Deep Learning in the Classification and Recognition of Cardiac Activity Patterns

Lukasz Jelen, Piotr Ciskowski, Konrad Kluwak


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 augmented
communication frameworks.

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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:

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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