ABSTRACT
The modern sensors can be used to create non-invasive, interconnected, adaptive, intelligent, dynamic systems that capture and analyze the physiological data and generate signals when life-saving action is required. The aim of this paper is to present and evaluate an information system for long term monitoring of physiological data registered through modern body sensors. The presented hardware and software system is based on the use of four photoplethysmographic sensors and one electrocardiographic sensor placed at different places on the human body. The photoplethysmographic sensors are placed two on the left side of the body and two on the right side, which allows studying the influence of cardiac activity on both halves of the body. The advantages of physiological monitoring realized with a system of five independent, simultaneously recorded signals from different parts of the human body are shown. The recorded signals are preprocessing in a modern technological microcontroller environment. The software part of the information system is implemented with the help of the cloud computing, through which technologies achieve efficient operation and interaction between the used sensors and systems for health prevention and timely signaling in situations of risk. The use of an integrated sensor-based system enables the refinement of physiological information submitted to the software system for data analysis and generation of effective action solutions.
- E. Gospodinova, M. Gospodinov, N. Dey. I. Domuschiev, A. S. Ashou, S. V. Balas, T. Olariu (2018). Specialized Software System for Heart Rate Variability Analysis: An Implementation of Nonlinear Graphical Methods. In: Balas V., Jain L., Balas M. (eds). Soft Computing Applications. SOFA 2016. Advances in Intelligent Systems and Computing, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-319-62521-8_31Google ScholarCross Ref
- A. Choudhury, S. Samanta, N. Dey, A.S. Ashour, D. Bălas-Timar, M. Gospodinov, E. Gospodinova (2015). Microscopic Image Segmentation Using Quantum Inspired Evolutionary Algorithm. Journal of Advanced Microscopy Research, Volume 10, Number 3, pp. 164--173. https://doi.org/10.1166/jamr.2015.1257Google ScholarCross Ref
- Christov I., I. Jekova, V. Krasteva, I. Dotsinsky, T. Stoyanov (2009). Rhythm Analysis by Heartbeat Classification in the Electrocardiogram, International Journal Bioautomation, 13(2), 84--96.Google Scholar
- Jekova I., V. Tsibulko, I. Iliev (2014). ECG Database Applicable for Development and Testing of Pace Detection Algorithms, International Journal Bioautomation, 18(4), 377--388.Google Scholar
- Krasteva V., Jekova I., and Ramun Schmid. (2019) Krasteva, Vessela et al. "Simulating Arbitrary Electrode Reversals in Standard 12-lead ECG." Sensors (Basel, Switzerland) vol. 19 (13): 2920., doi:10.3390/s19132920.Google ScholarCross Ref
- H. Ouyang, J. Tian, G. Sun, Y. Zou, Z. Liu, H. Li, L. Zhao, B. Shi, Y. Fan, Y. Fan, and Z. L. Wang, "Self-Powered Pulse Sensor for Antidiastole of Cardiovascular Disease," Adv. Mater., 29(40), 1--10, 2017. DOI: 10.1002/adma.201703456Google Scholar
- S. Kale, S. Mane, and P. Patil, "IOT based wearable biomedical monitoring system," Proceedings of the International Conference on Trends in Electronics and Informatics, 2017, pp. 971--976. DOI:10.1109/icoei.2017.8300852Google Scholar
- M. Taştan. IoT Based Wearable Smart Health Monitoring System. Celal Bayar University Journal of Science Volume 14, Issue 3, 2018, p 343--350. DOI: 10.18466/cbayarfbe.451076Google Scholar
- Francesco Rundo, Sabrina Conoci, Alessandro Ortis, and Sebastiano Battiato, An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment, Sensors 2018, 18(2), 405; https://doi.org/10.3390/s18020405.Google Scholar
- M. Ghamari; C. Soltanpur; S. Cabrera; R Romero; R Martinek; H. Nazera. Design and prototyping of a wristband-type wireless photoplethysmographic device for heart rate variability signal analysis. Published in: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Date of Conference: 16-20 Aug. 2016. DOI: 10.1109/EMBC.2016.7591842Google Scholar
- M. Bentham, G. Stansby, and J. Allen. Innovative Multi-Site Photoplethysmography Analysis for Quantifying Pulse Amplitude and Timing Variability Characteristics in Peripheral Arterial Disease. Diseases, 2018, 6(3); 81. DOI: 10.3390/diseases6030081Google Scholar
- Fujita D., Suzuki A., Ryu K., PPG-based Systolic Blood Pressure Estimation Method using PLS and Level-crossing Feature. Applied Sciences. 2019, 9, 304, DOI: 10.3390/app9020304.Google Scholar
- G. Acampora, D. Cook, P. Rashidi, A. Vasilakos. A Survey on Ambient Intelligence in Health Care. Proceeding of the IEEE, December 2013, DOI: 10.1109/JPROC.2013.2262913Google Scholar
- Fan, Q.; Li, K., 2018. Non-contact remote estimation of cardiovascular parameters. Biomed. Signal Process. Control, 40, 192--203. DOI: 10.1016/j.bspc.2017.09.022Google ScholarCross Ref
- J. L. Moraes, M. X. Rocha, G. G. Vasconcelos, J. E. V. Filho, V. de Albuquerque, and A. R. Alexandria, Advances in Photoplethysmography Signal Analysis for Biomedical Applications, Sensors, s 2018, 18, 1894, DOI:10.3390/s18061894Google Scholar
- Sun, Y.; Thakor, N. Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging. IEEE Trans. Biomed. Eng. 2016, 63, 463--477. DOI: 10.1109/TBME.2015.2476337.Google Scholar
- Georgieva-Tsaneva G. Wavelet Based Interval Varying Algorithm for Optimal Non-Stationary Signal Denoising. Proceedings of the 20th International Conference on Computer Systems and Technologies, June 2019, Pages 200--206. DOI: https://doi.org/10.1145/3345252.3345268.Google ScholarDigital Library
- Gospodinov M., Cheshmedziev K., Three-Sensor Portable Information System for Physiological Data Registration. Proceedings of the 20th International Conference on Computer Systems and Technologies, June 2019 Pages 36--41, DOI: https://doi.org/10.1145/3345252.3345281Google Scholar
- Body Sensors System for Physiological Data Long-term Monitoring
Recommendations
Three-Sensor Portable Information System for Physiological Data Registration
CompSysTech '19: Proceedings of the 20th International Conference on Computer Systems and TechnologiesThe current years are characterized by the development of a wide variety of portable and wearable devices and systems for personal monitoring of the functioning of the cardiovascular system. The first generation of such devices for recording and ...
Evaluation of T-wave alternans in comparison with ECG stress test and scintigraphic examination in patients with coronary artery disease
BIBE '12: Proceedings of the 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)The results of study on T-wave alternans in the group of patients with ischemic heart disease are shown. The body surface potential maps were recorded with use of 67 channel high-resolution ECG system. Electrocardiographic stress test was performed ...
A Novel Handheld Device for Use in Remote Patient Monitoring of Heart Failure Patients--Design and Preliminary Validation on Healthy Subjects
Remote patient monitoring (RPM) holds great promise for reducing the burden of congestive heart failure (CHF). Improved sensor technology and effective predictive algorithms can anticipate sudden decompensation events. Enhanced telemonitoring systems ...
Comments