Time Series Classification for Portable Medical Devices
DOI:
https://doi.org/10.4108/eetsis.v10i3.3219Keywords:
time series classification, autoencoder, attention mechanism, Medical mobile information systemsAbstract
INTRODUCTION: With the continuous progress of the medical Internet of Things, intelligent medical wearable devices are also gradually mature. Among them, medical wearable devices for arrhythmia detection have broad application prospects. Arrhythmia is a common cardiovascular disease. Arrhythmia causes millions of deaths every year and is one of the most noteworthy diseases. Medical mobile information systems (MMIS) provide many ECG signals, which can be used to train deep models to detect arrhythmia automatically.
OBJECTIVES: Using deep models to detect arrhythmia is a research hot spot. However, the current algorithms for arrhythmia detection lack of attention to the unsupervised depth model. And they usually build a large comprehensive model for all users for arrhythmia detection, which has low flexibility and cannot extract personalized features from users. Therefore, this paper proposes a personalized arrhythmia detection system based on attention mechanism called personAD.
METHODS: The personAD contains four modules: (1) Preprocessing module; (2) Training module; (3) Arrhythmia detection module and (4) User registration module. The personAD trains a separate autoencoder for each user to detect personalized arrhythmia. Using autoencoder to detect arrhythmia can avoid the imbalance of training data. The autoencoder combines a convolutional network and two attention mechanisms.
RESULTS: Based on the results on MIT-BIH Arrhythmia Database, we can find that our arrhythmia detection system achieve 98.03% and 99.32% respectively.
CONCLUSION: The personAD can effectively detect arrhythmia in ECG signals. The personAD has higher flexibility, and can easily modify the autoencoders for detecting arrhythmia for users.
References
Li W. Big Data precision marketing approach under IoT cloud platform information mining. Computational Intelligence and Neuroscience. 2022; 2022.
Li H, Liu J, Wu K, et al. Spatio-temporal vessel trajectory clustering based on data mapping and density. IEEE Access. 2018; 6: 58939-58954.
Zhang Y, Zhao Y, Zhou Y. User-centered cooperative-communication strategy for 5G Internet of vehicles. IEEE Internet of Things Journal. 2022; 9(15): 13486-13497.
Abd Elaziz M, Abualigah L, Ibrahim R A, et al. IoT workflow scheduling using intelligent arithmetic optimization algorithm in fog computing. Computational intelligence and neuroscience. 2021; 2021: 1-14.
Huang S, Liu A, Zhang S, et al. BD-VTE: a novel baseline data based verifiable trust evaluation scheme for smart network systems. IEEE transactions on network science and engineering. 2020; 8(3): 2087-2105.
Ahn N Y, Lee D H. Security of IoT device: perspective forensic/anti-forensic issues on invalid area of NAND flash memory. IEEE Access. 2022; 10: 74207-74219.
Nath R, Nath H V. Critical analysis of the layered and systematic approaches for understanding IoT security threats and challenges. Computers and Electrical Engineering. 2022; 100: 107997.
Qu Z, Sun H, Zheng M. An efficient quantum image steganography protocol based on improved EMD algorithm. Quantum Information Processing. 2021; 20: 1-29.
Bringhenti D, Yusupov J, Zarca A M, et al. Automatic, verifiable and optimized policy-based security enforcement for SDN-aware IoT networks. Computer Networks. 2022; 213: 109123.
Roy S, Vo T, Hernandez S, et al. IoT Security and Computation Management on a Multi-Robot System for Rescue Operations Based on a Cloud Framework. Sensors. 2022; 22(15): 5569.
Liu X, Xie C, Xie W, et al. Security performance analysis of RIS-assisted UAV wireless communication in industrial IoT. The Journal of Supercomputing. 2022; 2022: 1-17.
Bilgehan B, Kayed L, Sabuncu Ö. General probability distribution model for wireless body sensors in the medical monitoring system. Biomedical Signal Processing and Control. 2022; 77: 103777.
Akbari H, Sadiq M T, Siuly S, et al. An automatic scheme with diagnostic index for identification of normal and depression EEG signals. In: Health Information Science. 10th International Conference; 25-28 October 2021; Melbourne, VIC, Australia. Melbourne: Springer International Publishing; 2021. p. 59-70.
Kiranyaz S, Ince T, Gabbouj M. Personalized monitoring and advance warning system for cardiac arrhythmias. Scientific reports. 2017; 7(1): 9270.
Sun L, Zhou R, Peng D, et al. Automatically building service-based systems with function relaxation. IEEE Transactions on Cybernetics. 2022.
Tan W, Huang P, Li X, et al. Analysis of segmentation of lung parenchyma based on deep learning methods. Journal of X-ray science and technology. 2021; 29(6): 945-959.
Tan W, Zhou L, Li X, et al. Automated vessel segmentation in lung CT and CTA images via deep neural networks. Journal of X-ray science and technology. 2021; 29(6): 1123-1137.
Salam K A, Srilakshmi G. An algorithm for ECG analysis of arrhythmia detection. In: 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT); 5-7 March 2015 ; Coimbatore, India. Coimbatore: IEEE; 2015. p. 1-6.
Chen Z, Luo J, Lin K, et al. An energy-efficient ECG processor with weak-strong hybrid classifier for arrhythmia detection. IEEE Transactions on Circuits and Systems II: Express Briefs. 2017; 65(7): 948-952.
Rajpurkar P, Hannun A Y, Haghpanahi M, et al. Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv preprint arXiv:1707.01836, 2017.
Acharya U R, Fujita H, Oh S L, et al. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences. 2017; 415: 190-198.
Ochiai K, Takahashi S, Fukazawa Y. Arrhythmia detection from 2-lead ECG using convolutional denoising autoencoders. In: Proc. KDD; August 19-23 2018; London, UK. London: ACM Digital Library; 2018. p. 1-7.
Thill M, Konen W, Wang H, et al. Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing. 2021; 112: 107751.
Hou B, Yang J, Wang P, et al. LSTM-based auto-encoder model for ECG arrhythmias classification. IEEE Transactions on Instrumentation and Measurement. 2019; 69(4): 1232-1240.
Kiranyaz S, Ince T, Gabbouj M. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Transactions on Biomedical Engineering. 2015; 63(3): 664-675.
Zubair, M.; Kim, J.; Yoon, C. An automated ECG beat classification system using convolutional neural networks. In: Proceedings of the 2016 6th international conference on IT convergence and security (ICITCS); 26-29 September 2016; Prague, Czech Republic: IEEE; 2016. p. 1–5.
Chauhan S, Vig L. Anomaly detection in ECG time signals via deep long short-term memory networks. In: 2015 IEEE international conference on data science and advanced analytics (DSAA); 22-24 October 2015; St. Etienne, Paris: IEEE; 2015. p. 1-7.
Xu G, Xing G, Jiang J, et al. Arrhythmia detection using gated recurrent unit network with ECG signals. Journal of Medical Imaging and Health Informatics. 2020; 10(3): 750-757.
Pandey S K, Janghel R R. Automated detection of arrhythmia from electrocardiogram signal based on new convolutional encoded features with bidirectional long short-term memory network classifier. Physical and Engineering Sciences in Medicine. 2021; 44: 173-182.
Fujiwara Y, Kanai S, Arai J, et al. Efficient data point pruning for one-class SVM. In: Proceedings of the AAAI Conference on Artificial Intelligence; 27 January-1 February 2019; Hilton Hawaiian Village, Honolulu, Hawaii, USA: AAAI; 2019. p.3590-3597.
Chalapathy R, Menon A K, Chawla S. Anomaly detection using one-class neural networks. arXiv preprint arXiv:1802.06360, 2018.
Ruff L, Vandermeulen R, Goernitz N, et al. Deep one-class classification In: 35th International conference on machine learning; 10-15 July 2018; Stockholmsmässan, Stockholm Sweden: PMLR; 2018. 4393-4402.
Borghesi A, Bartolini A, Lombardi M, et al. Anomaly detection using autoencoders in high performance computing systems. In: Proceedings of the AAAI Conference on artificial intelligence. 27 January-1 February 2019; Hilton Hawaiian Village, Honolulu, Hawaii, USA: AAAI; 2019. p. 9428-9433.
Sun L, Zhong Z, Qu Z, et al. PerAE: an effective personalized AutoEncoder for ECG-based biometric in augmented reality system. IEEE journal of biomedical and health informatics. 2022; 26(6): 2435-2446.
Madan P, Singh V, Singh D P, et al. Denoising of ECG signals using weighted stationary wavelet total variation. Biomedical Signal Processing and Control. 2022; 73: 103478.
Merah M, Abdelmalik T A, Larbi B H. R-peaks detection based on stationary wavelet transform. Computer methods and programs in biomedicine. 2015; 121(3): 149-160.
Moody G B, Mark R G. The impact of the MIT-BIH arrhythmia database. IEEE engineering in medicine and biology magazine. 2001; 20(3): 45-50.
Homayouni H, Ray I, Ghosh S, et al. Anomaly detection in COVID-19 time-series data. SN Computer Science. 2021; 2(4): 279.
Zhao H, Li Y, He N, et al. Anomaly detection for medical images using self-supervised and translation-consistent features. IEEE Transactions on Medical Imaging. 2021; 40(12): 3641-3651.
Shvetsova N, Bakker B, Fedulova I, et al. Anomaly detection in medical imaging with deep perceptual autoencoders. IEEE Access. 2021; 9: 118571-118583.
Han C, Rundo L, Murao K, et al. MADGAN: Unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction. BMC bioinformatics. 2021; 22(2): 1-20.
Fernando T, Denman S, Ahmedt-Aristizabal D, et al. Neural memory plasticity for medical anomaly detection. Neural Networks. 2020; 127: 67-81.
Sarki R, Ahmed K, Wang H, et al. Convolutional neural network for multi-class classification of diabetic eye disease. EAI Endorsed Transactions on Scalable Information Systems. 2022; 9(4): e5-e5.
He J, Rong J, Sun L, et al. A framework for cardiac arrhythmia detection from IoT-based ECGs. World Wide Web. 2020; 23: 2835-2850.
Supriya S, Siuly S, Wang H, et al. Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Information Science and Systems. 2020; 8: 1-15.
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