A Narrowband IoT Personal Sensor for Long-Term Heart Rate Monitoring and Atrial Fibrillation Detection
Abstract
:1. Introduction
2. Materials and Methods
2.1. Personal Hardware Device
2.1.1. The MAX30003 Board
2.1.2. The NUCLEOF401RE Board
2.1.3. The STEVAL-STMODLTE Board
2.2. Cloud Platform
ThingSpeak IoT Service
2.3. Algorithms for Detecting AF
Lorenz Algorithm
2.4. AF ECG Dataset
2.5. Patient Simulator
2.6. Statistical Analysis
3. Results
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviation
Acronym | Full Form |
AF | Atrial Fibrillation |
AT | Atrial Tachycardia |
DAC | Digital to Analog Converter |
DMA | Direct Memory Access |
ECG | Electrocardiography |
ELRs | External Loop Recorders |
FN | False Negative |
FP | False Positive |
ILRs | Implantable Loop Recorders |
IoT | Internet of Things |
LPWAN | Low-Power Wide-Area Network |
LTE | Long-Term Evolution |
mHealth | mobile Health devices |
mIoT | Internet of Things in the medical field |
NB-HIoT | Health monitoring based on NB-IoT |
NB-IoT | Narrowband Internet of Things |
NSR | Normal Sinus Rhythm |
POUF | Protocols, Operations, Usage, and Formats |
PPG | Photoplethysmography |
PUF | Physical Unclonable Function |
SW | Software |
TN | True Negative |
TP | True Positive |
References
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Model | Manufacturer | Board Size |
---|---|---|
Max30003Wing | Analog Devices | 50.8 × 23.6 mm |
NucleoF401RE | STMicroelectronics | 70.0 × 82.50 mm |
STEVAL-STMODLTE | STMicroelectronics | 78.0 × 40.0 mm |
MAX30003 Settings | |
FMSTR clocks | 32 kHz |
Current magnitude | 10 nA |
ECG Settings | |
Digital LPF cutoff | 40 Hz |
Digital HPF cutoff | 0.5 Hz |
ECG gain | 160 V/V |
Sample Rate | 250 sps |
R-to-R Settings | |
WNDW | 96 ms |
Model | Manufacturer | Board Size |
---|---|---|
MCP4724 | Microchip Technology | 15.0 × 15.0 mm |
NucleoF401RE | STMicroelectronics | 70.0 × 82.50 mm |
SD card | Gigastone | 15.0 × 11.0 mm |
Patient | Accuracy | Sensitivity | Specificity |
---|---|---|---|
04015 | 0.970 | 0.333 | 0.977 |
04043 | 0.816 | 0.141 | 1.00 |
04048 | 0.993 | 0.00 | 1.00 |
04126 | 0.951 | 0.00 | 0.993 |
04746 | 0.967 | 0.939 | 1.00 |
04908 | 0.935 | 0.269 | 0.996 |
04936 | 0.766 | 0.692 | 1.00 |
05091 | 1.00 | // | 1.00 |
05121 | 0.836 | 0.763 | 0.980 |
05261 | 1.00 | 1.00 | 1.00 |
06426 | 0.951 | 0.953 | 0.875 |
06453 | 0.993 | 0.500 | 0.996 |
06995 | 0.911 | 0.853 | 0.963 |
07162 | 1.00 | 1.00 | // |
07879 | 0.669 | 0.465 | 1.00 |
07910 | 0.997 | 0.979 | 1.00 |
08215 | 0.997 | 0.996 | 1.00 |
08219 | 0.823 | 0.333 | 0.995 |
08378 | 0.902 | 0.578 | 0.988 |
08405 | 0.905 | 0.868 | 1.00 |
08434 | 0.957 | 0.308 | 0.986 |
08455 | 0.832 | 0.754 | 1.00 |
Study | Type of Connection | Sensor | Signal Analyzed | AF Detection Platform | AF Detection Method | Evaluation Metrics |
---|---|---|---|---|---|---|
Present study | LTE | ECG sensor | Inter-beat intervals | Cloud | Lorenz algorithm | Accuracy, sensitivity, specificity |
[38] | Alarm notification to remote server | ECG sensor | ECG signal | On board | Algorithm based on RR irregularity and P-wave absence from ECG data | Sensitivity, specificity |
[39] | Bluetooth | ECG sensor (AD8232) | ECG signal | Cloud | Deep learning algorithm | Accuracy, sensitivity, specificity |
[40] | No | No | Heart rate signal | No | Deep learning algorithm | Accuracy |
[41] | Bluetooth connection to smartphone | ECG patch | ECG signal | Cloud | Machine learning algorithm | Accuracy, sensitivity, specificity |
[42] | Bluetooth connection to smartphone | Polar H10 ECG sensor | ECG signal | Cloud | No AF detection; ventricular arrhythmia detection via machine learning | Accuracy |
[43] | No | PPG sensor | PPG signal | On board | Analytical algorithm to detect AF burden | Accuracy |
[44] | Wi-Fi | Pulse sensor | Heart rate signal | Cloud | Algorithm developed by authors | – |
SUPPORT | OPPOSE | |
---|---|---|
Strengths | Weaknesses | |
INTERNAL |
|
|
Opportunities | Threats | |
EXTERNAL |
|
|
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Share and Cite
Cinotti, E.; Centracchio, J.; Parlato, S.; Andreozzi, E.; Esposito, D.; Muto, V.; Bifulco, P.; Riccio, M. A Narrowband IoT Personal Sensor for Long-Term Heart Rate Monitoring and Atrial Fibrillation Detection. Sensors 2024, 24, 4432. https://doi.org/10.3390/s24144432
Cinotti E, Centracchio J, Parlato S, Andreozzi E, Esposito D, Muto V, Bifulco P, Riccio M. A Narrowband IoT Personal Sensor for Long-Term Heart Rate Monitoring and Atrial Fibrillation Detection. Sensors. 2024; 24(14):4432. https://doi.org/10.3390/s24144432
Chicago/Turabian StyleCinotti, Eliana, Jessica Centracchio, Salvatore Parlato, Emilio Andreozzi, Daniele Esposito, Vincenzo Muto, Paolo Bifulco, and Michele Riccio. 2024. "A Narrowband IoT Personal Sensor for Long-Term Heart Rate Monitoring and Atrial Fibrillation Detection" Sensors 24, no. 14: 4432. https://doi.org/10.3390/s24144432
APA StyleCinotti, E., Centracchio, J., Parlato, S., Andreozzi, E., Esposito, D., Muto, V., Bifulco, P., & Riccio, M. (2024). A Narrowband IoT Personal Sensor for Long-Term Heart Rate Monitoring and Atrial Fibrillation Detection. Sensors, 24(14), 4432. https://doi.org/10.3390/s24144432