Page 79 - Fister jr., Iztok, Andrej Brodnik, Matjaž Krnc and Iztok Fister (eds.). StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference. Koper: University of Primorska Press, 2019
P. 79
monitoring and detecting atrial fibrillation using networks and signal quality analysis to detect atrial
wearable technology,” 2017 IEEE EMBS International fibrillation using short single-lead ecg recordings,”
Conference on Biomedical & Health Informatics 2017 Computing in Cardiology Conference (CinC),
(BHI), 2017. 2017.
[5] “AF Classification from a short single lead ECG [19] Z. Xiong, M. Stiles, and J. Zhao, “Robust ecg signal
recording: the PhysioNet/Computing in Cardiology classification for the detection of atrial fibrillation
Challenge 2017.” [Online]. Available: using novel neural networks,” 2017 Computing in
https://physionet.org/challenge/2017/ Cardiology Conference (CinC), 2017.
[6] 2019. [Online]. Available: [20] D. Kingma and J. Ba, “Adam: A method for
https://store.alivecor.com/products/kardiamobile stochastic optimization,” arXiv preprint
arXiv:1412.6980, 2014. [Online]. Available:
[7] T. Teijeiro, C. A. Garc´ıa, D. Castro, and P. F´elix, https://arxiv.org/abs/1412.6980
“Arrhythmia classification from the abductive
interpretation of short single-lead ECG records,” [21] G. Klambauer, T. Unterthiner, A. Mayr, and
CoRR, vol. abs/1711.03892, 2017. [Online]. Available: S. Hochreiter, “Self-Normalizing Neural Networks,”
http://arxiv.org/abs/1711.03892 arXiv:1706.02515 [cs, stat], Jun. 2017, arXiv:
1706.02515. [Online]. Available:
[8] S. Datta, C. Puri, A. Mukherjee, R. Banerjee, A. D. http://arxiv.org/abs/1706.02515
Choudhury, R. Singh, A. Ukil, S. Bandyopadhyay,
A. Pal, and S. Khandelwal, “Identifying normal, af [22] F. Yu and V. Koltun, “Multi-Scale Context
and other abnormal ecg rhythms using a cascaded Aggregation by Dilated Convolutions,”
binary classifier,” 2017 Computing in Cardiology arXiv:1511.07122 [cs], Nov. 2015, arXiv: 1511.07122.
(CinC), pp. 1–4, 2017. [Online]. Available: http://arxiv.org/abs/1511.07122
[9] M. Zabihi, A. B. Rad, A. K. Katsaggelos, S. Kiranyaz, [23] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual
S. Narkilahti, and M. Gabbouj, “Detection of atrial learning for image recognition,” in Proceedings of the
fibrillation in ecg hand-held devices using a random IEEE Conference on Computer Vision and Pattern
forest classifier,” in 2017 Computing in Cardiology Recognition, 2016, pp. 770–778.
(CinC), Sept 2017, pp. 1–4.
[24] L. A. Gatys, A. S. Ecker, M. Bethge, A. Hertzmann,
[10] S. Hong, M. Wu, Y. Zhou, Q. Wang, J. Shang, H. Li, and E. Shechtman, “Controlling perceptual factors in
and J. Xie, “Encase: An ensemble classifier for ecg neural style transfer,” in IEEE Conference on
classification using expert features and deep neural Computer Vision and Pattern Recognition (CVPR),
networks,” in 2017 Computing in Cardiology (CinC), 2017.
Sept 2017, pp. 1–4.
[25] J. Long, E. Shelhamer, and T. Darrell, “Fully
[11] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual convolutional networks for semantic segmentation,” in
Learning for Image Recognition,” arXiv:1512.03385 Proceedings of the IEEE Conference on Computer
[cs], Dec. 2015, arXiv: 1512.03385. [Online]. Available: Vision and Pattern Recognition, 2015, pp. 3431–3440.
http://arxiv.org/abs/1512.03385 [Online]. Available: http://www.cv-foundation.org/
openaccess/content cvpr 2015/html/Long Fully
[12] S. Hochreiter and J. Schmidhuber, “Long short-term Convolutional Networks 2015 CVPR paper.html
memory,” Neural computation, vol. 9, pp. 1735–80, 12
1997. [26] A. v. d. Oord, S. Dieleman, H. Zen, K. Simonyan,
O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior,
[13] P. Warrick and M. N. Homsi, “Cardiac arrhythmia and K. Kavukcuoglu, “WaveNet: A Generative Model
detection from ecg combining convolutional and long for Raw Audio,” arXiv:1609.03499 [cs], Sep. 2016,
short-term memory networks,” 2017 Computing in arXiv: 1609.03499. [Online]. Available:
Cardiology Conference (CinC), 2017. http://arxiv.org/abs/1609.03499
[14] F. Plesinger, P. Nejedly, I. Viscor, J. Halamek, and [27] F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han,
P. Jurak, “Automatic detection of atrial fibrillation W. J. Dally, and K. Keutzer, “Squeezenet:
and other arrhythmias in holter ecg recordings using Alexnet-level accuracy with 50x fewer parameters and
rhythm features and neural networks,” 2017 <1mb model size,” CoRR, vol. abs/1602.07360, 2016.
Computing in Cardiology Conference (CinC), 2017. [Online]. Available: http://arxiv.org/abs/1602.07360
[15] M. Limam and F. Precioso, “Atrial fibrillation [28] K. Simonyan and A. Zisserman, “Very Deep
detection and ecg classification based on convolutional Convolutional Networks for Large-Scale Image
recurrent neural network,” 2017 Computing in Recognition,” arXiv:1409.1556 [cs], Sep. 2014, arXiv:
Cardiology Conference (CinC), 2017. 1409.1556. [Online]. Available:
http://arxiv.org/abs/1409.1556
[16] M. Zihlmann, D. Perekrestenko, and M. Tschannen,
“Convolutional recurrent neural networks for [29] P. Rajpurkar, A. Y. Hannun, M. Haghpanahi,
electrocardiogram classification,” 2017 Computing in C. Bourn, and A. Y. Ng, “Cardiologist-Level
Cardiology Conference (CinC), 2017. Arrhythmia Detection with Convolutional Neural
Networks,” arXiv:1707.01836 [cs], Jul. 2017, arXiv:
[17] F. Andreotti, O. Carr, M. A. F. Pimentel, A. Mahdi, 1707.01836. [Online]. Available:
and M. De Vos, “Comparing feature based classifiers http://arxiv.org/abs/1707.01836
and convolutional neural networks to detect
arrhythmia from short segments of ecg,” 2017 [30] I. Goodfellow, Y. Bengio, and A. Courville, “Deep
Computing in Cardiology Conference (CinC), 2017. learning,” 2016, book in preparation for MIT Press.
[Online]. Available: http://www.deeplearningbook.org
[18] S. Parvaneh, J. Rubin, R. Asif, B. Conroy, and
S. Babaeizadeh, “Densely connected convolutional
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 79
Koper, Slovenia, 10 October
wearable technology,” 2017 IEEE EMBS International fibrillation using short single-lead ecg recordings,”
Conference on Biomedical & Health Informatics 2017 Computing in Cardiology Conference (CinC),
(BHI), 2017. 2017.
[5] “AF Classification from a short single lead ECG [19] Z. Xiong, M. Stiles, and J. Zhao, “Robust ecg signal
recording: the PhysioNet/Computing in Cardiology classification for the detection of atrial fibrillation
Challenge 2017.” [Online]. Available: using novel neural networks,” 2017 Computing in
https://physionet.org/challenge/2017/ Cardiology Conference (CinC), 2017.
[6] 2019. [Online]. Available: [20] D. Kingma and J. Ba, “Adam: A method for
https://store.alivecor.com/products/kardiamobile stochastic optimization,” arXiv preprint
arXiv:1412.6980, 2014. [Online]. Available:
[7] T. Teijeiro, C. A. Garc´ıa, D. Castro, and P. F´elix, https://arxiv.org/abs/1412.6980
“Arrhythmia classification from the abductive
interpretation of short single-lead ECG records,” [21] G. Klambauer, T. Unterthiner, A. Mayr, and
CoRR, vol. abs/1711.03892, 2017. [Online]. Available: S. Hochreiter, “Self-Normalizing Neural Networks,”
http://arxiv.org/abs/1711.03892 arXiv:1706.02515 [cs, stat], Jun. 2017, arXiv:
1706.02515. [Online]. Available:
[8] S. Datta, C. Puri, A. Mukherjee, R. Banerjee, A. D. http://arxiv.org/abs/1706.02515
Choudhury, R. Singh, A. Ukil, S. Bandyopadhyay,
A. Pal, and S. Khandelwal, “Identifying normal, af [22] F. Yu and V. Koltun, “Multi-Scale Context
and other abnormal ecg rhythms using a cascaded Aggregation by Dilated Convolutions,”
binary classifier,” 2017 Computing in Cardiology arXiv:1511.07122 [cs], Nov. 2015, arXiv: 1511.07122.
(CinC), pp. 1–4, 2017. [Online]. Available: http://arxiv.org/abs/1511.07122
[9] M. Zabihi, A. B. Rad, A. K. Katsaggelos, S. Kiranyaz, [23] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual
S. Narkilahti, and M. Gabbouj, “Detection of atrial learning for image recognition,” in Proceedings of the
fibrillation in ecg hand-held devices using a random IEEE Conference on Computer Vision and Pattern
forest classifier,” in 2017 Computing in Cardiology Recognition, 2016, pp. 770–778.
(CinC), Sept 2017, pp. 1–4.
[24] L. A. Gatys, A. S. Ecker, M. Bethge, A. Hertzmann,
[10] S. Hong, M. Wu, Y. Zhou, Q. Wang, J. Shang, H. Li, and E. Shechtman, “Controlling perceptual factors in
and J. Xie, “Encase: An ensemble classifier for ecg neural style transfer,” in IEEE Conference on
classification using expert features and deep neural Computer Vision and Pattern Recognition (CVPR),
networks,” in 2017 Computing in Cardiology (CinC), 2017.
Sept 2017, pp. 1–4.
[25] J. Long, E. Shelhamer, and T. Darrell, “Fully
[11] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual convolutional networks for semantic segmentation,” in
Learning for Image Recognition,” arXiv:1512.03385 Proceedings of the IEEE Conference on Computer
[cs], Dec. 2015, arXiv: 1512.03385. [Online]. Available: Vision and Pattern Recognition, 2015, pp. 3431–3440.
http://arxiv.org/abs/1512.03385 [Online]. Available: http://www.cv-foundation.org/
openaccess/content cvpr 2015/html/Long Fully
[12] S. Hochreiter and J. Schmidhuber, “Long short-term Convolutional Networks 2015 CVPR paper.html
memory,” Neural computation, vol. 9, pp. 1735–80, 12
1997. [26] A. v. d. Oord, S. Dieleman, H. Zen, K. Simonyan,
O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior,
[13] P. Warrick and M. N. Homsi, “Cardiac arrhythmia and K. Kavukcuoglu, “WaveNet: A Generative Model
detection from ecg combining convolutional and long for Raw Audio,” arXiv:1609.03499 [cs], Sep. 2016,
short-term memory networks,” 2017 Computing in arXiv: 1609.03499. [Online]. Available:
Cardiology Conference (CinC), 2017. http://arxiv.org/abs/1609.03499
[14] F. Plesinger, P. Nejedly, I. Viscor, J. Halamek, and [27] F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han,
P. Jurak, “Automatic detection of atrial fibrillation W. J. Dally, and K. Keutzer, “Squeezenet:
and other arrhythmias in holter ecg recordings using Alexnet-level accuracy with 50x fewer parameters and
rhythm features and neural networks,” 2017 <1mb model size,” CoRR, vol. abs/1602.07360, 2016.
Computing in Cardiology Conference (CinC), 2017. [Online]. Available: http://arxiv.org/abs/1602.07360
[15] M. Limam and F. Precioso, “Atrial fibrillation [28] K. Simonyan and A. Zisserman, “Very Deep
detection and ecg classification based on convolutional Convolutional Networks for Large-Scale Image
recurrent neural network,” 2017 Computing in Recognition,” arXiv:1409.1556 [cs], Sep. 2014, arXiv:
Cardiology Conference (CinC), 2017. 1409.1556. [Online]. Available:
http://arxiv.org/abs/1409.1556
[16] M. Zihlmann, D. Perekrestenko, and M. Tschannen,
“Convolutional recurrent neural networks for [29] P. Rajpurkar, A. Y. Hannun, M. Haghpanahi,
electrocardiogram classification,” 2017 Computing in C. Bourn, and A. Y. Ng, “Cardiologist-Level
Cardiology Conference (CinC), 2017. Arrhythmia Detection with Convolutional Neural
Networks,” arXiv:1707.01836 [cs], Jul. 2017, arXiv:
[17] F. Andreotti, O. Carr, M. A. F. Pimentel, A. Mahdi, 1707.01836. [Online]. Available:
and M. De Vos, “Comparing feature based classifiers http://arxiv.org/abs/1707.01836
and convolutional neural networks to detect
arrhythmia from short segments of ecg,” 2017 [30] I. Goodfellow, Y. Bengio, and A. Courville, “Deep
Computing in Cardiology Conference (CinC), 2017. learning,” 2016, book in preparation for MIT Press.
[Online]. Available: http://www.deeplearningbook.org
[18] S. Parvaneh, J. Rubin, R. Asif, B. Conroy, and
S. Babaeizadeh, “Densely connected convolutional
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 79
Koper, Slovenia, 10 October
