signals across all 5 subjects was referenced with ECG and achieved an r-squared correlation coefficient of 0.956 when supine and 0.975 when standing, compared to 0.965 across the entire dataset.
Autocorrelated Differential Algorithm was able to successfully detect heart rate, regardless of orientation and posture.
Changes in orientation of the body during measurement introduce inaccuracies. This work shows that the algorithm is resistant to orientation and more adaptable to everyday life.
由于远程健康监测在改善医疗质量和提供医疗服务方面的潜力以及全球心血管疾病的增加，它已成为一个广泛讨论的话题。
地震心动图和陀螺心动图已被证明能提供可靠的心率信息。开发了一种简单高效的装置用于监测胸骨处的机械信号。基于自相关的算法在不同体位的受试者身上运行以检测心率。
受试者进行了多项测试，使用惯性测量单元、树莓派和BIOPAC采集系统记录了地震心动图和陀螺心动图。从5名受试者身上共获得了2335个心动周期。每秒确定心率，并通过相关系数与心电图（ECG）参考值进行比较。使用总体平均值来可视化向量心电图形态的差异。
将从所有5名受试者的向量心电图信号中获得的心率估计值与心电图进行参考，仰卧时r平方相关系数为0.956，站立时为0.975，而整个数据集的该系数为0.965。
自相关差分算法能够成功检测心率，无论体位和姿势如何。
测量过程中身体体位的变化会引入误差。这项工作表明该算法对体位具有抗性，并且更适应日常生活。
