A Novel Data-Adaptive Regression Framework Based on Multivariate Adaptive Regression Splines for Electrocardiographic Imaging

Onak O. N., Erenler T., SERİNAĞAOĞLU DOĞRUSÖZ Y.

IEEE Transactions on Biomedical Engineering, cilt.69, sa.2, ss.963-974, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 69 Sayı: 2
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1109/tbme.2021.3110767
  • Dergi Adı: IEEE Transactions on Biomedical Engineering
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Biotechnology Research Abstracts, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.963-974
  • Anahtar Kelimeler: Splines (mathematics), Training, Electrocardiography, Biomedical measurement, Training data, Imaging, Electric potential, Electrocardiography, inverse problem, non-parametric regression, data-driven, NEURAL-NETWORKS, INHOMOGENEITIES, REGULARIZATION, FIELDS, NOISE, ECGI
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

IEEEObjective: Noninvasive electrocardiographic imaging (ECGI) is a promising tool for revealing crucial cardiac electrical events with diagnostic potential. We propose a novel nonparametric regression framework based on multivariate adaptive regression splines (MARS) for ECGI. Methods: The inverse problem was solved by using the regression model trained with body surface potentials (BSP) and corresponding electrograms (EGM). Simulated data as well as experimental data from torso-tank experiments were used as to assess the performance of the proposed method. The robustness of the method to measurement noise and geometric errors were assessed in terms of electrogram reconstruction quality, activation time accuracy, and localization error metrics. The methods were compared with Tikhonov regularization and neural network (NN)-based methods. The resulting mapping functions between the BSPs and EGMs were also used to evaluate the most influential measurement leads. Results: MARS-based method outperformed Tikhonov regularization in terms of reconstruction accuracy and robustness to measurement noise. The effects of geometric errors were remedied to some extent by enriching the training set composition including model errors. The MARS-based method had a comparable performance with NN-based methods, which require the adjustment of many parameters. Conclusion: MARS-based method successfully discovers the inverse mapping functions between the BSPs and EGMs yielding accurate reconstructions, and quantifies the contribution of each BSP lead. Significance: MARS-based method is adaptive, requires fewer parameter adjustments than NN-based methods, and robust to errors. Thus, it can be a feasible data-driven approach for accurately solving inverse imaging problems.