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 toimittajatLiu, Jia; Zhang, Chi; Zhu, Yongjie; Ristaniemi, Tapani; Parviainen, Tiina; Cong, Fengyu

Lehti tai sarjaComputer Methods and Programs in Biomedicine

ISSN0169-2607

eISSN1872-7565

Julkaisuvuosi2020

Volyymi184

Artikkelinumero105120

KustantajaElsevier B.V.

JulkaisumaaAlankomaat

Julkaisun kielienglanti

DOIhttps://doi.org/10.1016/j.cmpb.2019.105120

Julkaisun avoin saatavuusEi avoin

Julkaisukanavan avoin saatavuus

Julkaisu on rinnakkaistallennettu (JYX)https://jyx.jyu.fi/handle/123456789/65924

LisätietojaCorrigendum 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-asiasanatsignaalinkäsittelysignaalianalyysiEKGsydäninfarkti

Vapaat asiasanatelectrocardiogram (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-raportointiKyllä

Raportointivuosi2020

JUFO-taso1


Viimeisin päivitys 2024-14-02 klo 17:27