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首页> 外文会议>2016 IEEE First Conference on Connected Health: Applications, Systems and Engineering Technologies >Heart Rate Monitoring During Intense Physical Activities Using a Motion Artifact Corrupted Signal Reconstruction Algorithm in Wearable Electrocardiogram Sensor
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Heart Rate Monitoring During Intense Physical Activities Using a Motion Artifact Corrupted Signal Reconstruction Algorithm in Wearable Electrocardiogram Sensor

机译：可穿戴式心电图传感器中使用运动伪影损坏信号重建算法在剧烈运动中的心率监测

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Accurate estimation of heart rates from electrocardiogram (ECG) signals during intense physical activity is a very challenging problem. In this study we investigated a novel technique to accurately reconstruct motion-corrupted ECG signals and HR based on time-varying spectral analysis. The algorithm is called Spectral filter algorithm for electrocardiogram Motion Artifacts and heart rate reconstruction (SegMA). The idea is to calculate time-frequency spectrum of ECG for each time shift of a windowed data segment and use the information from the spectrum to reconstruct HR during movement. The SegMA approach was applied to a datasets recorded in Chon Lab that includes 17 min recordings from 4 subjects during a challenging experimental protocol including walking, jogging, running, arm movement, wrist movement, body shaking, and weight lifting activities. The ECG and tri-axial accelerometer data were recorded from a wrist bands on both right and left wrists that are connected with wire through a tight suit. The reference ECG signals were recorded from chest using Holter monitor. The algorithm's accuracy was calculated by computing the mean absolute error between SegMA reconstructed HR from the wrist ECG and the reference HR from the Holter ECG. The average estimation errors using our method on this datasets are around 1 beats/min. These results show that the SegMA method has a potential for ECG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities.
机译：在激烈的体育活动中从心电图（ECG）信号准确估算心率是一个非常具有挑战性的问题。在这项研究中，我们研究了一种基于时变频谱分析准确重建运动受损ECG信号和HR的新技术。该算法称为用于心电图运动伪影和心率重建（SegMA）的频谱滤波器算法。这个想法是为窗口数据段的每个时间偏移计算ECG的时间频谱，并使用频谱中的信息来重建运动过程中的HR。 SegMA方法已应用于Chon Lab中记录的数据集，该数据集包括具有挑战性的实验方案（包括步行，慢跑，跑步，手臂运动，腕部运动，身体摇晃和举重活动）来自4个受试者的17分钟记录。心电图和三轴加速度计数据是通过左右手腕上的腕带记录的，这些腕带通过紧身套装与金属丝连接。使用Holter监护仪从胸部记录参考ECG信号。通过计算手腕ECG的SegMA重构HR与Holter ECG的参考HR之间的平均绝对误差来计算算法的准确性。使用我们的方法在此数据集上的平均估计误差约为1次/分钟。这些结果表明，SegMA方法可能在可穿戴设备中进行基于ECG的HR监测，从而在激烈的体育活动中进行健身追踪和健康监测。

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