Abstract
Atrial fibrillation detection (AFD) has attracted much attention in the field of embedded systems. In this study, we propose a sorting fuzzy min-max (SFMM) model, and then develop an SFMM-based embedded system for AF detection. The proposed SFMM model is essentially enhanced the fuzzy min-max (FMM) model that have been successfully applied in many classification fields. In comparison with the typical FMM model, the proposed SFMM model can overcome the limitation of the input order problem encountered in the typical FMM model. The embedded system consists of a control chip and an analog-digital conversion (ADC) chip. The STM32F407 chip is used as the control chip and the ADS1292 chip, which has a high common-mode rejection ratio (CMRR), is used as the ADC chip. A series of machine learning benchmarks are included to evaluate the performance of the SFMM model. Experimental results on AF data further demonstrate the effectiveness of the SFMM-based embedded system.
- [1] . 2021. Empowering things with intelligence: A survey of the progress, challenges, and opportunities in artificial intelligence of things. In Proceedings of the IEEE Internet of Things Journal 8, 10 (2021), 7789–7817.
DOI: Google ScholarCross Ref
- [2] D. González-Ortega, F. J. Díaz-Pernas, M. Martínez-Zarzuela, and M. Antón-Rodríguez. 2014. A Kinect-based system for cognitive rehabilitation exercises monitoring. Computer Methods and Programs in Biomedicine 113, 2 (2014), 620–631.
DOI: Google ScholarDigital Library
- [3] Tran Quang Trung and Nae-Eung Lee. 2016. Flexible and stretchable physical sensor integrated platforms for wearable human-activity monitoring and personal healthcare. Advanced Materials 28, 22 (2016), 4338–4372.
DOI: Google ScholarCross Ref
- [4] . 2017. Super normal vector for human activity recognition with depth cameras. In Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 5 (2017), 1028–1039.
DOI: Google ScholarDigital Library
- [5] Eleni Kroupi, Ashkan Yazdani, Jean-Marc Vesin, and Touradj Ebrahimi. 2014. EEG correlates of pleasant and unpleasant odor perception. ACM Transactions on Multimedia Computing, Communications, and Applications 11, 1 (2014), 1–17.
DOI: Google ScholarDigital Library
- [6] Rodrigo Ceballos, Beatrice Ionascu, Wanjoo Park, and Mohamad Eid. 2017. Implicit emotion communication: EEG classification and haptic feedback. ACM Transactions on Multimedia Computing, Communications, and Applications 14, 1 (2017), 1–18.
DOI: Google ScholarDigital Library
- [7] Annemijn H. Jonkman, Ricardo Juffermans, Jonne Doorduin, Leo M. A. Heunks, and Jaap Harlaar. 2021. Estimated ECG Subtraction method for removing ECG artifacts in esophageal recordings of diaphragm EMG. Biomedical Signal Processing and Control 69, 1 (2021), 102861–102870.
DOI: Google ScholarCross Ref
- [8] Sumeet S. Chugh, Rasmus Havmoeller, Kumar Narayanan, David Singh, Michiel Rienstra, Emelia J. Benjamin, Richard F. Gillum, and Young-Hoon K. 2014. Worldwide epidemiology of atrial fibrillation: A global burden of disease 2010 Study. Circulation 129, 8 (2014), 837–847.
DOI: Google ScholarCross Ref
- [9] Selcan Kaplan Berkaya, Alper Kursat Uysal, Efnan Sora Gunal, Semih Ergin, Serkan Gunal M., and Bilginer Gulmezoglu. 2018. A survey on ECG analysis, Biomed. Signal Process 43, 1 (2018), 216–235.
DOI: Google ScholarCross Ref
- [10] Fenghuan Li, Kehai Chen, Jie Ling, Yinwei Zhan, and Gunasekaran Manogaran. 2019. Automatic diagnosis of cardiac arrhythmia in electrocardiograms via multi-granulation computing. Applied Soft Computing 80, 1 (2019), 400–413.
DOI: Google ScholarDigital Library
- [11] Matteo Anselmino, Alberto Battaglia, Cristina Gallo, Sebastiano Gili, Mario Matta, Davide Castagno, Federico Ferraris, and Carla Giustetto. 2015. Atrial fibrillation and female sex. Journal of Cardiovascular Medicine 16, 12 (2015), 795–801.
DOI: Google ScholarCross Ref
- [12] . 2010. An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation. IEEE Transactions on Information Technology in Biomedicine 14, 3 (2010), 726–733.Google Scholar
Digital Library
- [13] . 2021. Design and implementation of A Low-Cost ECG monitoring system using ARM Cortex-M4 family microcontroller. In Proceedings of the 2021 IEEE International Conference on Consumer Electronics. 1–2.Google Scholar
Cross Ref
- [14] Huey Woan Lim, Yuan Wen Hau, Mohd Afzan Othman, and Chiao Wen Lim. 2017. Embedded system-on-chip design of atrial fibrillation classifier. In Proceedings of the 2017 International SoC Design Conference. IEEE, (2017), 90–91.
DOI: Google ScholarCross Ref
- [15] . 2002. Julio Ortega: Multi-objective optimization evolutionary algorithms applied to paroxysmal atrial fibrillation diagnosis based on the k-Nearest neighbours classifier. In Proceedings of the Ibero-American Conference on Artificial Intelligence. Springer, Berlin, 313–318.Google Scholar
- [16] . 2007. Mustafa Okandan: Atrial fibrillation classification with artificial neural networks. Pattern Recognition 40, 11 (2007), 2967–2973.Google Scholar
Digital Library
- [17] U. Rajendra Acharya, Hamido Fujita, Oh Shu Lih, Yuki Hagiwara, Jen Hong Tan, and Muhammad Adam. 2017. Automated detection of arrhythmias using different intervals of tachy-cardia ECG segments with convolutional neural network. Information Science 405, 1 (2017), 81–90.
DOI: Google ScholarDigital Library
- [18] Rasmus S. Andersen, Abdolrahman Peimankar, and Sadasivan Puthusserypady. 2019. A deep learning approach for real-time detection of atrial fibrillation. Expert Systems with Applications 115, 1 (2019), 465–473.
DOI: Google ScholarCross Ref
- [19] Jibin Wang. 2020. Automated detection of atrial fibrillation and atrial flutter in ECG signals based on convolutional and improved Elman neural network. Knowledge-Based Systems 193, 1 (2020), 1–10.
DOI: Google ScholarDigital Library
- [20] Georgios Petmezas, Kostas Haris, Leandros Stefanopoulos, Vassilis Kilintzis, Andreas Tzavelis, John A. Rogers, Aggelos K. Katsaggelos, and Nicos Maglaveras. 2021. Automated atrial fibrillation detection using a hybrid CNN- LSTM network on imbalanced ECG Datasets. Biomedical Signal Processing and Control 63, 1 (2021), 102194–102203.
DOI: Google ScholarCross Ref
- [21] Sajad Mousavi, Fatemeh Afghah U., and Rajendra Acharya. 2020. HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks. Computers in Biology and Medicine 127, 1 (2020), 104057–104066.
DOI: Google ScholarDigital Library
- [22] Ashkan Parsi, Martin Glavin, Edward Jones, and Dallan Byrne. 2021. Prediction of paroxysmal atrial fibrillation using new heart rate variability features. Comput. Biol. Medicine 133, 1 (2021), 104367–104378.
DOI: Google ScholarDigital Library
- [23] Jing Zhang, Jing Tian, Yang Cao, Yuxiang Yang, and Xiaobin Xu. 2020. Deep time.frequency representation and progressive decision fusion for ECG classification. Knowledge-Based Systems 190, 1 (2020), 105402–105412.
DOI: Google ScholarDigital Library
- [24] . 1992. Fuzzy min-max neural networks. I. classification. IEEE Transactions on Neural Networks 3, 5 (1992), 776–786.Google Scholar
Digital Library
- [25] . 2021. Self-Promoted prototype refinement for few-shot class-incremental learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6801–6810Google Scholar
Cross Ref
- [26] . 2022. Two-level residual distillation based triple network for incremental object detection. ACM Transactions on Multimedia Computing, Communications, and Applications 18, 1 (2022), 1–23.Google Scholar
Digital Library
- [27] . 2021. Image De-Raining via continual learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4907–4916.Google Scholar
Cross Ref
- [28] . 2020. An improved fuzzy min-Max neural network for data classification. IEEE Transactions on Fuzzy Systems 28, 9 (2020), 1910–1924.Google Scholar
Cross Ref
- [29] Patricia Melin, Jonathan Amezcua, Fevrier Valdez, and Oscar Castillo. 2014. A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias. Information Science 279, 1 (2014), 483–497.
DOI: Google ScholarCross Ref
- [30] . 2013. Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Systems with Applications 40, 8 (2013), 3196–3206.Google Scholar
Digital Library
- [31] . 2009. A neural network based multi-agent classifier system. Neurocomputing 72, 7 (Mar. 2009), 1639–1647.Google Scholar
Digital Library
- [32] . 2000. General fuzzy min-max neural network for clustering and classification. IEEE Transactions on Neural Networks 11, 3 (2000), 769–783.Google Scholar
Digital Library
- [33] . 2004. An inclusion/exclusion fuzzy hyperbox classifier. International Journal of Knowledge-based and Intelligent Engineering Systems 8, 2 (Aug. 2004), 91–98.Google Scholar
Digital Library
- [34] . 2005. A weighted fuzzy min-max neural network and its application to feature analysis. International Conference on Natural Computation. Springer, Berlin, 1178–1181.Google Scholar
Digital Library
- [35] . 2011. Data-Core based fuzzy min-max neural network for pattern classification. IEEE Transactions on Neural Networks 22, 12 (2011), 2339–2352.Google Scholar
Digital Library
- [36] . 2015. An enhanced fuzzy min-max neural network for pattern classification. IEEE Transactions on Neural Networks and Learning Systems 26, 3 (2015), 417–429.Google Scholar
Cross Ref
- [37] . 2008. Application of the fuzzy min-max neural networks to medical diagnosis. In Proceedings of the International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. Springer, Berlin, 548–555.Google Scholar
Digital Library
- [38] . 2006. A weighted FMM neural network and its application to face detection. In Proceedings of the International Conference on Neural Information Processing. Springer, Berlin, 177– 186.Google Scholar
Digital Library
- [39] . 2016. Role of Hyperbox Classifiers for Color Recognition. In Proceedings of the IIT Guwahati. 1–2.Google Scholar
- [40] Hongyu Wang, Dandan Zhang, Songtao Ding, Zhanyi Gao, Jun Feng, and Shaohua Wan. 2021. Rib segmentation algorithm for X-ray image based on unpaired sample augmentation and multi-scale network. Neural Comput & Applic 1, 1 (2021), 1–15.
DOI: Google ScholarCross Ref
- [41] Panagiotis Radoglou-Grammatikis, Konstantinos Rompolos, Panagiotis Sarigiannidis, Vasileios Argyriou, Thomas Lagkas, and Antonios Sarigia. 2021. Modeling, detecting, and mitigating threats against industrial health- care systems: a combined software defined networking and reinforcement learning approach. IEEE Transactions on Industrial Informatics 18, 3 (2021), 2041–2052.
DOI: Google ScholarCross Ref
- [42] Yue Zhang, Fanghui Zhang, Yi Jin, Yigang Cen, Viacheslav Voronin, and Shaohua Wan. 2022. Local Correlation Ensemble with GCN based on Attention Features for Cross-domain Person Re-ID. ACM Trans. Multimedia Comput. Commun. Appl. 1, 1 (2022), 1–22.
DOI: Google ScholarDigital Library
- [43] M. Shamim Hossain, Syed Umar Amin, Mansour Alsulaiman, and Ghulam Muhammad. 2019. Applying deep learning for epilepsy seizure detection and brain mapping visualization. ACM Transactions on Multimedia Computing, Communications, and Applications 15, 1 (2019), 1–17.
DOI: Google ScholarDigital Library
Index Terms
A Sorting Fuzzy Min-Max Model in an Embedded System for Atrial Fibrillation Detection
Recommendations
Prediction of atrial fibrillation using the recurrence complex network of body surface potential mapping signals
The 7th International Conference on Biomedical Engineering and BiotechnologyOBJECTIVE:Atrial fibrillation (AF) is the most common type of persistent arrhythmia. Early diagnosis and intervention of AF is essential to avert the further fatality. The technique of noninvasive electrical mapping, ...
An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation
Special section on new and emerging technologies in bioinformatics and bioengineeringThis study presents a novelwireless, ambulatory, realtime, and autoalarm intelligent telecardiology system to improve healthcare for cardiovascular disease, which is one of the most prevalent and costly health problems in the world. This system consists ...
How many leads are necessary for a reliable reconstruction of surface potentials during atrial fibrillation?
In this study, we aimed at determining how many leads are necessary for accurately reconstructing ECG potentials during atrial fibrillation (AF) on the body surface. Although the standard ECG is appropriate for the detection of this arrhythmia, its ...






Comments