a742656e-e974-4b18-a5b4-278e1f8a87cf20210111084837718naunmdt@crossref.orgMDT DepositInternational Journal of Biology and Biomedical Engineering1998-451010.46300/91011http://www.naun.org/cms.action?id=3041111202111120211510.46300/91011.2021.15https://www.naun.org/cms.action?id=23281Arrhythmia Detection Algorithm using GoogLeNet and Generative Adversarial Network with Lifelog SignalsSihoShinIT Fusion Technology Research Center, Department of IT Fusion Technology, Chosun University, 309 Pilmun-daero Dong-gu Gwangju 61452, Korea.JaehyoJungIT Fusion Technology Research Center, Department of IT Fusion Technology, Chosun University, 309 Pilmun-daero Dong-gu Gwangju 61452, Korea.MinguKangIT Fusion Technology Research Center, Department of IT Fusion Technology, Chosun University, 309 Pilmun-daero Dong-gu Gwangju 61452, Korea.Youn TaeKimIT Fusion Technology Research Center, Department of IT Fusion Technology, Chosun University, 309 Pilmun-daero Dong-gu Gwangju 61452, Korea.Arrhythmia is a cardiovascular disease with an irregular heartbeat, which can lead to a heart attack if it lasts for an excessive amount of time. Because the symptoms of arrhythmia occur irregularly, the heart needs to be monitored for a lengthy time period. This study suggests an arrhythmia diagnosis algorithm using GoogLeNet and a GAN. Because the algorithm proposed in this study can add to the number of data using a GAN, it can accurately diagnose an arrhythmic occurrence from measured lifelog over a short period of time. The classification of ECG data using GoogLeNet and a GAN showed an accuracy of approximately 99%.1112021111202113https://www.naun.org/main/NAUN/bio/2021/a022010-001(2021).pdf10.46300/91011.2021.15.1http://www.naun.org/main/NAUN/bio/2021/a022010-001(2021).pdfMayo Clinic, LIVESCIENCE, “What is a Normal Heart Rate?”, 2018 Victor Chang, Cardiac Research institue “Arrhythmia”, 2020. 10.22489/cinc.2017.213-185VOLLMER, Marcus, “Arrhythmia classification in long-term data using relative RR intervals”, In: 2017 Computing in Cardiology (CinC). IEEE, 2017, pp. 1-4. 10.1109/access.2019.2928017HUANG, Jingshan, et al, “ECG arrhythmia classification using STFTbased spectrogram and convolutional neural network”, IEEE Access, 2019, pp. 92871-92880. 10.1109/cipech.2018.8724191GUPTA, Varun; MITTAL, Monika, “R-peak based Arrhythmia Detection using Hilbert Transform and Principal Component Analysis”, In: 2018 3rd International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH), IEEE, 2018. pp. 1-4. Saudagar, Bhawna Jindal, "R Peak Detection with Diagnosis of Arrhythmia using Adaptive Filter and Hilbert Transform.", 2019. Markus H¨oglinger, JOHANNES KEPLER UNIVERSITY “ECG Preprocessing”, 2016. ROMERO, Francisco Perdigón, et al, “Baseline wander removal methods for ECG signals: A comparative study”, arXiv preprint arXiv, 2018. Liu, Ming, et al. "Constructing a guided filter by exploiting the butterworth filter for ECG signal enhancement." Journal of Medical and Biological Engineering, 2018, pp. 980-992. 10.3390/s19132916XU, Xiaowen, et al, “Adaptive motion artifact reduction based on empirical wavelet transform and wavelet thresholding for the noncontact ECG monitoring systems”, Sensors, vol. 19, 2019. 10.1016/j.measurement.2017.05.022SAHOO, Santanu, et al. Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement, vol. 108, pp. 55-66, May 2017. 10.2196/10828NELSON, Benjamin W.; ALLEN, Nicholas B. “Accuracy of consumer wearable heart rate measurement during an ecologically valid 24-hour period: intraindividual validation study”, JMIR mHealth and uHealth, vol. 7, May 2019. 10.1016/j.ihj.2017.06.004VAIDYA, Gaurang Nandkishor, “Application of exercise ECG stress test in the current high cost modern-era healthcare system”, Indian heart journal, 2017, pp. 551-555. 10.22489/cinc.2017.364-057PLESINGER, Filip, et al, “Automatic detection of atrial fibrillation and other arrhythmias in holter ECG recordings using rhythm features and neural networks”, Computing in Cardiology (CinC) IEEE, 2017. p. 1- 4. DELANEY, Anne Marie; BROPHY, Eoin; WARD, Tomas E. “Synthesis of Realistic ECG using Generative Adversarial Networks”, arXiv preprint arXiv: 2019. 10.1109/isbi.2018.8363576FRID-ADAR, Maayan, et al, “Synthetic data augmentation using GAN for improved liver lesion classification”, 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), 2018, pp. 289-293. KIM, Jeong-Hwan, et al, “Assessment of electrocardiogram rhythms by GoogLeNet deep neural network architecture”, Journal of healthcare engineering, 2019.