Version 1
: Received: 21 September 2023 / Approved: 22 September 2023 / Online: 25 September 2023 (13:00:13 CEST)
Version 2
: Received: 12 October 2023 / Approved: 12 October 2023 / Online: 12 October 2023 (11:23:40 CEST)
Kraft, D.; Bieber, G.; Jokisch, P.; Rumm, P. End-to-End Premature Ventricular Contraction Detection Using Deep Neural Networks. Sensors2023, 23, 8573.
Kraft, D.; Bieber, G.; Jokisch, P.; Rumm, P. End-to-End Premature Ventricular Contraction Detection Using Deep Neural Networks. Sensors 2023, 23, 8573.
Kraft, D.; Bieber, G.; Jokisch, P.; Rumm, P. End-to-End Premature Ventricular Contraction Detection Using Deep Neural Networks. Sensors2023, 23, 8573.
Kraft, D.; Bieber, G.; Jokisch, P.; Rumm, P. End-to-End Premature Ventricular Contraction Detection Using Deep Neural Networks. Sensors 2023, 23, 8573.
Abstract
In Holter monitoring, the precise detection of standard heartbeats and Ventricular Premature Contractions (PVCs) is paramount for accurate cardiac rhythm assessment. This study introduces a novel application of the 1D U-Net neural network architecture aimed at enhancing PVC detection in Holter recordings. Training data comprised the Icentia11k, INCART DB, and our custom dataset. The model’s efficacy was subsequently validated against traditional Holter analysis methodologies across multiple databases, including AHA DB, MIT 11 DB, NST, and another custom dataset encompassing challenging real-world examples. The results underscored the 1D U-Net model’s prowess in QRS complex detection, achieving near-perfect balanced accuracy scores across all databases. PVC detection exhibited variability, with balanced accuracy scores ranging from 0.909 to 0.986. Despite some databases, like the AHA DB, showcasing lower sensitivity metrics, their robust balanced accuracy accentuates the model’s equitable performance in discerning both false positives and negatives. In conclusion, while the 1D U-Net architecture is a formidable tool for QRS detection, there’s a clear avenue for further refinement in its PVC detection capability, given the inherent complexities and noise challenges in real-world PVC occurrences.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
12 October 2023
Commenter:
Dimitri Kraft
Commenter's Conflict of Interests:
Author
Comment:
Several changes to figures and methods section Added additional figures to model interpretability section and added additional sources for model explainability and blackbox AI.
Commenter: Dimitri Kraft
Commenter's Conflict of Interests: Author
Added additional figures to model interpretability section and added additional sources for model explainability and blackbox AI.