Gated Recurrent Units dalam Mendeteksi Obstructive Sleep Apnea

JASMAN PARDEDE; MUHAMMAD FAUZAN RASPATI

doi:10.26760/mindjournal.v6i2.221-235

Gated Recurrent Units dalam Mendeteksi Obstructive Sleep Apnea

JASMAN PARDEDE, MUHAMMAD FAUZAN RASPATI

Sari

Abstrak

Dalam melakukan penelitian obstructive sleep apnea (OSA), polysomnography (PSG) digunakan untuk diagnosis. Namun subjek diharuskan menginap dilaboratorium selama beberapa malam untuk melakukan tes dengan PSG dan karena banyaknya alat yang harus dikenakan pada tubuh dapat membuat tidak nyaman saat pengambilan data. Belakangan ini, beberapa peneliti mengunakan single-lead ECG untuk melakukan deteksi OSA. Untuk menghasilkan model terbaik, akan dilakukan eksperimen training, dengan batch normalization dan dropout yang berbeda. Pada penelitian ini apnea-ecg dataset digunakan, RR-Interval dan amplitudo QRS complex dari released set berjumlah 35 data akan disegmentasi permenit untuk digunakan sebagai input dari arsitektur yang diajukan adalah gated recurrent unit (GRU). Lalu withheld set berjumlah 35 data akan digunakan untuk pengujian per-segment dan per-recording. Kinerja sistem diukur berdasarkan accuracy, sensitifity, dan specificity dengan pengujian per-segment mendapat hasil accuracy 83.92%, sensitifity 81.28%, dan specificity 85.55%, dan pengujian per-recording mendapat hasil accuracy 97.14%, sensitifity 95.65% dan specificity 100%.

Kata kunci: Obstructive sleep apnea, GRU, ECG, RR-Interval, QRS complex.

Abstract

In conducting obstructive sleep apnea (OSA) studies, polysomnography (PSG) was used for the diagnosis. However, the subject was required to stay in the laboratory for several nights to carry out tests with the PSG and because of the many devices that had to be worn on the body, it could be uncomfortable to collect data. Recently, several researchers have used single-lead ECG to detect OSA. To produce the best model, training experiments will be conducted, with different batch normalization and dropout. In this study, the apnea-ecg dataset is used, the RR-Interval and the QRS complex amplitude from the released set totaling 35 data will be segmented per minute to be used as input for the proposed architecture is the gated recurrent unit (GRU). Then the withheld set of 35 data will be used for per-segment and per-recording testing. System performance was measured based on accuracy, sensitivity, and specificity with per-segment testing getting 83.92% accuracy, 81.28% sensitivity, and 85.55% specificity, and per-recording testing got 97.14% accuracy, 95.65% sensitivity and 100% specificity.

Keywords: Obstructive sleep apnea, GRU, ECG, RR-Interval, QRS complex.

Teks Lengkap:

PDF

Referensi

Almutairi, H., Hassan, G. M., & Datta, A. (2021). Detection of obstructive sleep apnoea by ECG signals using deep learning architectures. European Signal Processing Conference, 2021-Janua, 1382â€“1386. https://doi.org/10.23919/Eusipco47968.2020.9287360

Bolin, E., & Lam, W. (2013). A review of sensitivity, specificity, and likelihood ratios: Evaluating the utility of the electrocardiogram as a screening tool in hypertrophic cardiomyopathy. Congenital Heart Disease, 8(5), 406â€“410. https://doi.org/10.1111/chd.12083

Brownlee, J. (2017). Gentle Introduction to the Adam Optimization Algorithm for Deep Learning. Neural Networks. https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. 1â€“9. http://arxiv.org/abs/1412.3555

Darmadi, F., Rizal, A., & Sunarya, U. (2015). Deteksi Sleep Apnea Melalui Analisis Suara Dengkuran Dengan Metode Mel Frekuensi Cepstrum. 2(2), 2681â€“2686.

Hudgel, D. W. (2016). Sleep Apnea Severity Classification Revisited. Sleep, 39(5), 1165â€“1166. https://doi.org/10.5665/sleep.5776

Ioffe, S., & Szegedy, C. (2016). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Journalism Practice, 10(6), 730â€“743. https://doi.org/10.1080/17512786.2015.1058180

Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1â€“15.

Kulkarni, A., Chong, D., & Batarseh, F. A. (2020). Foundations of data imbalance and solutions for a data democracy. In Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering. Elsevier Inc. https://doi.org/10.1016/B978-0-12-818366-3.00005-8

Le, X. H., Ho, H. V., Lee, G., & Jung, S. (2019). Application of Long Short-Term Memory (LSTM) neural network for flood forecasting. Water (Switzerland), 11(7). https://doi.org/10.3390/w11071387

Penzel, T., Moody, G. B., Mark, R. G., Goldberger, A. L., & Peter, J. H. (2000). Apnea-ECG database. Computers in Cardiology, 255â€“258.

Purwowiyoto, S. L. (2018). Obstructive Sleep Apnea dan Gagal Jantung. YARSI Medical Journal, 25(3), 172. https://doi.org/10.33476/jky.v25i3.364

Shen, G., Tan, Q., Zhang, H., Zeng, P., & Xu, J. (2018). Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia Computer Science, 131, 895â€“903. https://doi.org/10.1016/j.procs.2018.04.298

Singh, D., Vinod, K., & Saxena, S. C. (2004). Sampling frequency of the RR interval time series for spectral analysis of heart rate variability. Journal of Medical Engineering and Technology, 28(6), 263â€“272. https://doi.org/10.1080/03091900410001662350

Song, C., Liu, K., Zhang, X., Chen, L., & Xian, X. (2016). An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model from ECG Signals. IEEE Transactions on Biomedical Engineering, 63(7), 1532â€“1542. https://doi.org/10.1109/TBME.2015.2498199

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (1993). Dropout: A Simple Way to Prevent Neural Networks from Overfittin. Physics Letters B, 299(3â€“4), 345â€“350. https://doi.org/10.1016/0370-2693(93)90272-J

Varon, C., Caicedo, A., Testelmans, D., Buyse, B., & Van Huffel, S. (2015). A Novel Algorithm for the Automatic Detection of Sleep Apnea from Single-Lead ECG. IEEE Transactions on Biomedical Engineering, 62(9), 2269â€“2278. https://doi.org/10.1109/TBME.2015.2422378

Wang, T., Lu, C., Shen, G., & Hong, F. (2019). Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network. PeerJ, 2019(9), 1â€“17. https://doi.org/10.7717/peerj.7731

DOI: https://doi.org/10.26760/mindjournal.v6i2.221-235