Name
Abstract | ||
|---|---|---|
This paper introduces a novel setup and algorithm for the automatic annotation of seismocardiographic (SCG) recordings from a MEMS accelerometer. The setup utilizes gyroscopic recordings as a reference for the detection of isovolumic moment (IM) and aortic valve closure (AC) peaks. A method for deriving the rotational kinetic energy waveform is proposed and the coefficients are generated using singular vector decomposition (SVD). Experimental results on 5 subjects at rest indicate an IM detection rate of 96.9% and AC detection rate of 95.6% without envelope filtering. It is suggested that this algorithm is feasible as an ECG-free automatic peak annotation method of SCG recordings from subjects at rest. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BioCAS.2016.7833767 | 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
Keywords | Field | DocType |
seismocardiography (SCG),gyroscope,annotation,singular vector decomposition (SVD),digital signal processing | Singular value decomposition,Computer vision,Digital signal processing,Gyroscope,Vector decomposition,Computer science,Accelerometer,Waveform,Filter (signal processing),Electronic engineering,Artificial intelligence,Rotational energy | Conference |
ISSN | ISBN | Citations |
2163-4025 | 978-1-5090-2960-0 | 2 |
PageRank | References | Authors |
0.39 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| chenxi yang | 1 | 10 | 4.40 |
| Sunli Tang | 2 | 2 | 0.39 |
| negar tavassolian | 3 | 10 | 6.43 |