International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Based Healthcare System for Detecting Sleep Apnea |
|---|---|
| ABSTRACT | Sleep Apnea Is A Common But Often Undiagnosed Sleep Disorder In Which A Person’s Breathing Repeatedly Stops And Starts During Sleep, Leading To Serious Health Problems Such As Heart Disease, High Blood Pressure, And Extreme Fatigue. The Standard Method Used For Diagnosis, Called Polysomnography (Psg), Is Expensive, Time-Consuming, And Requires Patients To Stay Overnight In A Hospital, Making It Difficult For Regular And Large-Scale Use. To Address These Limitations, This Paper Proposes An Ai-Based Healthcare System That Detects Obstructive Sleep Apnea (Osa) Using Electrocardiogram (Ecg) Signals. In This System, Raw Ecg Data Is First Cleaned Using Filtering Techniques To Remove Noise And Unwanted Disturbances, Then Divided Into Smaller Segments For Better Analysis. Important Features Related To Heart Activity, Known As Heart Rate Variability (Hrv), Are Extracted From Both Time And Frequency Domains. These Features Are Then Used To Train Multiple Machine Learning Models, Including Support Vector Machine (Svm), Random Forest (Rf), And Logistic Regression. To Improve Accuracy And Reliability, These Models Are Combined Using A Stacking Ensemble Method With A Meta-Classifier, Which Produces The Final Prediction. The System Classifies TheEcg Data IntoApneaOr Non-Apnea Conditions With High Accuracy. Overall, The Proposed Solution Is Low-Cost, Non-Invasive, And Suitable For Real-Time And Home-Based Monitoring, Making It Highly Useful For Early Detection And Effective Management Of Sleep Apnea. |
| AUTHOR | K.MUTHU LAKSHMI, ANEBOINA HEMA KRISHNA, BATCHU SANJAY RAJ, BOLISHETTI RAHUL, PARTHIREDDY PRANAY Dept. of Computer Science and Engineering, Bharath Institute Of Higher Education and Research (BIHER), Chennai, Tamil Nadu, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404129 |
| pdf/129_AI-Based Healthcare System for Detecting Sleep Apnea.pdf | |
| KEYWORDS | |
| References | [1] T. Penzel, G. Moody, R. Mark, A. Goldberger, and J. Peter, “The Apnea-ECG Database,” Computers in Cardiology, vol. 27, pp. 255–258, 2000. [2] P. de Chazal, T. Penzel, and C. Heneghan, “Automated Detection of Obstructive Sleep Apnea Using ECG Features,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 6, pp. 686–696, 2003. [3] B. Xie, H. Minn, and Y. Hu, “Automatic Sleep Apnea Detection Using ECG Signal and Support Vector Machine,” IEEE Access, vol. 7, pp. 48560–48570, 2019. [4] L. Wang, X. Li, and Y. Zhang, “Sleep Apnea Detection from Single-Lead ECG Using Ensemble Machine Learning Models,” Computers in Biology and Medicine, vol. 124, p. 103919, 2020. [5] Q. Zhang, L. Zhao, and M. Chen, “A Stacking Ensemble Framework for ECG-Based Sleep Apnea Classification,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 5, pp. 1642–1652, 2021. [6] K. Li, J. Wu, and H. Liu, “Deep Learning Approach for Obstructive Sleep Apnea Detection Using ECG Signals,” Biomedical Signal Processing and Control, vol. 45, pp. 1–9, 2018. [7] M. Rizwan, A. Sharma, and S. Kumar, “Deep Learning-Based Obstructive Sleep Apnea Detection Using Single-Lead ECG Signals,” Biomedical Signal Processing and Control, vol. 68, p. 102762, 2021. [8] B. Yilmaz and M. H. Asyali, “Sleep Apnea Detection from ECG Using Wavelet Transform and Neural Networks,” Expert Systems with Applications, vol. 37, no. 7, pp. 5817–5824, 2010. [9] D. Alvarez, R. Hornero, M. García, and F. del Campo, “Feature Selection from ECG for Obstructive Sleep Apnea Detection,” Medical Engineering & Physics, vol. 29, no. 7, pp. 831–841, 2007. [10] A. H. Khandoker, C. K. Karmakar, and M. Palaniswami, “Automated Recognition of Obstructive Sleep Apnea Using Heart Rate Variability Analysis,” Physiological Measurement, vol. 30, no. 10, pp. 1035–1049, 2009. [11] 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, pp. 45–50, 2001. [12] S. Faust, P. Acharya, and H. Adeli, “Wavelet-Based EEG Processing for Computer-Aided Seizure Detection and Epilepsy Diagnosis,” IEEE Reviews in Biomedical Engineering, vol. 8, pp. 1–20, 2015. [13] H. Hannun, P. Rajpurkar, M. Haghpanahi, et al., “Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks,” Nature Medicine, vol. 25, no. 1, pp. 65–69, 2019. [14] D. Attia, P. Noseworthy, F. Lopez-Jimenez, et al., “An Artificial Intelligence-Enabled ECG Algorithm for Identification of Atrial Fibrillation,” The Lancet, vol. 394, no. 10201, pp. 861–867, 2019. [15] A. Esteva, B. Kuprel, R. A. Novoa, et al., “Dermatologist-Level Skin Cancer Classification with Deep Neural Networks,” Nature, vol. 542, pp. 115–118, 2017. [16] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016. [17] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, pp. 436–444, 2015. [18] C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. [19] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [20] J. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001. [21] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in KDD, 2016, pp. 785–794. [22] R. Polikar, “Ensemble Learning,” in Ensemble Machine Learning, Springer, 2012, pp. 1–34. [23] Z. Zhou, Ensemble Methods: Foundations and Algorithms, Chapman and Hall/CRC, 2012. [24] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. [25] A. Graves, “Supervised Sequence Labelling with Recurrent Neural Networks,” Springer, 2012. [26] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” ICLR, 2015. [27] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in CVPR, 2016, pp. 770–778. [28] A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” NeurIPS, 2012. [30] D. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” ICLR, 2015. |
