Name
Title | ||
|---|---|---|
Revisiting signal processing with spectrogram analysis on EEG, ECG and speech signals |
Abstract | ||
|---|---|---|
Biomedical signal processing is the utilization of digital signal processing techniques, such as Fourier transform, filtering, spectral estimation and wavelet transform to biomedical complications, such as the analysis of breathing cycle, cardiac signals, brain signals, etc. Digital filters are used to preserve the in-band signals and to block out-of-band noise. Low-pass, high-pass, band-pass, and band-stop filters are commonly used for filtering applications. The digital signal processing (DSP) concepts can be used for other biomedical applications such as biomedical imaging (MRI, CT, X-ray, PET, ultrasound) and genomic signal processing too. The experiments such as denoising corrupted ECG signals, respiratory artifacts removal, and an identification of rhythmic patterns from EEG signals with spectrogram analysis are performed. Matlab R2016b tool is used for this study. Finally, the assessment, survey and feedback results from the student are tabulated for the better improvement of the course study. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.future.2018.12.060 | Future Generation Computer Systems |
Keywords | Field | DocType |
Corrupted ECG,EEG signal analysis,Spectrogram,Matlab,Bio-signal processing | Noise reduction,Signal processing,Spectral density estimation,Digital signal processing,Digital filter,Pattern recognition,Computer science,Spectrogram,Filter (signal processing),Real-time computing,Artificial intelligence,Wavelet transform | Journal |
Volume | ISSN | Citations |
98 | 0167-739X | 1 |
PageRank | References | Authors |
0.37 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weijie Wang | 1 | 1 | 0.71 |
| Gaopeng Zhang | 2 | 1 | 0.37 |
| Luming Yang | 3 | 1 | 0.37 |
| V.S. Balaji | 4 | 1 | 0.37 |
| Elamaran Vellaiappan | 5 | 30 | 8.24 |
| N. Arunkumar | 6 | 1 | 1.05 |