Deep Learning in the Classification and Recognition of Cardiac Activity Patterns
Abstract
Electrocardiography is an examination performed frequently in patients experiencing symptoms of heart disease. Upon a detailed analysis, it has shown potential to detect and identify various activities. In this article, we present a deep learning approach that can be used to analyze ECG signals. Our research shows promising results in recognizing activity and disease patterns with nearly 90% accuracy. In this paper, we present the early results of our analysis, indicating the potential of using deep learning algorithms in the analysis of both onedimensional and two–dimensional data. The methodology we present can be utilized for ECG data classification and can be extended to wearable devices. Conclusions of our study pave the way for exploring live data analysis through wearable devices in order to not only predict specific cardiac conditions, but also a possibility of using them in alternative and augmentedcommunication frameworks.
References
A. Lyon, A. Minchol´e, J. Martinez, P. Laguna, and B. Rodriguez,
“Computational techniques for ECG analysis and interpretation in light
of their contribution to medical advances,” R Soc Interface, vol. 15, no.
, 2018.
D. Hatzinakos, F. Agrafioti, and A. K. Anderson, “Ecg pattern analysis
for emotion detection,” IEEE Transactions on Affective Computing,
vol. 3, no. 1, pp. 102–115, 2012.
S. Br´as, J. Ferreira, S. Soares, and A. Pinho, “Biometric and emotion
identification: An ecg compression based method,” Frontiers in Psychology,
vol. 9, p. 467, 2018.
M. Surowiec, P. Ciskowski, K. Kluwak, and Ł. Jele´n, “Deep learning
ecg signal analysis: Description and preliminary results,” in Dependable
Computer Systems and Networks, W. Zamojski, J. Mazurkiewicz,
J. Sugier, T. Walkowiak, and J. Kacprzyk, Eds. Cham: Springer Nature
Switzerland, 2023, pp. 309–318.
A. Bulagang, N. Weng, J. Mountstephens, and J. Teo, “A review of
recent approaches for emotion classification using electrocardiography
and electrodermography signals,” Informatics in Medicine Unlocked,
vol. 20, p. 100363, 2020.
J. Selvaraj, M. Murugappan, K. Wan, and S. Yaacob, “Classification of
emotional states from electrocardiogram signals: a non-linear approach
based on hurst,” BioMedical Engineering OnLine, vol. 12, no. 1, p. 44,
F. Agrafioti, D. Hatzinakos, and A. K. Anderson, “Ecg pattern analysis
for emotion detection,” IEEE Transactions on Affective Computing,
vol. 3, no. 1, pp. 102–115, 2012.
J. Liu, J. Chen, H. Jiang, W. Jia, Q. Lin, and Z. Wang, “Activity
recognition in wearable ecg monitoring aided by accelerometer data,” in
IEEE International Symposium on Circuits and Systems (ISCAS),
, pp. 1–4.
P. Kligfield, L. S. Gettes, J. J. Bailey, R. Childers, B. J. Deal, E. W.
Hancock, G. van Herpen, J. A. Kors, P. Macfarlane, D. M. Mirvis,
O. Pahlm, P. Rautaharju, and G. S. Wagner, “Recommendations for
the standardization and interpretation of the electrocardiogram. part i,”
Circulation, vol. 115, pp. 1306–1324, 2007.
A. Atkielski, “Schematic diagram of normal sinus rhythm for a
human heart as seen on ecg, two periods forming a rr-interval.” 2009,
[Wikipedia Online; accessed 19-February-2021]. [Online]. Available:
https://en.wikipedia.org/wiki/File:ECG-RRinterval.svg
M. Etiwy, Z. Akhrass, L. Gillinov, A. Alashi, R. Wang, G. Blackburn,
S. Gillinov, D. Phelan, A. Gillinov, P. Houghtaling, H. Javadikasgari,
and M. Desai, “Accuracy of wearable heart rate monitors in cardiac
rehabilitation,” Cardiovascular Diagnosis and Therapy, vol. 9, 05 2019.
R. Gilgen-Ammann, T. Schweizer, and T. Wyss, “Rr interval signal
quality of a heart rate monitor and an ecg holter at rest and during
exercise,” Eur J Appl Physiol, vol. 119, p. 1525–1532, 2019.
D. Azariadi, V. Tsoutsouras, S. Xydis, and D. Soudris, “Ecg signal
analysis and arrhythmia detection on IoT wearable medical devices,”
in 2016 5th International Conference on Modern Circuits and Systems
Technologies (MOCAST). IEEE, 5 2016.
J. Hua, Y. Xu, J. Tang, J. Liu, and J. Zhang, “Ecg heartbeat classification
in compressive domain for wearable devices,” Journal of Systems
Architecture, vol. 104, p. 101687, 3 2020.
S. Saadatnejad, M. Oveisi, and M. Hashemi, “Lstm-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices,”
IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 2, pp.
–523, 2 2020.
R. Krishnan and M. Ramesh, “Qrs axis based classification of electrode
interchange in wearable ECG devices,” in Proceedings of the 5th
EAI International Conference on Wireless Mobile Communication and
Healthcare - ”Transforming healthcare through innovations in mobile
and wireless technologies”. ICST, 2015.
P. Pławiak, “Novel methodology of cardiac health recognition based
on ecg signals and evolutionary-neural system,” Expert Systems with
Applications, vol. 92, pp. 334–349, 2018.
M. Kadbi, J. Hashemi, H. Mohseni, and A. Maghsoudi, “Classification
of ECG arrhythmias based on statistical and time-frequency features,” in
IET 3rd International Conference MEDSIP 2006. Advances in Medical,
Signal and Information Processing. IEE, 2006.
T. Teijeiro, C. A. Garcia, D. Castro, and P. Flix, “Arrhythmia Classification
from the Abductive Interpretation of Short Single-Lead ECG
Records,” in Computing in Cardiology Conference (CinC). Computing
in Cardiology, sep 14 2017.
A. Ullah, S. M. Anwar, M. Bilal, and R. M. Mehmood, “Classification
of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image
Representation,” Remote Sensing, vol. 12, no. 10, p. 1685, may 25 2020.
S. Zeybekoglu and M. Ozkan, “Classification of ECG Arrythmia beats
with Artificial Neural Networks,” in 2010 15th National Biomedical
Engineering Meeting. IEEE, 4 2010.
J. Demsar, T. Curk, A. Erjavec, Crt Gorup, T. Hocevar, M. Milutinovic,
M. Mozina, M. Polajnar, M. Toplak, A. Staric, M. Stajdohar, L. Umek,
L. Zagar, J. Zbontar, M. Zitnik, and B. Zupan, “Orange: Data mining
toolbox in python,” Journal of Machine Learning Research, vol. 14, pp.
–2353, 2013.
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