Abstract
This work proposes a novel filter for motion artifact cancellation from a single channel seismocardiography (SCG) data recorded by a tri-axis accelerometer. A real time adaptive forgetting factor recursive least square filter (AFFRLSF) was developed and embedded in our designed single channel SCG recorder system (SCSRS) for motion artifact cancellation, heartbeat signal extraction, and heart rate calculation. This SCSRS was placed on the chest wall of 24 subjects who were asked to perform standing, walking, jogging, and jumping movements on a treadmill. We recorded the single channel SCG signal and the standard Electrocardiogram (ECG) lead I signal by placing one electrode on the right arm (RA) and another on the left arm (LA) of the subjects. The heartbeat signals were extracted and heart rates were calculated from the output of AFFRLSF. The graphs of the extracted heartbeat signals were very clear under all the standing, walking, jogging, and jumping motions. The results indicate an average correlation coefficient of up to 0.9878 between heart rates estimated from SCG and ECG of all the 24 subjects. This observation shows that the proposed AFFRLSF could be an effective method for motion artifact cancellation from recorded single channel SCG signals.
Highlights
Seismocardiography (SCG) is a non-invasive mechanical vibration measurement that is generated by heartbeat and transmitted to the chest wall [1,2,3,4,5]
It is obvious that the amplitude of motion artifact increases from standing to jumping condition, and the extracted heartbeat signal is visually noticeable in Figure 5(c) while polluted by motion artifact in Figure 5(b), which well proves that the proposed adaptive forgetting factor recursive least square filter (AFFRLSF) outperforms the Savitzky Golay based polynomial smoothing method
It can be observed that the features and graphs of heartbeat signals which polluted by motion artifact are visually unnoticeable are extracted to be visually noticeable under walking, jogging, and jumping conditions
Summary
Seismocardiography (SCG) is a non-invasive mechanical vibration measurement that is generated by heartbeat and transmitted to the chest wall [1,2,3,4,5]. In 2017, Javaid and Taebi proposed an ensemble averaging and empirical mode decomposition method to remove white noise from a synthetic vibrocardiographic signal and to reduce the motion artifacts generated due to walking at normal and moderately fast speeds at treadmill respectively [26, 27] All these signal processing methods have good performances on motion artifact cancellation from recorded acceleration data, but the graph of the extracted SCG signal could not be recovered. The heart rates were successfully estimated but the graph of the extracted SCG was recovered by using an extra moving average method To solve this problem, our research group has recently developed a real time adaptive forgetting factor recursive least square filter (AFFRLSF) for motion artifact cancellation from the single channel SCG signal which is recorded by only one accelerometer.
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