Name
Title | ||
|---|---|---|
Simultaneously Concentrated PSWF-based Synchrosqueezing S-transform and its application to R peak detection in ECG signal |
Abstract | ||
|---|---|---|
Time-frequency (TF) analysis through well-known TF tool namely S-transform (ST) has been extensively used for QRS detection in Electrocardiogram (ECG) signals. However, Gaussian window-based conventional ST suffers from poor TF resolution due to the fixed scaling criterion and the long taper of the Gaussian window. Many variants of ST using different scaling criteria have been reported in literature for improving the accuracy in the detection of QRS complexes. This paper presents the usefulness of zero-order prolate spheroidal wave function (PSWF) as a window kernel in ST. PSWF has ability to concentrate maximum energy in narrow and finite time and frequency intervals, and provides more flexibility in changing window characteristics. Synchrosqueezing transform is a post processing method that improves the energy concentration in a TFR remarkably. This paper proposes a PSWF-based synchrosqueezing ST for detection of R peaks in ECG signals. The results show that the proposed method accurately detects R peaks with a sensitivity, positive predictivity and accuracy of 99.96 %, 99. 96% and 99. 92% respectively. It also improves upon on existing techniques in terms of the aforementioned metrics and the search back range. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/RO-MAN46459.2019.8956391 | 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) |
Keywords | Field | DocType |
R peak detection,ECG signal,time-frequency analysis,QRS detection,Electrocardiogram signals,poor TF resolution,fixed scaling criterion,long taper,scaling criteria,QRS complexes,zero-order prolate spheroidal wave function,window kernel,frequency intervals,synchrosqueezing transform,energy concentration,R peaks,changing window characteristics,Gaussian window-based conventional ST,simultaneously concentrated PSWF-based synchrosqueezing S-transform | Kernel (linear algebra),Computer vision,Prolate spheroidal wave function,Computer science,Algorithm,Gaussian,QRS complex,Artificial intelligence,S transform,Scaling,Finite time | Conference |
ISSN | ISBN | Citations |
1944-9445 | 978-1-7281-2623-4 | 0 |
PageRank | References | Authors |
0.34 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neha Singh | 1 | 0 | 0.34 |
| Puneesh Deora | 2 | 1 | 1.38 |
| Pyari Mohan Pradhan | 3 | 116 | 11.91 |