Artifact Noise Removal Techniques on Seismocardiogram Using Two Tri-Axial Accelerometers

Abstract

The aim of this study is on the investigation of motion noise removal techniques using two-accelerometer sensor system and various placements of the sensors on gentle movement and walking of the patients. A Wi-Fi based data acquisition system and a framework on Matlab are developed to collect and process data while the subjects are in motion. The tests include eight volunteers who have no record of heart disease. The walking and running data on the subjects are analyzed to find the minimal-noise bandwidth of the SCG signal. This bandwidth is used to design filters in the motion noise removal techniques and peak signal detection. There are two main techniques of combining signals from the two sensors to mitigate the motion artifact: analog processing and digital processing. The analog processing comprises analog circuits performing adding or subtracting functions and bandpass filter to remove artifact noises before entering the data acquisition system. The digital processing processes all the data using combinations of total acceleration and z-axis only acceleration. The two techniques are tested on three placements of accelerometer sensors including horizontal, vertical, and diagonal on gentle motion and walking. In general, the total acceleration and z-axis acceleration are the best techniques to deal with gentle motion on all sensor placements which improve average systolic signal-noise-ratio (SNR) around 2 times and average diastolic SNR around 3 times comparing to traditional methods using only one accelerometer. With walking motion, ADDER and z-axis acceleration are the best techniques on all placements of the sensors on the body which enhance about 7 times of average systolic SNR and about 11 times of average diastolic SNR comparing to only one accelerometer method. Among the sensor placements, the performance of horizontal placement of the sensors is outstanding comparing with other positions on all motions.

Respiration Monitoring via Forcecardiography Sensors

“…Moreover, future studies should focus on assessing the viability of FCG-based monitoring in physical activities, such as walking or running, performing heavy work or engaging in sport activities. This would require extensive testing to verify the robustness of FCG regarding motion artifacts, as already observed in accelerometer-based SCG studies [98][99][100][101].…”

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

“…Current research teams are either working on proposing extraction algorithms [56] or enhancing the quality through quantitative analysis of motion artifacts [57,58]. This involves utilizing an adaptive recursive least-squares filter [59] or time-frequency distribution analysis [60] or employing two cooperating accelerometers [54,61]. Using SCG, we can detect mechanical aortic opening, aortic closure, the point of maximal blood acceleration in the aorta, opening of the mitral valve, closure of the mitral valve, rapid filling of the left ventricle, accelerometer-derived respiration, RR, HR, and respiration amplitude.…”

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

“…Researchers mostly used only one accelerometer placed on the sternum. Recently in [47], a multi accelerometersbased noise removal technique was proposed. Accelerometers were placed in various positions on the chest.…”