Application of TQWT based filter-bank for sleep apnea screening using ECG signals

被引：43
作者：
Nishad A. [1 ]
Pachori R.B. [1 ]
Acharya U.R. [2 ,3 ,4 ]
机构：
[1] Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore
[2] Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
[3] Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore
[4] Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur
来源：
Journal of Ambient Intelligence and Humanized Computing | 2024年 / 15卷 / 01期
关键词：
Centered correntropy; Electrocardiogram signals; Random forest classifier; Sleep apnea; Tunable-Q wavelet transform;
D O I：
10.1007/s12652-018-0867-3
中图分类号：
学科分类号：
摘要：
The sleep apnea is a disease in which there is the absence of airflow during respiration for at least 10 s. It may occur several times during the night sleep. This disease can lead to many types of cardiovascular diseases. To detect this disease, signals obtained from many channels of polysomnography are to be observed visually by physicians for the long duration. This procedure is expensive, time-consuming, and subjective. Hence, it is required to build an automated system to detect the sleep apnea with few channels. This paper uses single-lead electrocardiogram (ECG) signal to detect apneic and non-apneic events. The proposed method uses tunable-Q wavelet transform (TQWT) based filter-bank instead of TQWT to decompose the segment of ECG signal into several constant bandwidth sub-band signals. Then centered correntropies are computed from the various sub-band signals. The obtained features are then fed to the various classifiers to select the optimum performing classifier. In this work, we have obtained the highest classification accuracy, specificity, and sensitivity of 92.78%, 93.91%, and 90.95% respectively using random forest classifier. Hence, our developed prototype is ready for validation with the huge database and clinical usage. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.
引用
收藏
页码：893 / 904
页数：11
相关论文
共 70 条
  • [11] Arqub O.A., Abo-Hammour Z., Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm, Inf Sci, 279, pp. 396-415, (2014)
  • [12] Azar A.T., El-Said S.A., Performance analysis of support vector machines classifiers in breast cancer mammography recognition, Neural Comput Appl, 24, pp. 1163-1177, (2014)
  • [13] Azarbarzin A., Moussavi Z., Snoring sounds variability as a signature of obstructive sleep apnea, Med Eng Phys, 35, pp. 479-485, (2013)
  • [14] Bhattacharyya A., Pachori R.B., Acharya U.R., Tunable-Q wavelet transform based multivariate sub-band fuzzy entropy with application to focal EEG signal analysis, Entropy, 19, (2017)
  • [15] Bhattacharyya A., Pachori R.B., Upadhyay A., Acharya U.R., Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals, Appl Sci, 7, (2017)
  • [16] Box J.F., Guinness, gosset, fisher, and small samples, Stat Sci, 2, pp. 45-52, (1987)
  • [17] Breiman L., Bagging predictors, Mach Learn, 24, pp. 123-140, (1996)
  • [18] Breiman L., Random forests, Mach Learn, 45, pp. 5-32, (2001)
  • [19] Chen L., Zhang X., Song C., An automatic screening approach for obstructive sleep apnea diagnosis based on single-lead electrocardiogram, IEEE Trans Autom Sci Eng, 12, pp. 106-115, (2015)
  • [20] Chen L., Zhang X., Wang H., An obstructive sleep apnea detection approach using kernel density classification based on single-lead electrocardiogram, J Med Syst, 39, (2015)
1234567