YURAK-QON TOMIR KASALLIKLARI DIAGNOSTIKASI UCHUN TEXNOLOGIYALAR, ALGORITMLAR VA VOSITALAR

Авторы

  • Nosirjon Sharibayev
  • Anvar Jabborov

Ключевые слова:

Texnologiya, Diagnostika, EKG, Algoritmlar, Xolter monitori, Fiziologik signal, Filtrlash, Veyvlet almashtirish

Аннотация

Ushbu maqolada yurak-qon tomir kasalliklari diagnostikasi uchun ishlatiladigan turli xil texnologiyalar, algoritmlar va vositalar keltirilgan. So’nggi paytlarda CNN EKG tasnifining talqin qilinishini yaxshilash uchun asoslangan arxitekturadan foydalandi. HeartNet, CNN modeli ustidagi ko’p boshli diqqat mexanizmi bilan siqilgan yangi chuqur o’rganish usuli EKGni avtomatik tasniflash uchun taklif qilingan.

Библиографические ссылки

T. Castroflorio, L. Mesin, G. M. Tartaglia, C. Sforza, and D. Farina, “Use of electromyographic and electrocardiographic signals to detect sleep bruxism episodes in a natural environment,” IEEE J. Biomed. Heal. Informatics, vol. 17, no. 6, 2013, doi: 10.1109/JBHI.2013.2274532.

K. T. Chui, K. F. Tsang, H. R. Chi, B. W. K. Ling, and C. K. Wu, “An Accurate ECG-Based Transportation Safety Drowsiness Detection Scheme,” IEEE Trans. Ind. Informatics, vol. 12, no. 4, 2016, doi: 10.1109/TII.2016.2573259.

I. Nejadgholi, M. H. Moradi, and F. Abdolali, “Using phase space reconstruction for patient independent heartbeat classification in comparison with some benchmark methods,” Comput. Biol. Med., vol. 41, no. 6, 2011, doi: 10.1016/j.compbiomed.2011.04.003.

R. Ghorbani Afkhami, G. Azarnia, and M. A. Tinati, “Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals,” Pattern Recognit. Lett., vol. 70, 2016, doi: 10.1016/j.patrec.2015.11.018.

S. Nuthalapati, V. Y. Goduguluri, M. Z. Ur Rahman, C. S. L. Prasanna, S. Y. Divvela, and K. Cheemakurty, “Artifact elimination in cardiac signals using through circular leaky adaptive algorithms for remote patient care monitoring,” Indian J. Public Heal. Res. Dev., vol. 10, no. 11, 2019, doi: 10.5958/0976-5506.2019.03956.1.

L. Q., R. C., and C. G.D., “Ventricular fibrillation and tachycardia classification using a machine learning approach,” IEEE Trans. Biomed. Eng., vol. 61, no. 6, 2014.

M. N. Salman, P. Trinatha Rao, and Z. Ur Rahman, “Novel logarithmic reference free adaptive signal enhancers for ECG analysis of wireless cardiac care monitoring systems,” IEEE Access, vol. 6, 2018, doi: 10.1109/ACCESS.2018.2866303.

A. Sulthana, M. Z. U. Rahman, and S. S. Mirza, “An efficient kalman noise canceller for cardiac signal analysis in modern telecardiology systems,” IEEE Access, vol. 6, 2018, doi: 10.1109/ACCESS.2018.2848201.

C. Lastre-Dominguez, Y. S. Shmaliy, O. Ibarra-Manzano, and M. Vazquez-Olguin, “Denoising and features extraction of ecg signals in state space using unbiased fir smoothing,” IEEE Access, vol. 7, 2019, doi: 10.1109/ACCESS.2019.2948067.

D. Sadhukhan and M. Mitra, “R-Peak Detection Algorithm for Ecg using Double Difference And RR Interval Processing,” Procedia Technol., vol. 4, 2012, doi: 10.1016/j.protcy.2012.05.143.

P. Oktivasari, M. Hasyim, H. S. Amy, H. Freddy, and Suprijadi, “A simple real-time system for detection of normal and myocardial ischemia in the ST segment and T wave ECG signal,” 2019, doi: 10.1109/ICOIACT46704.2019.8938461.

N. Fujita, A. Sato, and M. Kawarasaki, “Performance study of wavelet-based ECG analysis for ST-segment detection,” 2015, doi: 10.1109/TSP.2015.7296298.

A. Kumar and M. Singh, “Ischemia detection using Isoelectric Energy Function,” Comput. Biol. Med., vol. 68, 2016, doi: 10.1016/j.compbiomed.2015.11.002.

C. Venkatesan, P. Karthigaikumar, A. Paul, S. Satheeskumaran, and R. Kumar, “ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications,” IEEE Access, vol. 6, 2018, doi: 10.1109/ACCESS.2018.2794346.

C. K. Jha and M. H. Kolekar, “Cardiac arrhythmia classification using tunable Q-wavelet transform based features and support vector machine classifier,” Biomed. Signal Process. Control, vol. 59, 2020, doi: 10.1016/j.bspc.2020.101875.

T. P. Exarchos, M. G. Tsipouras, C. P. Exarchos, C. Papaloukas, D. I. Fotiadis, and L. K. Michalis, “A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree,” Artif. Intell. Med., vol. 40, no. 3, 2007, doi: 10.1016/j.artmed.2007.04.001.

V. A. Ardeti, V. R. Kolluru, G. T. Varghese, and R. K. Patjoshi, “An Outlier Detection and Feature Ranking based Ensemble Learning for ECG Analysis,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 6, 2022, doi: 10.14569/IJACSA.2022.0130686.

K. K. Jen and Y. R. Hwang, “ECG feature extraction and classification using cepstrum and neural networks,” J. Med. Biol. Eng., vol. 28, no. 1, 2008.

G. Goovaerts, S. Padhy, B. Vandenberk, C. Varon, R. Willems, and S. Van Huffel, “A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation,” IEEE J. Biomed. Heal. Informatics, vol. 23, no. 5, 2019, doi: 10.1109/JBHI.2018.2878492.

M. Hadjem, F. Naït-Abdesselam, and A. Khokhar, “ST-segment and T-wave anomalies prediction in an ECG data using RUSBoost,” 2016, doi: 10.1109/HealthCom.2016.7749493.

R. Xiao et al., “Monitoring significant ST changes through deep learning,” J. Electrocardiol., vol. 51, no. 6, 2018, doi: 10.1016/j.jelectrocard.2018.07.026.

M. K. Sarkaleh, “Classification Of Ecg Arrhythmias Using Discrete Wavelet Transform and Neural Networks,” Int. J. Comput. Sci. Eng. Appl., vol. 2, no. 1, 2012, doi: 10.5121/ijcsea.2012.2101.

A. Sellami and H. Hwang, “A robust deep convolutional neural network with batch-weighted loss for heartbeat classification,” Expert Syst. Appl., vol. 122, 2019, doi: 10.1016/j.eswa.2018.12.037.

R. Kamaleswaran, R. Mahajan, and O. Akbilgic, “A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length,” Physiol. Meas., vol. 39, no. 3, 2018, doi: 10.1088/1361-6579/aaaa9d.

A. Mostayed, J. Luo, X. Shu, and W. Wee, “Classification of 12-Lead ECG Signals with Bi-directional LSTM Network,” pp. 1–16, 2018, [Online]. Available: http://arxiv.org/abs/1811.02090.

K. C. Chang et al., “Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms,” Can. J. Cardiol., vol. 37, no. 1, 2021, doi: 10.1016/j.cjca.2020.02.096.

S. M. Mathews, C. Kambhamettu, and K. E. Barner, “A novel application of deep learning for single-lead ECG classification,” Comput. Biol. Med., vol. 99, 2018, doi: 10.1016/j.compbiomed.2018.05.013.

H. Beyramienanlou and N. Lotfivand, “An Efficient Teager Energy Operator-Based Automated QRS Complex Detection,” J. Healthc. Eng., vol. 2018, pp. 1–11, Sep. 2018, doi: 10.1155/2018/8360475.

A. Xintarakou, V. Sousonis, D. Asvestas, P. E. Vardas, and S. Tzeis, “Remote Cardiac Rhythm Monitoring in the Era of Smart Wearables: Present Assets and Future Perspectives,” Frontiers in Cardiovascular Medicine, vol. 9. 2022, doi: 10.3389/fcvm.2022.853614.

E. Besterman and R. Creese, “Waller: pioneer of electrocardiography,” Br. Heart J., vol. 42, no. 1, 1979, doi: 10.1136/hrt.42.1.61.

Y. M. Chi, T. P. Jung, and G. Cauwenberghs, “Dry-contact and noncontact biopotential electrodes: Methodological review,” IEEE Rev. Biomed. Eng., vol. 3, 2010, doi: 10.1109/RBME.2010.2084078.

N. Shukla, A. Pandey, A. P. Shukla, and S. C. Neupane, “ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices,” J. Sensors, vol. 2022, 2022, doi: 10.1155/2022/2449956.

E. Spanò, S. Di Pascoli, and G. Iannaccone, “Low-Power Wearable ECG Monitoring System for Multiple-Patient Remote Monitoring,” IEEE Sens. J., vol. 16, no. 13, 2016, doi: 10.1109/JSEN.2016.2564995.

M. Wasimuddin, K. Elleithy, A.-S. Abuzneid, M. Faezipour, and O. Abuzaghleh, “Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey,” IEEE Access, vol. 8, pp. 177782–177803, 2020, doi: 10.1109/ACCESS.2020.3026968.

G. B. Moody and R. G. Mark, “The impact of the MIT-BIH arrhythmia database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3. 2001, doi: 10.1109/51.932724.

S. H. Mousavi, J. M. Hijmans, R. Rajabi, R. Diercks, J. Zwerver, and H. van der Worp, “Kinematic risk factors for lower limb tendinopathy in distance runners: A systematic review and meta-analysis,” Gait and Posture, vol. 69. 2019, doi: 10.1016/j.gaitpost.2019.01.011.

Z. Li, D. Zhou, L. Wan, J. Li, and W. Mou, “Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram,” J. Electrocardiol., vol. 58, 2020, doi: 10.1016/j.jelectrocard.2019.11.046.

M. Elgendi, “Fast QRS Detection with an Optimized Knowledge-Based Method: Evaluation on 11 Standard ECG Databases,” PLoS One, vol. 8, no. 9, 2013, doi: 10.1371/journal.pone.0073557.

S. Celin and K. Vasanth, “ECG Signal Classification Using Various Machine Learning Techniques,” J. Med. Syst., vol. 42, no. 12, 2018, doi: 10.1007/s10916-018-1083-6.

H. P. da Silva, C. Carreiras, A. Lourenço, A. Fred, R. C. das Neves, and R. Ferreira, “Off-the-person electrocardiography: performance assessment and clinical correlation,” Health Technol. (Berl)., vol. 4, no. 4, 2015, doi: 10.1007/s12553-015-0098-y.

A. Ebrahimzadeh, B. Shakiba, and A. Khazaee, “Detection of electrocardiogram signals using an efficient method,” Appl. Soft Comput. J., vol. 22, 2014, doi: 10.1016/j.asoc.2014.05.003.

T. W. Chua and W. W. Tan, “Non-singleton genetic fuzzy logic system for arrhythmias classification,” Eng. Appl. Artif. Intell., vol. 24, no. 2, 2011, doi: 10.1016/j.engappai.2010.10.003.

J. N. F. Mak, Y. Hu, and K. D. K. Luk, “An automated ECG-artifact removal method for trunk muscle surface EMG recordings,” Med. Eng. Phys., vol. 32, no. 8, 2010, doi: 10.1016/j.medengphy.2010.05.007.

E. D. Übeyli, “Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents,” Comput. Methods Programs Biomed., vol. 93, no. 3, 2009, doi: 10.1016/j.cmpb.2008.10.012.

F. E. Olvera and S. Member, “Electrocardiogram Waveform Feature Extraction Using the Matched Filter,” Signal Processing, 2006.

P. Tadejko and W. Rakowski, “Mathematical morphology based ECG feature extraction for the purpose of heartbeat classification,” 2007, doi: 10.1109/CISIM.2007.47.

M. B. Tayel and M. E. EI-Bouridy, “ECG Images Classification Using Feature Extraction Based On Wavelet Transformation And Neural Network,” WMSCI 2006 - 10th World Multi-Conference Syst. Cybern. Informatics, Jointly with 12th Int. Conf. Inf. Syst. Anal. Synth. ISAS 2006 - Proc., vol. 5, no. January 2006, pp. 68–70, 2006.

F. Y. O. Abdalla, L. Wu, H. Ullah, G. Ren, A. Noor, and Y. Zhao, “ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition,” Signal, Image Video Process., vol. 13, no. 7, 2019, doi: 10.1007/s11760-019-01479-4.

P. Jyothi and G. Pradeepini, “Review on Cardiac Arrhythmia Through Segmentation Approaches in Deep Learning,” in Advances in Intelligent Systems and Computing, 2021, vol. 1312 AISC, doi: 10.1007/978-981-33-6176-8_15.

Z. Liu, H. Wang, Y. Gao, and S. Shi, “Automatic Attention Learning Using Neural Architecture Search for Detection of Cardiac Abnormality in 12-Lead ECG,” IEEE Trans. Instrum. Meas., vol. 70, 2021, doi: 10.1109/TIM.2021.3109396.

T. H. Rafi and Y. Woong Ko, “HeartNet: Self Multihead Attention Mechanism via Convolutional Network With Adversarial Data Synthesis for ECG-Based Arrhythmia Classification,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3206431.

Дополнительные файлы

Опубликован

2023-12-11

Как цитировать

Sharibayev, N., & Jabborov, A. (2023). YURAK-QON TOMIR KASALLIKLARI DIAGNOSTIKASI UCHUN TEXNOLOGIYALAR, ALGORITMLAR VA VOSITALAR. Потомки Аль-Фаргани, 1(4), 128–136. извлечено от https://al-fargoniy.uz/index.php/journal/article/view/201

Выпуск

Раздел

Статьи

Категории

Наиболее читаемые статьи этого автора (авторов)