Multiclass Arrhythmia Classification using Smartwatch Photoplethysmography Signals Collected in Real-life Settings
<|reference_start|>Multiclass Arrhythmia Classification using Smartwatch Photoplethysmography Signals Collected in Real-life Settings: Most deep learning models of multiclass arrhythmia classification are tested on fingertip photoplethysmographic (PPG) data, which has higher signal-to-noise ratios compared to smartwatch-derived PPG, and the best reported sensitivity value for premature atrial/ventricular contraction (PAC/PVC) detection is only 75%. To improve upon PAC/PVC detection sensitivity while maintaining high AF detection, we use multi-modal data which incorporates 1D PPG, accelerometers, and heart rate data as the inputs to a computationally efficient 1D bi-directional Gated Recurrent Unit (1D-Bi-GRU) model to detect three arrhythmia classes. We used motion-artifact prone smartwatch PPG data from the NIH-funded Pulsewatch clinical trial. Our multimodal model tested on 72 subjects achieved an unprecedented 83% sensitivity for PAC/PVC detection while maintaining a high accuracy of 97.31% for AF detection. These results outperformed the best state-of-the-art model by 20.81% for PAC/PVC and 2.55% for AF detection even while our model was computationally more efficient (14 times lighter and 2.7 faster).<|reference_end|>
@article{han2024multiclass,
    title={Multiclass Arrhythmia Classification using Smartwatch
  Photoplethysmography Signals Collected in Real-life Settings},
    author={Dong Han, Jihye Moon, Lu'is Roberto Mercado D'iaz, Darren Chen,
  Devan Williams, Eric Y. Ding, Khanh-Van Tran, David D. McManus, Ki H. Chon},
    journal={arXiv preprint arXiv:2409.06147},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06147},
    primaryClass={eess.SP cs.AI}
}