The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring
Abstract
In this paper, we propose a novel signal quality-aware Internet of Things (IoT)-enabled electrocardiogram (ECG) telemetry system for continuous cardiac health monitoring applications. The proposed quality-aware ECG monitoring system consists of three modules: 1) ECG signal sensing module; 2) automated signal quality assessment (SQA) module; and 3) signal-quality aware (SQAw) ECG analysis and transmission module. The main objectives of this paper are: design and development of a light-weight ECG SQA method for automatically classifying the acquired ECG signal into acceptable or unacceptable class and real-time implementation of proposed IoT-enabled ECG monitoring framework using ECG sensors, Arduino, Android phone, Bluetooth, and cloud server. The proposed framework is tested and validated using the ECG signals taken from the MIT-BIH arrhythmia and Physionet challenge databases and the real-time recorded ECG signals under different physical activities. Experimental results show that the proposed SQA method achieves promising results in identifying the unacceptable quality of ECG signals and outperforms existing methods based on the morphological and RR interval features and machine learning approaches. This paper further shows that the transmission of acceptable quality of ECG signals can significantly improve the battery lifetime of IoT-enabled devices. The proposed quality-aware IoT paradigm has great potential for assessing clinical acceptability of ECG signals in improvement of accuracy and reliability of unsupervised diagnosis system.
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Cites
An IoT-Aware Architecture for Smart Healthcare Systems
Catarinucci, L., de Donno, D., Mainetti, L., Palano, L., Patrono, L., Stefanizzi, M. L., & Tarricone, L. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2417684 · Cited by 1,167
Multicolumn bidirectional long short-term memory for mobile devices-based human activity recognition
Tao, D., Wen, Y., & Hong, R. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2561962 · Cited by 83
Cited by
A Survey on Internet of Things and Cloud Computing for Healthcare
Dang, L. M., Piran, M. J., Han, D., Min, K., & Moon, H. (2019) · Electronics · doi:10.3390/electronics8070768 · Cited by 640
ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges
Serhani, M. A., T. El Kassabi, H., Ismail, H., & Nujum Navaz, A. (2020) · Sensors · doi:10.3390/s20061796 · Cited by 357
Popli, S., Jha, R. K., & Jain, S. (2019) · IEEE Access · doi:10.1109/access.2018.2881533 · Cited by 325
Trends in IoT based solutions for health care: Moving AI to the edge
Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.05.016 · Cited by 318
A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment
Satija, U., Ramkumar, B., & Manikandan, M. S. (2018) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2018.2810957 · Cited by 295
Self-citation
Metadata
- Authors
- Satija, U., Ramkumar, B., & Sabarimalai Manikandan, M.
- Venue
- IEEE Internet of Things Journal
- Published
- 2017-06
- Volume
- 4
- Issue
- 3
- Pages
- 815-823
- ISSN
- 2327-4662 (ISSN-L)
- OpenAlex
- W2587967768
- Cited by
- 370
- References
- 2
- FWCI
- 23.74
- Type
- journal article
- Language
- en
- Open access
- closed
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