• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
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A Comparative Study on the Optimal Model for abnormal Detection event of Heart Rate Time Series Data Based on the Correlation between PPG and ECG


Jin-soo Kim, Kang-yoon Lee, Journal of Internet Computing and Services, Vol. 20, No. 6, pp. 137-142, Dec. 2019
10.7472/jksii.2019.20.6.137, Full Text:
Keywords: Photoplethysmography(PPG), Electrocardiogram(ECG), Abnormal event detection, SVM, LSTM

Abstract

This paper Various services exist to detect and monitor abnormal event. However, most services focus on fires and gas leaks. so It is impossible to prevent and respond to emergency situations for the elderly and severely disabled people living alone. In this study, AI model is designed and compared to detect abnormal event of heart rate signal which is considered to be the most important among various bio signals. Specifically, electrocardiogram (ECG) data is collected using Physionet's MIT-BIH Arrhythmia Database, an open medical data. The collected data is transformed in different ways. We then compare the trained AI model with the modified and ECG data.


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Cite this article
[APA Style]
Kim, J. & Lee, K. (2019). A Comparative Study on the Optimal Model for abnormal Detection event of Heart Rate Time Series Data Based on the Correlation between PPG and ECG. Journal of Internet Computing and Services, 20(6), 137-142. DOI: 10.7472/jksii.2019.20.6.137.

[IEEE Style]
J. Kim and K. Lee, "A Comparative Study on the Optimal Model for abnormal Detection event of Heart Rate Time Series Data Based on the Correlation between PPG and ECG," Journal of Internet Computing and Services, vol. 20, no. 6, pp. 137-142, 2019. DOI: 10.7472/jksii.2019.20.6.137.

[ACM Style]
Jin-soo Kim and Kang-yoon Lee. 2019. A Comparative Study on the Optimal Model for abnormal Detection event of Heart Rate Time Series Data Based on the Correlation between PPG and ECG. Journal of Internet Computing and Services, 20, 6, (2019), 137-142. DOI: 10.7472/jksii.2019.20.6.137.