CAT-TODNet: A Contextual Transformer-Based Optimized Deformable Convolution Framework for Efficient ECG-Based Heart Failure Detection

Heart Failure detection using Electrocardiogram (ECG) signals is a critical clinical task, as continuous analysis of cardiac waveforms supports early diagnosis and effective intervention. Despite advancements in machine learning and deep learning techniques, existing approaches often suffer from limited contextual representation, sensitivity to noise, and in-adequate handling of non-stationary temporal deformations in ECG signals, which restrict diagnostic reliability. To address these challenges, this study introduces a novel deep learning framework termed Contextual Auxiliary Transformer with Triple Stacked Optimized Deformable Convolution Network (CAT-TODNet) for accurate heart failure detection from ECG signals. ECG recordings acquired from the MIT-BIH Arrhythmia Database are initially subjected to three-stage preprocessing, including de-noising, signal smoothing, and Power Line Interference (PLI) removal, to enhance signal quality. The Contextual Auxiliary Transformer (CAT) module explicitly captures both static and dynamic contextual dependencies, enabling robust contextual feature extraction. These context-aware features are subsequently processed through triple stacked deformable convolution layers with adaptive receptive fields. To ensure stable offset estimation under non-stationary ECG conditions, the Al-Biruni Earth Radius (ABER) optimization algorithm is employed to optimize deformable convolution offsets, overcoming the limitations of gradient-based learning. Experimental results demonstrate that CAT-TODNet achieves an accuracy of 98.88.

@article{V2025,
  title     = {CAT-TODNet: A Contextual Transformer-Based Optimized Deformable Convolution Framework for Efficient ECG-Based Heart Failure Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Vinitha V and V. Parthasarathy and R. Santhosh},
  doi       = {10.14569/IJACSA.2025.0161211},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161211}
}