Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset

References

• Bottou L 2010. Large-scale machine learning with stochastic gradient descent. In: Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT’2010). Springer; p. 177–186.
   Google Scholar
• Bradski G. 2000. The OpenCV library. Dr Dobb’s J Software Tools. 25:120–125.
   Google Scholar
• Faes L, Liu X, Kale A, Bruynseels A, Shamdas M, Moraes G, Fu DJ, Wagner SK, Kern C, Ledsam JR, et al. 2019. Deep learning under scrutiny: performance against health care professionals in detecting diseases from medical imaging-systematic review and meta-analysis. Lancet Digital Health. 1:E271–E297. doi:10.1016/S2589-7500(19)30123-2.
   Google Scholar
• Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR. 2018. Deep learning for healthcare applications based on physiological signals: A review. Comput Methods Programs Biomed. 161:1–13. doi:10.1016/j.cmpb.2018.04.005.
   Google Scholar
• Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. 2000. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 101(23):e215–e220. doi:10.1161/01.CIR.101.23.e215.
   Google Scholar
• Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY. 2019. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 25(1):65–69. doi:10.1038/s41591-018-0268-3.
   Google Scholar
• Hou B, Yang J, Wang P, Yan R. 2019. LSTM-based auto-encoder model for ECG arrhythmias classification. IEEE Trans Instrum Meas. 69(4):1232–1240. doi:10.1109/TIM.2019.2910342.
   Google Scholar
• Ioffe S, Szegedy C. 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv Preprint. arXiv:150203167.
   Google Scholar
• Jane R, Laguna P, Thakor NV, Caminal P. 1992. “Adaptive baseline wander removal in the ECG: Comparative analysis with cubic spline technique,” Proceedings Computers in Cardiology, Durham, NC, USA, 1992, pp. 143–146, doi: 10.1109/CIC.1992.269426.
   Google Scholar
• Jang J, Tschabrunn CM, Barkagan M, Anter E, Menze B, Nezafat R. date unknown. Three-dimensional holographic visualization of high-resolution myocardial scar on HoloLens. PLOS ONE 13(10): e0205188.
   Google Scholar
• Jimenez-Perez G, Alcaine A, Camara O. 2020. Ecg-delnet: delineation of ambulatory electrocardiograms with mixed quality labeling using neural networks. arXiv Preprint. arXiv:200505236.
   Google Scholar
• Lamounier E, Bucioli A, Cardoso A, Andrade A, Soares A. 2010. On the use of Augmented Reality techniques in learning and interpretation of cardiologic data. 2010 Ann Int Conf IEEE Eng Med Biol Soc. EMBC’10:610–613.
   Google Scholar
• LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature. 521(7553):436–444. doi:10.1038/nature14539.
   Google Scholar
• Liu F, Liu C, Zhao L, Zhang X, Wu X, Xu X, Liu Y, Ma C, Wei S, He Z, et al. 2018. An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection. J Med Imaging Health Informat. 8(7):1368–1373. doi:10.1166/jmihi.2018.2442
   Google Scholar
• López Farré A, Macaya Miguel C. 2009. Libro de la salud Cardiovascular del Hospital Clínico San Carlos y la Fundación BBVA. Fundación BBVA. ISBN: 978-84-96515-92-5
   Google Scholar
• Lyon A, Mincholé A, Martnez JP, Laguna P, Rodriguez B. 2018. Computational techniques for ecg analysis and interpretation in light of their contribution to medical advances. J Royal Soc Interf. 15(138):20170821. doi:10.1098/rsif.2017.0821.
   Google Scholar
• Martínez JP, Almeida R, Olmos S, Rocha AP, Laguna P. 2004. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans Biomed Eng. 51(4):570–581. doi:10.1109/TBME.2003.821031.
   Google Scholar
• Meyer C, Keiser H. 1977. Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. Comput Biomed Res. 10(5):459–470. doi:10.1016/0010-4809(77)90021-0.
   Google Scholar
• Mincholé A, Camps J, Lyon A, Rodrguez B. 2019. Machine learning in the electrocardiogram. J Electrocardiol. 57:S61–S64. doi:10.1016/j.jelectrocard.2019.08.008.
   Google Scholar
• Okamoto T, Onda S, Yanaga K, Suzuki N, Hattori A. 2015. Clinical application of navigation surgery using augmented reality in the abdominal field. Surg Today. 45(4):397–406. doi:10.1007/s00595-014-0946-9.
   Google Scholar
• Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, et al. 2019. Pytorch: an imperative style, high-performance deep learning library. . Wallach et al., eds. Advances in Neural Information Processing Systems 32. Curran Associates, Inc., pp. 8024–8035. http://citebay.com/how-to-cite/pytorch/
   Google Scholar
• Perez Alday EA, Shah A, Liu C, Sharma A, Seyedi S, Bahrami Rad A, Reyna M, Clifford G 2020. Classification of 12-lead ECGs: the PhysioNet - computing in cardiology challenge 2020 v1.0.1. PhysioNet. Report No:.
   Google Scholar
• Prabhakararao E, Dandapat S. 2020. Myocardial infarction severity stages classification from ecg signals using attentional recurrent neural network. IEEE Sens J. 20(15):8711–8720. doi:10.1109/JSEN.2020.2984493.
   Google Scholar
• Rajaganeshan R, Ludlam C, Francis D, Parasramka S, Sutton R. 2008. Accuracy in ECG lead placement among technicians, nurses, general physicians and cardiologists. Int J Clin Pract. 62(1):65–70. doi:10.1111/j.1742-1241.2007.01390.x.
   Google Scholar
• Sassi R, Bond RR, Cairns A, Finlay DD, Guldenring D, Libretti G, Isola L, Vaglio M, Poeta R, Campana M, et al. 2017. Pdf–ecg in clinical practice: A model for long–term preservation of digital 12–lead ecg data. J Electrocardiol. 50(6):776–780.
   Google Scholar
• Strodthoff N, Wagner P, Schaeffter T, Samek W. 2020. Deep Learning for ECG Analysis: benchmarks and Insights from PTB-XL. arXiv Preprint. arXiv:200413701.
   Google Scholar
• Tompson J, Goroshin R, Jain A, LeCun Y, Bregler C 2015. “Efficient object localization using Convolutional Networks,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA. 2015, pp. 648–656, doi:10.1109/CVPR.2015.7298664.
   Google Scholar
• Treivase S, Gomez A, Matthew J, Skelton E, Schnabel J, Toussaint N. 2020. Screen tracking for clinical translation of live ultrasound image analysis methods. arXiv Preprint. arXiv:200706272.
   Google Scholar
• Yang W, Huang H, Zhang Z, Chen X, Huang K, Zhang S 2019. Towards Rich Feature Discovery With Class Activation Maps Augmentation for Person Re-Identification, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 1389–1398, doi:10.1109/CVPR.2019.00148.
   Google Scholar