Araştırma Makalesi
İki Boyutlu Difüzyon Filtreleme ve Stockwell Dönüşümü Kullanılarak Elektrokardiyogram İşaretlerinin İyileştirilmesi
Öz
Elektrokardiyogram (EKG) kalp rahatsızlıkları
için önemli bir teşhis aracı olarak kullanılmaktadır. Temizlenmiş bir EKG
işareti kalp rahatsızlıklarının türü hakkında elektriksel bilgi içerir. Bu
çalışmada, Stockwell dönüşümü ve iki boyutlu eşyönsüz lineer olmayan difüzyon
metotları kullanılarak yeni hibrit bir EKG gürültü giderme metodu önerilmiştir.
Önerilen metot MIT-BIH aritmi veri tabanının altı EKG işaretine kas kasılmaları (MA) ve elektrot hareket (EM) gürültüleri eklenerek test edilmiştir. Deneysel çalışmalarda, önerilen metot geleneksel
S-dönüşümü ve WT- alt bant metotları ile karşılaştırılmıştır. Deneysel sonuçlar
önerilen hibrit modelin daha iyi Sinyal Gürültü Oranı (SGO) ve daha düşük
Ortalama Karesel Hatanın Karekökü (OKHK) değerlerine sahip olduğunu
göstermiştir
Anahtar Kelimeler
Kaynakça
- [1] Barros AK, Mansour A, Ohnishi, N. Removing artifacts from electrocardiographic signals using independent components analysis. Neurocomputing 1998; 22(1–3): 173-186. [2] Wang F, Ji Z. Application of the dual-tree complex wavelet transform in biomedical signal denoising. Bio-Med. Mater. Eng. 2014; 24(1): 109-115. [3] El B’charri, O, Latif R, Elmansouri K, Abenaou A, Jenkal W. ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform. Biomed. Eng. Online 2017; 16(26): 1-26. [4] Poornachandra S. Wavelet-based denoising using subband dependent threshold for ECG signals. Digital Signal Process. 2008; 18(1): 49–55. [5] Ho CYF, Ling BWK, Wong TPL, Chan AYP, Tam PKS. Fuzzy multiwavelet denoising on ECG signal. Electron. Lett. 2003; 39(16): 1163-1164. [6] Thakor NV, Zhu YS. Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 1991; 38(8): 785-794. [7] Tayel MB, Eltrass AS, Ammar AI. A new multi-stage combined kernel filtering approach for ECG noise removal. J. Electrocardiol. 2017; 51(2): 265-275. [8] Xiong P, Wang H, Liu M, Zhou S, Hou Z, Liu X. ECG signal enhancement based on improved denoising auto-encoder. Eng. App. Artif. Intell. 2016; 52: 194-202. [9] Erçelebi E. Electrocardiogram signals de-noising using lifting-based discrete wavelet transform. Comput. Biol. Med. 2004; 34(6): 479-493. [10] Smital L, Vítek M, Kozumplík J, Provazník I. Adaptive wavelet wiener filtering of ECG Signals. IEEE Trans. Biomed. Eng. 2013; 60(2): 437–445. [11] Ari S, Das MK, Chacko A. ECG signal enhancement using S-Transform. Comput. Biol. Med. 2013; 43(6): 649–660. [12] Das MK, Ari S. Analysis of ECG signal denoising method based on S-transform. IRBM 2013; 34(6): 362–370. [13] Rodrigues R,Couto P. A. Neural Network Approach to ECG Denoising. Cs.CE 2012; 1–15. Retrieved from http://arxiv.org/abs/1212.5217. [14] Stockwell RG. A basis for efficient representation of the S-transform. Digital Signal Process. 2007;17(1): 371–393. [15] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 1990; 12(7): 629–639. [16] MIT-BIH Arrhythmia Database. [Online]. Available: https://www.physionet.org/physiobank/database/mitdb/. [17] PhysioBank ATM. [Online]. Available: https://www.physionet.org/cgi-bin/atm/ATM.
Yıl 2019,
Cilt: 31 Sayı: 1, 39 - 46, 15.03.2019
Öz
Kaynakça
- [1] Barros AK, Mansour A, Ohnishi, N. Removing artifacts from electrocardiographic signals using independent components analysis. Neurocomputing 1998; 22(1–3): 173-186. [2] Wang F, Ji Z. Application of the dual-tree complex wavelet transform in biomedical signal denoising. Bio-Med. Mater. Eng. 2014; 24(1): 109-115. [3] El B’charri, O, Latif R, Elmansouri K, Abenaou A, Jenkal W. ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform. Biomed. Eng. Online 2017; 16(26): 1-26. [4] Poornachandra S. Wavelet-based denoising using subband dependent threshold for ECG signals. Digital Signal Process. 2008; 18(1): 49–55. [5] Ho CYF, Ling BWK, Wong TPL, Chan AYP, Tam PKS. Fuzzy multiwavelet denoising on ECG signal. Electron. Lett. 2003; 39(16): 1163-1164. [6] Thakor NV, Zhu YS. Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 1991; 38(8): 785-794. [7] Tayel MB, Eltrass AS, Ammar AI. A new multi-stage combined kernel filtering approach for ECG noise removal. J. Electrocardiol. 2017; 51(2): 265-275. [8] Xiong P, Wang H, Liu M, Zhou S, Hou Z, Liu X. ECG signal enhancement based on improved denoising auto-encoder. Eng. App. Artif. Intell. 2016; 52: 194-202. [9] Erçelebi E. Electrocardiogram signals de-noising using lifting-based discrete wavelet transform. Comput. Biol. Med. 2004; 34(6): 479-493. [10] Smital L, Vítek M, Kozumplík J, Provazník I. Adaptive wavelet wiener filtering of ECG Signals. IEEE Trans. Biomed. Eng. 2013; 60(2): 437–445. [11] Ari S, Das MK, Chacko A. ECG signal enhancement using S-Transform. Comput. Biol. Med. 2013; 43(6): 649–660. [12] Das MK, Ari S. Analysis of ECG signal denoising method based on S-transform. IRBM 2013; 34(6): 362–370. [13] Rodrigues R,Couto P. A. Neural Network Approach to ECG Denoising. Cs.CE 2012; 1–15. Retrieved from http://arxiv.org/abs/1212.5217. [14] Stockwell RG. A basis for efficient representation of the S-transform. Digital Signal Process. 2007;17(1): 371–393. [15] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 1990; 12(7): 629–639. [16] MIT-BIH Arrhythmia Database. [Online]. Available: https://www.physionet.org/physiobank/database/mitdb/. [17] PhysioBank ATM. [Online]. Available: https://www.physionet.org/cgi-bin/atm/ATM.
Toplam 1 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | MBD |
| Yazarlar | |
| Yayımlanma Tarihi | 15 Mart 2019 |
| Gönderilme Tarihi | 8 Mart 2018 |
| Yayımlandığı Sayı | Yıl 2019 Cilt: 31 Sayı: 1 |
