Deteksi Insomnia Menggunakan Sensor GSR dan Max30102 Metode Naïve Bayes
DOI:
https://doi.org/10.30736/informatika.v10i1.1370Keywords:
Insomnia, Elektrokardiogram, Naïve Bayes, ESP32, MAX30102, GSRAbstract
Insomnia adalah gangguan tidur yang menurunkan kualitas istirahat dan membuat sulit tertidur atau mempertahankan tidur. Insomnia dapat menyerang berbagai kalangan dan disebabkan oleh berbagai faktor. Deteksi dini penting untuk mencegah insomnia menjadi kondisi serius. Polysomnography adalah metode medis konvensional untuk mendeteksi insomnia, namun memerlukan peralatan kompleks dan pasien harus menginap di rumah sakit. Untuk itu, penelitian ini mengusulkan deteksi dini insomnia dengan alat portabel berbasis sinyal elektrokardiogram (EKG) yang memiliki fitur P, Q, R, S, dan T yang dapat dianalisis. Metode yang digunakan adalah Naive Bayes, yang mengklasifikasikan data sebagai insomnia atau normal berdasarkan probabilitas tertinggi. Naive Bayes dipilih karena penelitian sebelumnya menunjukkan akurasi 80% dalam mendeteksi apnea tidur. Penelitian ini menggunakan mikrokontroler ESP32 dan sensor MAX30102 untuk akuisisi sinyal EKG, yang efisien dari segi biaya dan daya. Hasil penelitian menunjukkan akurasi sensor MAX30102 sebesar 97,73% dan sensor GSR sebesar 90% dalam mendeteksi aktivitas listrik pada kulit jari. Klasifikasi Naive Bayes mencapai akurasi 90% dalam membedakan antara kondisi normal dan insomnia.
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References
Iman Fahruzi, E. Purnama, and Mauridhi Hery Purnomo, “Asesmen ECG-Apnea Satu Sadapan untuk Peningkatan Akurasi Klasifikasi Gangguan Tidur Berdasarkan AdaBoost,” JNTETI (Jurnal Nasional Teknik Elektro dan Teknologi Informasi), May 2020, doi: https://doi.org/10.22146/jnteti.v9i2.159.
D. Riemann, L. B. Krone, K. Wulff, and C. Nissen, “Sleep, insomnia, and depression,” Neuropsychopharmacology, vol. 45, no. 1, pp. 74–89, May 2019, doi: https://doi.org/10.1038/s41386-019-0411-y.
Y. Sincihu, H. Daeng, and P. Yola, “Hubungan Kecemasan dengan Derajat Insomnia pada Lansia,” Jurnal Ilmiah Kedokteran Wijaya Kusuma, vol. 7, no. 2580–5967, pp. 15–30, Mar. 2018.
E. Urtnasan, J.-U. Park, E.-Y. Joo, and K.-J. Lee, “Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network,” Journal of Medical Systems, vol. 42, no. 6, Apr. 2018, doi: https://doi.org/10.1007/s10916-018-0963-0.
L. B. Marinho, N. de M. M. Nascimento, J. W. M. Souza, M. V. Gurgel, P. P. Rebouças Filho, and V. H. C. de Albuquerque, “A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification,” Future Generation Computer Systems, vol. 97, pp. 564–577, Aug. 2019, doi: https://doi.org/10.1016/j.future.2019.03.025.
“View of Sleep Heart Rate Variability Analysis and k-Nearest Neighbours Classification of Primary Insomnia,” Uthm.edu.my, 2024. https://penerbit.uthm.edu.my/ojs/index.php/ ijie/article/view/3475/1729 (accessed Nov. 11, 2024).
M. Ozgeyik, T. Sen, M. A. Astarcioglu, M. Okay, and A. Uslu, “Comparison of Heart Rate Variability between Shift and Rest Days in Nurses Working at Coronary Intensive Care Unit,” Acta Medica, pp. 1–7, Feb. 2021, doi: https://doi.org/10.32552/2021.actamedica.531.
Fudholi, D. H., Nayoan, R. A. N., Suyuti, M., & Rahmadi, R. (2021). Deteksi Indikasi Kelelahan Menggunakan Deep Learning. J-SAKTI (Jurnal Sains Komputer dan Informatika), 5(1), 1-9.
N. L. Molin, C. Molin, R. J. Dalpatadu, and A. K. Singh, “Prediction of obstructive sleep apnea using Fast Fourier Transform of overnight breath recordings,” Machine Learning with Applications, vol. 4, p. 100022, Jun. 2021, doi: https://doi.org/10.1016/j.mlwa.2021.100022.
Urtnasan, E., Park, J. U., & Lee, K. J. (2020). Automatic detection of sleep-disordered breathing events using recurrent neural networks from an electrocardiogram signal. Neural computing and applications, 32, 4733-4742.
A. M. Putrada, M. Abdurohman, and A. G. Putrada, “Increasing Smoke Classifier Accuracy using Naïve Bayes Method on Internet of Things,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 19–26, Nov. 2018, doi: https://doi.org/10.22219/kinetik.v4i1.704.
L. O. Aghenta and M. T. Iqbal, “Low-Cost, Open Source IoT-Based SCADA System Design Using Thinger.IO and ESP32 Thing,” Electronics, vol. 8, no. 8, p. 822, Jul. 2019, doi: https://doi.org/10.3390/electronics8080822.
R. H. Ria Hariri, L. H. Lutfi Hakim, and R. F. L. Riska Fita Lestari, “Sistem Monitoring Detak Jantung Menggunakan Sensor AD8232,” JOURNAL ZETROEM, vol. 2, no. 2, Oct. 2020, doi: https://doi.org/10.36526/ztr.v2i2.1017.
Irmayanti Irmayanti and L. T. Arlym, “ASUHAN KEBIDANAN BERKESINAMBUNGAN PADA NY. K DI PUSKESMAS CIKAMPEK KARAWANG JAWA BARAT TAHUN 2023,” PREPOTIF : JURNAL KESEHATAN MASYARAKAT, vol. 8, no. 1, pp. 2507–2519, 2023, doi: https://doi.org/10.31004/prepotif.v8i1.24018.
Y. Li, W. Pan, K. Li, Q. Jiang, and G. Liu, “Sliding Trend Fuzzy Approximate Entropy as a Novel Descriptor of Heart Rate Variability in Obstructive Sleep Apnea,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 1, pp. 175–183, Jan. 2019, doi: https://doi.org/10.1109/jbhi.2018.2790968.
A. Bijaksana Putra, H. Muhadi, and I. Melinda Putri, “ANALISIS SENTIMEN MASKAPAI PENERBANGAN MENGGUNAKAN METODE NAIVE BAYES DAN SELEKSI FITUR INFORMATION GAIN,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 7, no. 2355–7699, pp. 599–606, Jun. 2020, doi: https://doi.org/10.25126/jtiik.202071947.
M. K. Stoller, “Economic effects of insomnia,” Clinical therapeutics, vol. 16, no. 5, pp. 873–97; discussion 854, Sep. 1994, Available: https://europepmc.org/article/med/7859246
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