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

Background and objective. It is challenging to conduct real-time identification of myocardial infarction (MI) due to artifact corruption and high dimensionality of multi-lead electrocardiogram (ECG). In the present study, we proposed an automated single-beat MI detection and localization system using dual-Q tunable Q-factor wavelet transformation (Dual-Q TQWT) denoising algorithm.

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


Viimeisin päivitys 2021-07-07 klo 21:38