Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI Techniques
Source abstract
Abstract
Internet of Things (IoT) devices play a crucial role in the real-time acquisition of photoplethysmography (PPG) signals, facilitating seamless data transmission to cloud-based platforms for analysis. Atrial fibrillation (AF), affecting approximately 1–2% of the global population, requires accurate detection methods due to its prevalence and health impact. This study employs IoT devices to capture PPG signals and implements comprehensive preprocessing steps, including windowing, filtering, and artifact removal, to extract relevant features for classification. We explored a broad range of machine learning (ML) and deep learning (DL) approaches. Our results demonstrate superior performance, achieving an accuracy of 97.7%, surpassing state-of-the-art methods, including those with FDA clearance. Key strengths of our proposal include the use of shortened 15-second traces and validation using publicly available datasets. This research advances the design of cost-effective IoT devices for AF detection by leveraging diverse ML and DL techniques to enhance classification accuracy and robustness.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: ECG Monitoring and Analysis · Non-Invasive Vital Sign Monitoring · Atrial Fibrillation Management and Outcomes
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computer Science Department, Carlos III University of Madrid, Avda de la Universidad, 30, 28911 Leganés, Madrid, Spain
Funding
Spanish Ministry of Science, Innovation and Universities · INCIBE (in the context of the funds from the Recovery, Transformation, and Resilience Plan, financed by the European Union (Next Generation))
References (39)
Show source-provided references
1. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: Executive summary: A report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society10.1161/CIR.0000000000000040 ↗
2. Photoplethysmography based atrial fibrillation detection: A review10.1038/s41746-019-0207-9 ↗
3. Photoplethysmography and its application in clinical physiological measurement10.1088/0967-3334/28/3/R01 ↗
4. Tarniceriu, A., Harju, J., Yousefi, Z.R., Vehkaoja, A., Parak, J., Yli-Hankala, A., and Korhonen, I. (2018). The accuracy of atrial fibrillation detection from wrist photoplethysmography. A study on post-operative patients. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, Hawaii, 17–21 July 2018, IEEE.10.1109/EMBC.2018.8513197 ↗
5. Comparison between electrocardiogram-and photoplethysmogram-derived features for atrial fibrillation detection in free-living conditions10.1088/1361-6579/aad2c0 ↗
6. Can one detect atrial fibrillation using a wrist-type photoplethysmographic device?10.1007/s11517-018-1886-0 ↗
7. Diagnostic performance of a smart device with photoplethysmography technology for atrial fibrillation detection: Pilot study (Pre-mAFA II registry)10.2196/11437 ↗
8. Reiss, A., Schmidt, P., Indlekofer, I., and Van Laerhoven, K. (2018, January 8–12). PPG-based heart rate estimation with time-frequency spectra: A deep learning approach. Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, Singapore.10.1145/3267305.3274176 ↗
9. Schäck, T., Harb, Y.S., Muma, M., and Zoubir, A.M. (2017). Computationally efficient algorithm for photoplethysmography-based atrial fibrillation detection using smartphones. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Republic of Korea, 11–15 July 2017, IEEE.10.1109/EMBC.2017.8036773 ↗
10. Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms10.1136/heartjnl-2018-313147 ↗
11. Shashikumar, S.P., Shah, A.J., Clifford, G.D., and Nemati, S. (2018). Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks. arXiv.10.1145/3219819.3219912 ↗
12. Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation10.1088/1361-6579/ab5b84 ↗
13. Detecting Atrial Fibrillation in Real Time Based on PPG via Two CNNs for Quality Assessment and Detection10.1109/JSEN.2022.3217037 ↗
14. Han, D., Bashar, S.K., Zieneddin, F., Ding, E., Whitcomb, C., McManus, D.D., and Chon, K.H. (2020). Digital image processing features of smartwatch photoplethysmography for cardiac arrhythmia detection. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 20–24 July 2020, IEEE.10.1109/EMBC44109.2020.9176142 ↗
15. Petmezas, G., Haris, K., Stefanopoulos, L., Kilintzis, V., Tzavelis, A., Rogers, J.A., Katsaggelos, A.K., and Maglaveras, N. (2021). Automated atrial fibrillation detection using a hybrid CNN-LSTM network on imbalanced ECG datasets. Biomed. Signal Process. Control, 63.10.1016/j.bspc.2020.102194 ↗
16. Chen, X., Cheng, Z., Wang, S., Lu, G., Xv, G., Liu, Q., and Zhu, X. (2021). Atrial fibrillation detection based on multi-feature extraction and convolutional neural network for processing ECG signals. Comput. Methods Programs Biomed., 202.10.1016/j.cmpb.2021.106009 ↗
17. An IOT framework for detecting cardiac arrhythmias in real-time using deep learning resnet model10.1016/j.measen.2023.100866 ↗
18. Cinotti, E., Centracchio, J., Parlato, S., Andreozzi, E., Esposito, D., Muto, V., Bifulco, P., and Riccio, M. (2024). A Narrowband IoT Personal Sensor for Long-Term Heart Rate Monitoring and Atrial Fibrillation Detection. Sensors, 24.10.3390/s24144432 ↗
19. Non-invasive prediction of hemoglobin level using machine learning techniques with the PPG signal’s characteristics features10.1016/j.asoc.2015.04.008 ↗
20. Investigation of finger reflectance photoplethysmography in volunteers undergoing a local sympathetic stimulation
21. Charlton, P.H. (2024, September 24). MIMIC PERform Datasets (1.01) [Data Set]. Zenodo. Available online: https://zenodo.org/records/6807403.
22. Detecting beats in the photoplethysmogram: Benchmarking open-source algorithms10.1088/1361-6579/ac826d ↗
23. Moody, B., Moody, G., Villarroel, M., Clifford, G.D., and Silva, I. (2024, September 24). MIMIC-III Waveform Database Matched Subset (version 1.0); PhysioNet: 2020. Available online: https://physionet.org/content/mimic3wdb-matched/1.0/.
24. Noise Detection in Electrocardiogram Signals for Intensive Care Unit Patients10.1109/ACCESS.2019.2926199 ↗
25. Clifford, G.D., Azuaje, F., and McSharry, P. (2006). Advanced Methods and Tools for ECG Data Analysis, Artech House Boston.
26. Shan, S.M., Tang, S.C., Huang, P.W., Lin, Y.M., Huang, W.H., Lai, D.M., and Wu, A.Y.A. (2016). Reliable PPG-based algorithm in atrial fibrillation detection. Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS), Shanghai, China, 17–19 October 2016, IEEE.10.1109/BioCAS.2016.7833801 ↗
27. Park, J., Seok, H.S., Kim, S.S., and Shin, H. (2022). Photoplethysmogram Analysis and Applications: An Integrative Review. Front. Physiol., 12.10.3389/fphys.2021.808451 ↗
28. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
29. Ruiz, P. (2024, September 01). Understanding and Visualizing DenseNets. Available online: http://www.pabloruizruiz10.com/resources/CNNs/DenseNets.pdf.
30. Tihak, A., Konjicija, S., and Boskovic, D. (2022, January 15–16). Deep learning models for atrial fibrillation detection: A review. Proceedings of the 30th Telecommunications Forum (TELFOR), Belgrade, Serbia.10.1109/TELFOR56187.2022.9983698 ↗
31. Huang, C.W., and Ding, J.J. (2022, January 28–30). Atrial Fibrillation Detection Algorithm with Ratio Variation-Based Features. Proceedings of the 4th Eurasia Conference on IOT, Communication and Engineering (ECICE), Yunlin, Taiwan.10.1109/ECICE55674.2022.10042909 ↗
32. Abdelazez, M., Rajan, S., and Chan, A.D.C. (2020, January 20–24). Transfer Learning for Detection of Atrial Fibrillation in Deterministic Compressive Sensed ECG. Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.10.1109/EMBC44109.2020.9175813 ↗
33. Salinas-Martínez, R., De Bie, J., Marzocchi, N., and Sandberg, F. (2020, January 13–16). Automatic Detection of Atrial Fibrillation Using Electrocardiomatrix and Convolutional Neural Network. Proceedings of the 2020 Computing in Cardiology, Rimini, Italy.10.22489/CinC.2020.170 ↗
34. Bashar, S.K., Han, D., Hajeb-Mohammadalipour, S., Ding, E., Whitcomb, C., McManus, D.D., and Chon, K.H. (2019). Atrial fibrillation detection from wrist photoplethysmography signals using smartwatches. Sci. Rep., 9.10.1038/s41598-019-49092-2 ↗
35. Optimized signal quality assessment for photoplethysmogram signals using feature selection10.1109/TBME.2022.3158582 ↗
36. Aldughayfiq, B., Ashfaq, F., Jhanjhi, N., and Humayun, M. (2023). A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG. Diagnostics, 13.10.3390/diagnostics13142442 ↗
37. Detection of Atrial Fibrillation from PPG Sensor Data using Variational Mode Decomposition10.1109/LSENS.2024.3358589 ↗
38. Evaluation of Atrial Fibrillation Detection in short-term Photoplethysmography (PPG) signals using artificial intelligence
39. Voisin, M., Shen, Y., Aliamiri, A., Avati, A., Hannun, A., and Ng, A. (2018). Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning. arXiv.