Motion artifact cancellation to obtain heart sounds from a single chest-worn accelerometer

Pandia, Keya; Ravindran, Sourabh; Cole, Randy; Kovacs, G.T.A.; Giovangrandi, Laurent

doi:10.1109/icassp.2010.5495553

Cited by 54 publications

(23 citation statements)

References 8 publications

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“…The data show that the heartbeat signals are contaminated by the motion artifact, and the features and graphs of the heartbeat signals cannot be identified during the walking period. Figure 9b,c show the extracted heartbeat signals using Savitzky Golay-based polynomial smoothing [14] and ARLSF. Features and graphs of heartbeat signals are not clear from 120 s to 300 s in Figure 9b.…”

Section: Feature Extractionmentioning

confidence: 99%

A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography

Liu

2020

Sensors

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This paper presents a novel adaptive recursive least squares filter (ARLSF) for motion artifact removal in the field of seismocardiography (SCG). This algorithm was tested with a consumer-grade accelerometer. This accelerometer was placed on the chest wall of 16 subjects whose ages ranged from 24 to 35 years. We recorded the 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. These subjects were asked to perform standing and walking movements on a treadmill. ARLSF was developed in MATLAB to process the collected SCG and ECG signals simultaneously. The SCG peaks and heart rate signals were extracted from the output of ARLSF. The results indicate a heartbeat detection accuracy of up to 98%. The heart rates estimated from SCG and ECG are similar under both standing and walking conditions. This observation shows that the proposed ARLSF could be an effective method to remove motion artifact from recorded SCG signals.

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Section: Feature Extractionmentioning

confidence: 99%

A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography

Liu

2020

Sensors

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“…3.b illustrates the processed SCG signal, which is achieved after the low-pass filtering and NLMS adaptive filtering steps performed on the acceleration data. For comparison purposes, the result of a Savitzky Golay smoothing filter designed in [13] is demonstrated in Fig. 3.c and the reference ECG signal is plotted in Fig.…”

Section: Experimental Setup and Protocolmentioning

confidence: 99%

“…Their threshold for choosing a proper segment is that the standard deviation of the measurement should be smaller than 4 mg (milli-gravity). Pandia et al implemented a polynomial smoothing filter to cancel motion artifacts in walking subjects and amplify the peak of the heart sound with the trade-off of distorting the acceleration waveform [13]. This method has successfully improved the heartbeat detection rate but the exact SCG graph from the moving segments could not be recovered.…”

Section: Introduction Eismocardiogram (Scg)mentioning

confidence: 98%

Motion Artifact Cancellation of Seismocardiographic Recording From Moving Subjects

Yang

Tavassolian

2016

IEEE Sensors J.

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This paper presents a novel method of extracting seismocardiographic (SCG) data from moving adult subjects recorded via micro-electromechanical (MEMS) accelerometers. A digital signal processing (DSP) system based on the normalized least mean square (NLMS) adaptive filter design is developed in MATLAB to process the signals collected from the MEMS sensor node. Standardized experiments were performed on 40 moving adult subjects. False-positives were ruled out for a more precise detection rate. A research on sliding ensemble average was also conducted to find the minimum required window size. The results indicate a detection rate of 96% and a sliding window size of 32 intervals for robust continuous monitoring, showing that adaptive filtering could be a promising technique for the cancellation of motion noise artifacts from SCG recordings in moving subjects. since 2013. Her main research interests are non-invasive diagnostic methods, nuclear magnetic resonance (NMR)-based sensors and systems, bio and applied electromagnetics, and microelectromechanical systems (MEMS).

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“…More studies are needed that compare different filtering methods in clinical and ambulatory settings. [26,36,38,41,45,46,55,[58][59][60][61][62][63]67,71,75,76,[78][79][80]82,93] Adaptive filtering Motion artefact removal [88,95] Averaging theory Motion artefact removal [101] Comb filtering Removing respiration noise from radar signal [50] Empirical mode decomposition Baseline wandering, breathing and body movement artefact removal [76,94,95] Independent component analysis Motion artefact removal [102] Median filtering [96] Morphological filtering [95] Polynomial smoothing Motion artefact removal [103] Savitzky-Golay filtering Motion artefact removal [83,103] Wavelet denoising Segmentation of HSs and SCG [64,95,96] Wiener filtering [94] 2.…”

Section: Noise Reductionmentioning

confidence: 99%

Recent Advances in Seismocardiography

et al. 2019

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Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG.

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Motion artifact cancellation to obtain heart sounds from a single chest-worn accelerometer

Cited by 54 publications

References 8 publications

A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography

A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography

Motion Artifact Cancellation of Seismocardiographic Recording From Moving Subjects

Recent Advances in Seismocardiography

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