A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition (2020)
Liu, J., Zhang, C., Zhu, Y., Ristaniemi, T., Parviainen, T., & Cong, F. (2020). Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition. Computer Methods and Programs in Biomedicine, 184, Article 105120. https://doi.org/10.1016/j.cmpb.2019.105120
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Liu, Jia; Zhang, Chi; Zhu, Yongjie; Ristaniemi, Tapani; Parviainen, Tiina; Cong, Fengyu
Lehti tai sarja: Computer Methods and Programs in Biomedicine
ISSN: 0169-2607
eISSN: 1872-7565
Julkaisuvuosi: 2020
Volyymi: 184
Artikkelinumero: 105120
Kustantaja: Elsevier B.V.
Julkaisumaa: Alankomaat
Julkaisun kieli: englanti
DOI: https://doi.org/10.1016/j.cmpb.2019.105120
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/65924
Lisätietoja: Corrigendum to this article: https://doi.org/10.1016/j.cmpb.2020.105785
Tiivistelmä
Methods. After denoising and segmentation of ECG, a fourth-order wavelet tensor (leads × subbands × samples × beats) was constructed based on thediscretewavelet packet transform (DWPT), to represent the features considering the information of inter-beat, intra-beat, inter-frequency, and inter-lead. To reduce the tensor dimension and preserve the intrinsic information, the multilinear principal component analysis (MPCA) was employed. Afterward, 84 discriminate features were fed into a classifier of bootstrap-aggregated decision trees (Treebagger). A total of 78 healthy and 328 MI (6types) records including 57557 beats were chosen from PTB diagnostic ECG database for evaluation.
Results.The validation results demonstratedthat our proposed MI detection and localization system embedded with Dual-Q TQWT and wavelet packet tensor decomposition outperformedcommonly used discrete wavelet transform (DWT), empirical mode decomposition (EMD) denoising methods and vector-based PCA method. With the Treebagger classifier, we obtained an accuracy of 99.98% in beat level and an accuracy of 97.46% in record level training/testing for MI detection. We also achieved an accuracy of 99.87% in beat level and an accuracy of 90.39% in record level for MI localization.
Conclusion. Altogether, the automated system brings potential improvement in automated detectionand localization of MI in clinical practice.
YSO-asiasanat: signaalinkäsittely; signaalianalyysi; EKG; sydäninfarkti
Vapaat asiasanat: electrocardiogram (ECG); myocardial infarction (MI); dual-Q tunable Q-factor wavelet transformation (Dual-Q TQWT); discrete wavelet packet transform (DWPT); multilinear principal component analysis (MPCA)
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2020
JUFO-taso: 1
