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
  • [1] Acharya U.R., Chua E.C., Faust O., Lim T.C., Lim L.F., Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters, Physiol Meas, 32, pp. 287-303, (2011)
  • [2] Acharya U.R., Dua S., Du X., Sree S.V., Chua C.K., Automated diagnosis of glaucoma using texture and higher order spectra features, IEEE Trans Inf Technol Biomed, 15, pp. 449-455, (2011)
  • [3] Acharya U.R., Ng E.Y.K., Eugene L.W.J., Noronha K.P., Min L.C., Nayak K.P., Bhandary S.V., Decision support system for the glaucoma using Gabor transformation, Biomed Signal Process Control, 15, pp. 18-26, (2015)
  • [4] Acharya U.R., Sudarshan V.K., Ghista D.N., Lim W.J.E., Molinari F., Sankaranarayanan M., Computer aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method, Knowl Based Syst, 81, pp. 56-64, (2015)
  • [5] Acharya U.R., Fujita H., Lih O.S., Hagiwara Y., Tan J.H., Adam M., Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network, Inf Sci, 405, pp. 81-90, (2017)
  • [6] Acharya U.R., Fujita H., Oh S.L., Hagiwara Y., Tan J.H., Adam M., Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals, Inf Sci, 415-416, pp. 190-198, (2017)
  • [7] Acharya U.R., Oh S.L., Hagiwara Y., Tan J.H., Adam M., Gertych A., Tan R.S., A deep convolutional neural network model to classify heartbeats, Comput Biol Med, 89, pp. 389-396, (2017)
  • [8] Afkhami R.G., Azarnia G., Tinati M.A., Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals, Pattern Recogn Lett, 70, pp. 45-51, (2016)
  • [9] Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research, Sleep, 22, pp. 667-689, (1999)
  • [10] Arqub O.A., Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm–Volterra integrodifferential equations, Neural Comput Appl, 28, pp. 1591-1610, (2017)
1234567