توسعه یک روش تطبیقی جدید بر پایه تجزیه فوریه تجربی برای تشخیص آپنه خواب انسدادی به کمک تحلیل سیگنال الکتروکاردیوگرام

نوع مقاله : علمی-پژوهشی

نویسندگان

1 گروه بیوالکتریک، دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران

2 دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند

3 دانشگاه صنعتی سهند تبریز

چکیده

آپنه خواب انسدادی یک اختلال شایع تنفسی در حین خواب است که می‌تواند عواقب منفی قابل‌توجهی بر کیفیت زندگی و عملکرد روزانه افراد داشته باشد. در حال حاضر، پلی‌سومنوگرافی استاندارد اصلی تشخیص آپنه خواب است که نمی‌تواند انتظارات یک تشخیص سریع و اقتصادی را با تحلیل چندین سیگنال به ‌صورت همزمان تأمین کند. در این راستا توسعه الگوریتم‌های تشخیصی خودکار، قابل اعتماد و مقرون ‌به ‌صرفه حائز اهمیت است. از این رو در این مطالعه، با هدف تشخیص رویدادهای آپنه خواب انسدادی، یک الگوریتم تشخیص خودکار بر اساس تحلیل تک لید سیگنال الکتروکاردیوگرام ارائه ‌شده است. بدین منظور از یک روش تطبیقی جدید مبتنی بر تجزیه فوریه تجربی و استخراج ویژگی‌های آماری و بعد فرکتال از توابع باند ذاتی فوریه سیگنال به همراه الگوریتم انتخاب ویژگی ReliefFو طبقه‌بند جنگل تصادفی استفاده شده است. روش تجزیه فوریه تجربی می‌تواند به عنوان یک ابزار جدید تجزیه سیگنال قابلیت مناسبی در استخراج نوسانات مرتبط با اجزای غیر ایستای سیگنال ارائه دهد. در این مطالعه جهت بررسی قدرت تشخیص روش پیشنهادی از پایگاه داده Apnea-ECG که شامل ۷۰ ثبت از سیگنال الکتروکاردیوگرام تک کانال می‌باشد، استفاده شده است. نتایج حاصل نشان داده است که الگوریتم پیشنهادی قادر به تشخیص رویدادهای آپنه خواب انسدادی با مقادیر صحت 03/88%، حساسیت 44/83% و اختصاصیت 84/90% می‌باشد. صحت بالای نتایج به دست‌آمده به همراه تعداد ویژگی‌های مناسب نشان‌دهنده مصالحه بین دقت و تعداد ویژگی‌های استخراج‌شده می‌باشد که منجر به بار محاسباتی مناسب الگوریتم پیشنهادی می‌گردد که استفاده آن را در کاربردهای کلینیکی ممکن می‌سازد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Development of a New Adaptive Method Based on Empirical Fourier Decomposition for the Diagnosis of Obstructive Sleep Apnea Using Electrocardiogram Signal Analysis

نویسندگان [English]

  • Masoumeh Pourezzat 1
  • Hamed Danandeh Hesar 3
1 Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran
3 Faculty of Biomedical Engineering Sahand University of Technology
چکیده [English]

Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder that can have significant effects on people's quality of life and daily functioning. polysomnography is the gold standard for diagnosing sleep apnea which cannot provide the expectations of a fast and economical diagnosis by analyzing several signals simultaneously. In this regard, the development of automatic, reliable and cost-effective diagnosis algorithms is important. Therefore, in this study, with the aim of diagnosing obstructive sleep apnea events, an automatic diagnostic algorithm based on single-lead Electrocardiogram (ECG) signal has been proposed. For this purpose, a new adaptive method based on Empirical Fourier Decomposition (EFD) and extraction of statistical and fractal dimension features from the Fourier Intrinsic Band Functions (FIBF) of the signal along with the ReliefF selection algorithm and Random Forest classification has been used. Empirical Fourier Decomposition can be a new tool for signal decomposition, which provides a suitable capability in extracting oscillations related to non-stationary components of the signal. In order to evaluate the proposed algorithm, the Apnea-ECG database, which contains 70 recordings of single-channel ECG signals, has been used. The results have shown that the proposed algorithm is able to detect obstructive sleep apnea events with %88.03 accuracy, %83.44 sensitivity, and %90.84 specificity. The high accuracy of the obtained results along with the number of suitable features indicates a compromise between the accuracy and the number of extracted features, which leads to a suitable computational load for the proposed algorithm, which makes it possible to use it in clinical applications.

کلیدواژه‌ها [English]

  • Obstructive sleep apnea (OSA)
  • Electrocardiogram (ECG) signal
  • Empirical Fourier Decomposition (EFD)

مراجع

[1] V. M. Kumar, "Sleep and sleep disorders," Indian Journal of Chest Diseases and Allied Sciences, vol. 50, no. 1, p. 129, 2008.
[2] T. Porkka‐Heiskanen, K. M. Zitting, and H. K. Wigren, "Sleep, its regulation and possible mechanisms of sleep disturbances," Acta physiologica, vol. 208, no. 4, pp. 311-328, 2013.
[3] R. Rohan, D. S. Kumar, and S. R. Patri, "Various methods for identification of obstructive sleep apnea using electrocardiogram features," Journal of Scientific Research, vol. 64, no. 1, 2020.
[4] A. R. Hassan, "Automatic screening of obstructive sleep apnea from single-lead electrocardiogram," in 2015 international conference on electrical engineering and information communication technology (ICEEICT), 2015: IEEE, pp. 1-6.
[5] J. Balcerzak, "Obstructive sleep apnea syndrome--the most common sleep related breathing disorder," Otolaryngologia Polska= The Polish Otolaryngology, vol. 55, no. 5, pp. 483-487, 2001.
[6] H. Sharma and K. Sharma, "Sleep apnea detection from ECG using variational mode decomposition," Biomedical Physics & Engineering Express, vol. 6, no. 1, p. 015026, 2020.
[7] A. Zarei and B. M. Asl, "Automatic classification of apnea and normal subjects using new features extracted from HRV and ECG-derived respiration signals," Biomedical Signal Processing and Control, vol. 59, p. 101927, 2020.
[8] B. Fatimah, P. Singh, A. Singhal, and R. B. Pachori, "Detection of apnea events from ECG segments using Fourier decomposition method," Biomedical Signal Processing and Control, vol. 61, p. 102005, 2020.
[9] V. P. Rachim, G. Li, and W.-Y. Chung, "Sleep apnea classification using ECG-signal wavelet-PCA features," Bio-medical materials and engineering, vol. 24, no. 6, pp. 2875-2882, 2014.
[10] L. Chen, X. Zhang, and H. Wang, "An obstructive sleep apnea detection approach using kernel density classification based on single-lead electrocardiogram," Journal of medical systems, vol. 39, no. 5, pp. 1-11, 2015.
[11] A. R. Hassan, "A comparative study of various classifiers for automated sleep apnea screening based on single-lead electrocardiogram," in 2015 International Conference on Electrical & Electronic Engineering (ICEEE), 2015: IEEE, pp. 45-48.
[12] R. Atri and M. Mohebbi, "Obstructive sleep apnea detection using spectrum and bispectrum analysis of single-lead ECG signal," Physiological measurement, vol. 36, no. 9, p. 1963, 2015.
[13] T. Penzel et al., "Modulations of heart rate, ECG, and cardio-respiratory coupling observed in polysomnography," Frontiers in physiology, vol. 7, p. 460, 2016.
[14] S. Krishnan and Y. Athavale, "Trends in biomedical signal feature extraction," Biomedical Signal Processing and Control, vol. 43, pp. 41-63, 2018.
[15] S. Rezaei, S. Moharreri, N. J. Dabanloo, K. Maghooli, and S. Parvanch, "Sleep Apnea Detection Using Multi-Lag Poincare Plot," in 2021 Computing in Cardiology (CinC), 2021, vol. 48: IEEE, pp. 1-4.
[16] H. D. Nguyen, B. A. Wilkins, Q. Cheng, and B. A. Benjamin, "An online sleep apnea detection method based on recurrence quantification analysis," IEEE journal of biomedical and health informatics, vol. 18, no. 4, pp. 1285-1293, 2013.
[17] C. Cheng, C. Kan, and H. Yang, "Heterogeneous recurrence analysis of heartbeat dynamics for the identification of sleep apnea events," Computers in biology and medicine, vol. 75, pp. 10-18, 2016.
[18] S. Babaeizadeh, D. P. White, S. D. Pittman, and S. H. Zhou, "Automatic detection and quantification of sleep apnea using heart rate variability," Journal of electrocardiology, vol. 43, no. 6, pp. 535-541, 2010.
[19] P. Janbakhshi and M. Shamsollahi, "Sleep apnea detection from single-lead ECG using features based on ECG-derived respiration (EDR) signals," Irbm, vol. 39, no. 3, pp. 206-218, 2018.
[20] P. De Chazal, C. Heneghan, E. Sheridan, R. Reilly, P. Nolan, and M. O'Malley, "Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea," IEEE transactions on biomedical engineering, vol. 50, no. 6, pp. 686-696, 2003.
[21] C. Song, K. Liu, X. Zhang, L. Chen, and X. Xian, "An obstructive sleep apnea detection approach using a discriminative hidden Markov model from ECG signals," IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1532-1542, 2015.
[22] A. Zarei and B. M. Asl, "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, vol. 23, no. 3, pp. 1011-1021, 2018.
[23] A. R. Hassan and M. A. Haque, "An expert system for automated identification of obstructive sleep apnea from single-lead ECG using random under sampling boosting," Neurocomputing, vol. 235, pp. 122-130, 2017.
[24] A. R. Hassan, "Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting," Biomedical Signal Processing and Control, vol. 29, pp. 22-30, 2016.
[25] A. Nishad, R. B. Pachori, and U. R. Acharya, "Application of TQWT based filter-bank for sleep apnea screening using ECG signals," Journal of Ambient Intelligence and Humanized Computing, pp. 1-12, 2018.
[26] A. R. Hassan and M. A. Haque, "Computer-aided obstructive sleep apnea identification using statistical features in the EMD domain and extreme learning machine," Biomedical Physics & Engineering Express, vol. 2, no. 3, p. 035003, 2016.
[27] R. Tripathy, "Application of intrinsic band function technique for automated detection of sleep apnea using HRV and EDR signals," Biocybernetics and Biomedical Engineering, vol. 38, no. 1, pp. 136-144, 2018.
[28] S. H. El-Khafif and M. A. El-Brawany, "Artificial neural network-based automated ECG signal classifier," International Scholarly Research Notices, vol. 2013, 2013.
[29] W. Zhou, Z. Feng, Y. Xu, X. Wang, and H. Lv, "Empirical Fourier decomposition: An accurate signal decomposition method for nonlinear and non-stationary time series analysis," Mechanical Systems and Signal Processing, vol. 163, p. 108155, 2022.
[30] W. Zhou, Z. Feng, X. Wang, and H. Lv, "Empirical fourier decomposition," arXiv preprint arXiv:1912.00414, 2019.
[31] T. Penzel, G. B. Moody, R. G. Mark, A. L. Goldberger, and J. H. Peter, "The apnea-ECG database," in Computers in Cardiology 2000. Vol. 27 (Cat. 00CH37163), 2000: IEEE, pp. 255-258.
[32] https://physionet.org/content/apnea-ecg/1.0.0/. (accessed.
[33] A. Zarei and B. M. Asl, "Performance evaluation of the spectral autocorrelation function and autoregressive models for automated sleep apnea detection using single-lead ECG signal," Computer Methods and Programs in Biomedicine, vol. 195, p. 105626, 2020.
[34] Y. Liu et al., "Diagnosis of AF based on time and frequency features by using a hierarchical classifier," in 2017 Computing in Cardiology (CinC), 2017: IEEE, pp. 1-4.
[35] J. Li, I. Tobore, Y. Liu, A. Kandwal, L. Wang, and Z. Nie, "Non-invasive monitoring of three glucose ranges based on ECG by using DBSCAN-CNN," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 9, pp. 3340-3350, 2021.
[36] C. S. Viswabhargav, R. Tripathy, and U. R. Acharya, "Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals," Computers in biology and medicine, vol. 108, pp. 20-30, 2019.
[37] W. Zhou, Z. Feng, Y. Xu, X. Wang, and H. Lv, "Empirical Fourier Decomposition: An Accurate Adaptive Signal Decomposition Method," arXiv preprint arXiv:2009.08047, 2020.
[38] M. Sharma and R. B. Pachori, "A novel approach to detect epileptic seizures using a combination of tunable-Q wavelet transform and fractal dimension," Journal of Mechanics in Medicine and Biology, vol. 17, no. 07, p. 1740003, 2017.
[39] J. E. Jacob, G. K. Nair, A. Cherian, and T. Iype, "Application of fractal dimension for EEG based diagnosis of encephalopathy," Analog Integrated Circuits and Signal Processing, vol. 100, no. 2, pp. 429-436, 2019.
[40] U. R. Acharya, E. C.-P. Chua, O. Faust, T.-C. Lim, and L. F. B. Lim, "Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters," Physiological measurement, vol. 32, no. 3, p. 287, 2011.
[41] M. Bachmann, J. Lass, A. Suhhova, and H. Hinrikus, "Spectral asymmetry and Higuchi’s fractal dimension measures of depression electroencephalogram," Computational and mathematical methods in medicine, vol. 2013, 2013.
[42] C. F. Vega and J. Noel, "Parameters analyzed of Higuchi's fractal dimension for EEG brain signals," in 2015 Signal Processing Symposium (SPSympo), 2015: IEEE, pp. 1-5.
[43] R. Esteller, G. Vachtsevanos, J. Echauz, and B. Lilt, "A comparison of fractal dimension algorithms using synthetic and experimental data," in 1999 IEEE International Symposium on Circuits and Systems (ISCAS), 1999, vol. 3: IEEE, pp. 199-202.
[44] M. Bedeeuzzaman, T. Fathima, Y. U. Khan, and O. Farooq, "Seizure prediction using statistical dispersion measures of intracranial EEG," Biomedical Signal Processing and Control, vol. 10, pp. 338-341, 2014.
[45] O. Yaman, F. Ertam, and T. Tuncer, "Automated Parkinson’s disease recognition based on statistical pooling method using acoustic features," Medical Hypotheses, vol. 135, p. 109483, 2020.
[46] N. Rafiuddin, Y. U. Khan, and O. Farooq, "Feature extraction and classification of EEG for automatic seizure detection," in 2011 International Conference on Multimedia, Signal Processing and Communication Technologies, 2011: IEEE, pp. 184-187.
[47] J. Miao and L. Niu, "A survey on feature selection," Procedia Computer Science, vol. 91, pp. 919-926, 2016.
[48] S. Khalid, T. Khalil, and S. Nasreen, "A survey of feature selection and feature extraction techniques in machine learning," in 2014 science and information conference, 2014: IEEE, pp. 372-378.
[49] E. Pippa et al., "Improving classification of epileptic and non-epileptic EEG events by feature selection," Neurocomputing, vol. 171, pp. 576-585, 2016.
[50] J. Yang and R. Yan, "A multidimensional feature extraction and selection method for ECG arrhythmias classification," IEEE Sensors Journal, vol. 21, no. 13, pp. 14180-14190, 2020.
[51] G. Biau, L. Devroye, and G. Lugosi, "Consistency of random forests and other averaging classifiers," Journal of Machine Learning Research, vol. 9, no. 9, 2008.
[52] L. Fraiwan, K. Lweesy, N. Khasawneh, H. Wenz, and H. Dickhaus, "Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier," Computer methods and programs in biomedicine, vol. 108, no. 1, pp. 10-19, 2012.
[53] C. Donos, M. Dümpelmann, and A. Schulze-Bonhage, "Early seizure detection algorithm based on intracranial EEG and random forest classification," International journal of neural systems, vol. 25, no. 05, p. 1550023, 2015.
[54] C. Nguyen, Y. Wang, and H. N. Nguyen, "Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic," 2013.
[55] J. Kim, T. Kim, D. Lee, J.-W. Kim, and K. Lee, "Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification," Biomedical engineering online, vol. 16, pp. 1-18, 2017.
[56] H. Sharma and K. Sharma, "An algorithm for sleep apnea detection from single-lead ECG using Hermite basis functions," Computers in biology and medicine, vol. 77, pp. 116-124, 2016.
[57] M. Bahrami and M. Forouzanfar, "Sleep apnea detection from single-lead ECG: a comprehensive analysis of machine learning and deep learning algorithms," IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-11, 2022.
[58] K. Li, W. Pan, Y. Li, Q. Jiang, and G. Liu, "A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal," Neurocomputing, vol. 294, pp. 94-101, 2018.
[59] K. Feng, H. Qin, S. Wu, W. Pan, and G. Liu, "A sleep apnea detection method based on unsupervised feature learning and single-lead electrocardiogram," IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-12, 2020.
[60] S. A. Singh and S. Majumder, "A novel approach osa detection using single-lead ECG scalogram based on deep neural network," Journal of Mechanics in Medicine and Biology, vol. 19, no. 04, p. 1950026, 2019.
مجله مهندسی برق دانشگاه تبریز
دوره 53، شماره 3
آبان 1402
صفحه 159-170

فایل ها

هم رسانی

ارجاع به این مقاله

آمار