Name
Abstract | ||
|---|---|---|
As an important biological signal, electrocardiogram (ECG) signals provide a valuable basis for the clinical diagnosis and treatment of several diseases. However, its reference significance is based on the effective acquisition and correct recognition of ECG signals. In fact, this mV-level weak signal can be easily affected by various interferences caused by the power of magnetic field, patient respiratory motion or contraction, and so on from the sampling terminal to the receiving and display end. The overlapping interference affects the quality of ECG waveform, leading to the false detection and recognition of wave groups, and thus causing misdiagnosis or faulty treatment. Therefore, the elimination of the interference of the ECG signal and the subsequent wave group identification technology has been a hot research topic, and their study has important significance. Based on the above, this paper introduces two improved adaptive algorithms based on the classical least mean square (LMS) algorithm by introducing symbolic functions and block-processing concepts. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/s17050942 | SENSORS |
Keywords | Field | DocType |
ECG signal,interference cancellation,LMS algorithm | Least mean squares filter,False detection,Respiratory motion,Single antenna interference cancellation,Waveform,Group identification,Speech recognition,Electronic engineering,Interference (wave propagation),Clinical diagnosis,Engineering | Journal |
Volume | Issue | Citations |
17 | 5.0 | 2 |
PageRank | References | Authors |
0.37 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aifeng Ren | 1 | 23 | 5.55 |
| Zhenxing Du | 2 | 2 | 0.37 |
| Juan Li | 3 | 9 | 0.93 |
| Fangming Hu | 4 | 10 | 3.94 |
| Xiaodong Yang | 5 | 46 | 13.17 |
| Haider Abbas | 6 | 391 | 43.88 |