Analisis Fitur Domain Waktu ECG Heart Rate Variability Berdasarkan Gain Informasi
Sari
ABSTRAK
Salah satu analisis yang sering digunakan untuk mendeteksi berbagai penyakit yang terkait fungsi jantung adalah dengan menghitung rasio tidur dan terjaganya seseorang saat tidur pada malam hari. Namun dalam praktiknya sering ditemukan kendala baik pada saat akuisisi data ataupun pada saat pemrosesan data untuk menyimpulkan sebuah hasil analisis. Salah satu penyebabnya adalah kurang tepatnya dalam pemilihan fitur ECG untuk proses implementasi. Oleh karena itu, pada penelitian ini dilakukan seleksi fitur ECG pada domain waktu dan klasifikasi kondisi tidur dan terjaga dari 10 subjek berdasarkan fitur Heart Rate Variability (HRV) dengan menggunakan algoritma random forest. Wavelet digunakan untuk mendapatkan komponen sinyal ECG yang tepat sedangkan gain informasi digunakan untuk memilih fitur ECG yang dominan. Hasil akhir dari implementasi menunjukkan nilai akurasi rata-rata sebesar 80,26 % dengan fitur terbaik adalah median nearest neighbor index.
Kata kunci: ECG wavelet, HRV, domain waktu, gain informasi, akurasi
Â
ABSTRACT
One of the most frequent methods to detect heart-related diseases is by calculating the ratio of total sleep and awake of someone during night sleep. However, it is often encountered problems either in data acquisition or data processing to output the results of the analysis. One of the reasons is selecting ECG features improperly during implementation. Therefore, this study has been conducted ECG features selection in the time domain and classification of sleep and awake states across 10 subjects based on Heart Rate Variability (HRV) features obtained using a random forest algorithm. Wavelet was used to get the proper ECG signal components while information gain was used to select the dominant ECG features. The implementation results showed an average accuracy of 80.26 % with the median nearest neighbor index as the best feature.
Keywords: ECG, wavelet, HRV, time-domain, information gain, accuracy
ÂKata Kunci
Teks Lengkap:
PDFReferensi
Gautam, D. D., & Giri, V. K. (2016). Analysis of HRV signal for disease diagnosis. 2016 11th International Conference on Industrial and Information Systems (ICIIS), 2018-Janua, (pp. 639–643). https://doi.org/10.1109/ICIINFS.2016.8263017
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), 1–2. https://doi.org/10.1161/01.CIR.101.23.e215
Hafizhana, Y., Safitri, I., Novamizanti, L., & Ibrahim, N. (2020). Image Watermarking pada Citra Medis menggunakan Compressive Sensing berbasis Stationary Wavelet Transform. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 8(1), 43. https://doi.org/10.26760/elkomika.v8i1.43
Hilton, M. L. (2004). Wavelet and wavelet packet compression of phonocardiograms. Electronics Letters, 40(17), 1040. https://doi.org/10.1049/el:20045476
Islam, M. T., Rafa, S. R., & Kibria, M. G. (2020). Early Prediction of Heart Disease Using PCA and Hybrid Genetic Algorithm with k-Means. 2020 23rd International Conference on Computer and Information Technology (ICCIT), (pp. 1–6). https://doi.org/10.1109/ICCIT51783.2020.9392655
Jabbar, M. A., & Samreen, S. (2016). Heart disease prediction system based on hidden naïve bayes classifier. 2016 International Conference on Circuits, Controls, Communications and Computing (I4C), (pp. 1–5). https://doi.org/10.1109/CIMCA.2016.8053261
Jose, S. K., Shambharkar, C. M., & Chunkath, J. (2015). HRV analysis using ballistocardiogram with LabVIEW. 2015 International Conference on Computing and Communications Technologies (ICCCT), (pp. 128–132). https://doi.org/10.1109/ICCCT2.2015.7292732
Jung, D. W., Hwang, S. H., Lee, Y. J., Jeong, D.-U., & Park, K. S. (2017). Apnea–Hypopnea Index Prediction Using Electrocardiogram Acquired During the Sleep-Onset Period. IEEE Transactions on Biomedical Engineering, 64(2), 295–301. https://doi.org/10.1109/TBME.2016.2554138
Kansal, S., Bansod, P. P., & Kumar, A. (2015). Statistical Approach for Determination of ECG Markers. 2015 International Conference on Computational Intelligence and Communication Networks (CICN), (pp. 446–451). https://doi.org/10.1109/CICN.2015.93
Ku, C.T., Wang, H.S., Hung, K.C. & Hung, Y.S. (2006). A Novel ECG Data Compression Method Based on Nonrecursive Discrete Periodized Wavelet Transform. IEEE Transactions on Biomedical Engineering, 53(12), 2577–2583. https://doi.org/10.1109/TBME.2006.881772
Lei, S. (2012). A Feature Selection Method Based on Information Gain and Genetic Algorithm. 2012 International Conference on Computer Science and Electronics Engineering , (pp. 355–358). https://doi.org/10.1109/ICCSEE.2012.97
Liu, X., & Tang, J. (2014). Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method. IEEE Systems Journal, 8(3), 910–920. https://doi.org/10.1109/JSYST.2013.2286539
Mendonca, F., Mostafa, S. S., Morgado-Dias, F., Julia-Serda, G., & Ravelo-Garcia, A. G. (2020). A Method for Sleep Quality Analysis Based on CNN Ensemble With Implementation in a Portable Wireless Device. IEEE Access, 8, 158523–158537. https://doi.org/10.1109/ACCESS.2020.3019734
Peker, M., Arslan, A., Sen, B., Celebi, F. V., & But, A. (2015). A novel hybrid method for determining the depth of anesthesia level: Combining ReliefF feature selection and random forest algorithm (ReliefF+RF). 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), (pp. 1–8). https://doi.org/10.1109/INISTA.2015.7276737
Raj, A. A. S., Dheetsith, N., Nair, S. S., & Ghosh, D. (2014). Auto analysis of ECG signals using artificial neural network. 2014 International Conference on Science Engineering and Management Research (ICSEMR), (pp. 1–4). https://doi.org/10.1109/ICSEMR.2014.7043597
Raschka, S. (2015). Python Machine Learning. Packt Publishing.
Shaffer, F., & Ginsberg, J. P. (2017). An Overview of Heart Rate Variability Metrics and Norms.Frontiers in Public Health, 5, 1–17. https://doi.org/10.3389/fpubh.2017.00258
Shi, T., & Horvath, S. (2006). Unsupervised Learning With Random Forest Predictors. Journal of Computational and Graphical Statistics, 15(1), 118–138. https://doi.org/10.1198/106186006X94072
Szypulska, M., & Piotrowski, Z. (2012). Prediction of fatigue and sleep onset using HRV analysis. Proceedings of the 19th International Conference - Mixed Design of Integrated Circuits and Systems, MIXDES 2012, (pp. 543–546).
Vityazeva, T., Vityazev, S., & Mikheev, A. (2018). Synchronization of heart rate and respiratory signals for HRV analysis. 2018 7th Mediterranean Conference on Embedded Computing (MECO), June, (pp. 1–4). https://doi.org/10.1109/MECO.2018.8405989
Weeks, M. (2007). Digital Signal Processing Using matlab and Wavelets. In Infinity Science Press LLC (1st ed., Vol. 1).
Zarei, A., & Asl, B. M. (2019). Automatic Detection of Obstructive Sleep Apnea Using Wavelet Transform and Entropy-Based Features From Single-Lead ECG Signal. IEEE Journal of Biomedical and Health Informatics, 23(3), 1011–1021. https://doi.org/10.1109/JBHI.2018.2842919
Zheng, Q., Chen, C., Li, Z., Huang, A., Jiao, B., Duan, X., & Xie, L. (2013). A novel multiresolution SVM (MR-SVM) algorithm to detect ECG signal anomaly in WE-CARE project. 2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), (pp. 1–6). https://doi.org/10.1109/BRC.2013.6487453
DOI: https://doi.org/10.26760/elkomika.v10i2.419
Refbacks
- Saat ini tidak ada refbacks.
_______________________________________________________________________________________________________________________
ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638
Publisher:
Department of Electrical Engineering Institut Teknologi Nasional Bandung
Address: 20th Building Institut Teknologi Nasional Bandung PHH. Mustofa Street No. 23 Bandung 40124
Contact: +627272215 (ext. 206)
Email: jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________
Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.