Glucose Data Classification for Diabetic Patient Monitoring

[EN] Living longer and healthier is the wish of all patients. Therefore, to design effective solutions for this objective, the concept of Big Data in the health field can be integrated. Our work proposes a patient monitoring system based on Internet of Things (IoT) and a diagnostic prediction tool for diabetic patients. This system provides real-time blood glucose readings and information on blood glucose levels. It monitors blood glucose levels at regular intervals. The proposed system aims to prevent high blood sugar and significant glucose fluctuations. The system provides a precise result. The collected and stored data will be classified by using several classification algorithms to predict glucose levels in diabetic patients. The main advantage of this system is that the blood glucose level is reported instantly; it can be lowered or increased.This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P.Rghioui, A.; Lloret, J.; Parra-Boronat, L.; Sendra, S.; Oumnad, A. (2019). Glucose Data Classification for Diabetic Patient Monitoring. Applied Sciences. 9(20):1-15. https://doi.org/10.3390/app9204459S115920Rghioui, A., Sendra, S., Lloret, J., & Oumnad, A. (2016). Internet of Things for Measuring Human Activities in Ambient Assisted Living and e-Health. Network Protocols and Algorithms, 8(3), 15. doi:10.5296/npa.v8i3.10146Zhang, Y., Gravina, R., Lu, H., Villari, M., & Fortino, G. (2018). PEA: Parallel electrocardiogram-based authentication for smart healthcare systems. Journal of Network and Computer Applications, 117, 10-16. doi:10.1016/j.jnca.2018.05.007Ismail, W. N., Hassan, M. M., Alsalamah, H. A., & Fortino, G. (2018). Mining productive-periodic frequent patterns in tele-health systems. Journal of Network and Computer Applications, 115, 33-47. doi:10.1016/j.jnca.2018.04.014Aboufadel, E., Castellano, R., & Olson, D. (2011). Quantification of the Variability of Continuous Glucose Monitoring Data. Algorithms, 4(1), 16-27. doi:10.3390/a4010016Katon, W. J., Rutter, C., Simon, G., Lin, E. H. B., Ludman, E., Ciechanowski, P., … Von Korff, M. (2005). The Association of Comorbid Depression With Mortality in Patients With Type 2 Diabetes. Diabetes Care, 28(11), 2668-2672. doi:10.2337/diacare.28.11.2668Riazul Islam, S. M., Daehan Kwak, Humaun Kabir, M., Hossain, M., & Kyung-Sup Kwak. (2015). The Internet of Things for Health Care: A Comprehensive Survey. IEEE Access, 3, 678-708. doi:10.1109/access.2015.2437951Lloret, J., Canovas, A., Sendra, S., & Parra, L. (2015). A smart communication architecture for ambient assisted living. IEEE Communications Magazine, 53(1), 26-33. doi:10.1109/mcom.2015.7010512Xiao, Z., Tan, X., Chen, X., Chen, S., Zhang, Z., Zhang, H., … Min, H. (2015). An Implantable RFID Sensor Tag toward Continuous Glucose Monitoring. IEEE Journal of Biomedical and Health Informatics, 1-1. doi:10.1109/jbhi.2015.2415836Wang, H.-C., & Lee, A.-R. (2015). Recent developments in blood glucose sensors. Journal of Food and Drug Analysis, 23(2), 191-200. doi:10.1016/j.jfda.2014.12.001Ahmed, H. B., & Serener, A. (2016). Effects of External Factors in CGM Sensor Glucose Concentration Prediction. Procedia Computer Science, 102, 623-629. doi:10.1016/j.procs.2016.09.452Siddiqui, S. A., Zhang, Y., Lloret, J., Song, H., & Obradovic, Z. (2018). Pain-Free Blood Glucose Monitoring Using Wearable Sensors: Recent Advancements and Future Prospects. IEEE Reviews in Biomedical Engineering, 11, 21-35. doi:10.1109/rbme.2018.2822301Fortino, G., Parisi, D., Pirrone, V., & Di Fatta, G. (2014). BodyCloud: A SaaS approach for community Body Sensor Networks. Future Generation Computer Systems, 35, 62-79. doi:10.1016/j.future.2013.12.015Kanchan, B. D., & Kishor, M. M. (2016). Study of machine learning algorithms for special disease prediction using principal of component analysis. 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). doi:10.1109/icgtspicc.2016.7955260Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software. ACM SIGKDD Explorations Newsletter, 11(1), 10-18. doi:10.1145/1656274.1656278Huda, S., Yearwood, J., Jelinek, H. F., Hassan, M. M., Fortino, G., & Buckland, M. (2016). A Hybrid Feature Selection With Ensemble Classification for Imbalanced Healthcare Data: A Case Study for Brain Tumor Diagnosis. IEEE Access, 4, 9145-9154. doi:10.1109/access.2016.264723

Description and Experience of the Clinical Testbeds

This deliverable describes the up-to-date technical environment at three clinical testbed demonstrator sites of the 6WINIT Project, including the adapted clinical applications, project components and network transition technologies in use at these sites after 18 months of the Project. It also provides an interim description of early experiences with deployment and usage of these applications, components and technologies, and their clinical service impact

A health decision support system for rural india

In India and other developing nations, the bulk of the morbidity & mortality is due to commonly occurring communicable diseases & parasitic diseases coupled with malnutrition especially in rural areas. The effective decision making at the top level of any health services mostly depends on the availability of various resources such as human expertise, equipments, and medicine. People die from infectious and/or chronic diseases are the leading causes of death, especially in rural areas. By analyzing mortality, morbidity, and behavioral data, one can attempt to quantify health problems and the behavioral risk factors that contribute to them Hence in a country like India an effective multi disease surveillance system is essential for the General Health Care System to detect an outbreak, monitor the trend, prevent an epidemic & decrease the morbidity & mortality rate of India The proposed DSS is targeting to assist the top management of the State health service which will provide a practical, relatively inexpensive and replicable model of disease surveillance. The proposed system consists of application and management software that support clinical and operational data. The software is designed for multi-site use in individual medical facilities and health workers in remote villages. The disease surveillance data is collected and updated periodically by the health workers to the central database through SMS. This disease surveillance system through SMS will provide real time data and extract the statistical and customized information and even facilitate the prediction of the outbreak of epidemics and report emergencies. It also provides an automatic response messaging through SMS to people regarding basic preventive measures and cures. A smart phone application is built using J2ME which make data transmission error free and secured. The use of SMS as the mode of data transmission will help reduce bureaucratic delays and will automate the task of disease surveillance by providing an inexpensive replacement to the existing trend

Development and Evaluation of a Python Telecare System Based on a Bluetooth Body Area Network

This paper presents a prototype of a telemonitoring system, based on a BAN (Body Area Network) that is integrated by a Bluetooth (BT) pulse oximeter, a GPS (Global Positioning System) unit, and a smartphone. The smartphone is the hardware platformfor running a Python software thatmanages the Bluetooth piconet formed by the sensors. Thus the smartphone forwards the data received from the Bluetooth devices, encoded into JSON (JavaScript Object Notation), to a central server. This server provides universal access to the information of the patient’s location and health status through a web application based on AJAX (Asynchronous JavaScript and XML) technology. Additionally, for the described prototype, the study presents some performance analyses about several topics that are of great interest for the applicability of the prototype: (i) the technique used to forward the patient’s location and health status, (ii) the power consumption of the smartphone (which is compared with the measurements of an equivalent software developed for Java Micro Edition platform), and (iii) the web browser compatibility of the web application developed for the control and monitoring of the patients.Ministerio de Educación y Ciencia TEC2009-13763-C02-0

REAL-TIME MULTI-PATIENT MONITORING SYSTEM USING ARM AND WIRELESS SENSOR NETWORK

Mobile Multi patient monitoring device has become increasingly important in Hospital wards to record real-time data during normal activity for better treatment. However, the current quality and reliability have not been satisfactory due to the size, weight, distance of coverage and also high power consumption. This paper provides a solution for enhancing the reliability, flexibility by improving the performance and power management of the real-time multi-patient monitoring system (MPMS). In the current proposed system the patient health is continuously monitored by the MPMS and the acquired data is transmitted to a centralized ARM server using Wireless Sensor Networks. A ZigBee node is connected to every patient monitor system which will send the patient\u27s vital information .Upon system boot up, the mobile patient monitor system will continuously monitor the patients vital parameters like Heart Beat, body temperature etc and will periodically send those parameters to a centralized server using ZigBee node configured as co-coordinator. If a particular patient’s health parameter falls below the threshold value, a buzzer alert is triggered by the ARM server. Along with a buzzer an automated SMS is posted to the pre-configured Doctors mobile number using a standard GSM module interfaced to the ARM server. The Doctor is continuously connected to the ARM server using GSM Module and he/she can get a record of a particular patient’s information by just posting a SMS message to the centralized ARM server. This will reduce treatment time, cost and power consumption to a greater extent. At the same time, the efficiency of examining ward will be improved by making the system more real-time and robust

Remote monitoring and follow-up of pacemakers and implantable cardioverter defibrillators

In the era of communication technology, new options are now available for following-up patients implanted with pacemakers (PMs) and defibrillators (ICDs). Most major companies offer devices with wireless capabilities that communicate automatically with home transmitters, which then relay data to the physician, thereby allowing remote patient follow-up and monitoring. These systems are being widely used in the USA for remote follow-up, and have been more recently introduced in Europe, where their adoption is increasing. In this article, we describe the currently existing systems, review the available evidence in the literature regarding remote follow-up and monitoring of PMs and ICDs, and finally discuss some unresolved issues

Economic impact of remote monitoring on ordinary follow-up of implantable cardioverter defibrillators as compared with conventional in-hospital visits: a single-center prospective and randomized study

Few data are available on actual follow-up costs of remote monitoring (RM) of implantable defibrillators (ICD). Our study aimed at assessing current direct costs of 1-year ICD follow-up based on RM compared with conventional quarterly in-hospital follow-ups. Methods and results Patients (N=233) with indications for ICD were consecutively recruited and randomized at implant to be followed up for 1 year with standard quarterly inhospital visits or by RM with one in-hospital visit at 12 months, unless additional in-hospital visits were required due to specific patient conditions or RM alarms. Costs were calculated distinguishing between provider and patient costs, excluding RM device and service cost. The frequency of scheduled in-hospital visits was lower in the RM group than in the control arm. Follow-up required 47 min per patient/year in the RM arm versus 86 min in the control arm (p=0.03) for involved physicians, generating cost estimates for the provider of USD 45 and USD 83 per patient/- year, respectively. Costs for nurses were comparable. Overall, the costs associated with RM and standard follow-up were USD 103±27 and 154±21 per patient/year, respectively (p=0.01). RM was cost-saving for the patients: USD 97±121 per patient/year in the RM group versus 287± 160 per patient/year (p=0.0001). Conclusion The time spent by the hospital staff was significantly reduced in the RM group. If the costs for the device and service are not charged to patients or the provider, patients could save about USD 190 per patient/year while the hospital could save USD 51 per patient/year

IoT-Based Solution for Paraplegic Sufferer to Send Signals to Physician via Internet

We come across hospitals and non-profit organizations that care for people with paralysis who have experienced all or portion of their physique being incapacitated by the paralyzing attack. Due to a lack of motor coordination by their mind, these persons are typically unable to communicate their requirements because they can speak clearly or use sign language. In such a case, we suggest a system that enables a disabled person to move any area of his body capable of moving to broadcast a text on the LCD. This method also addresses the circumstance in which the patient cannot be attended to in person and instead sends an SMS message using GSM. By detecting the user part's tilt direction, our suggested system operates. As a result, patients can communicate with physicians, therapists, or their loved ones at home or work over the web. Case-specific data, such as heart rate, must be continuously reported in health centers. The suggested method tracks the body of the case's pulse rate and other comparable data. For instance, photoplethysmography is used to assess heart rate. The decoded periodic data is transmitted continually via a Microcontroller coupled to a transmitting module. The croaker's cabin contains a receiver device that obtains and deciphers data as well as constantly exhibits it on Graphical interfaces viewable on the laptop. As a result, the croaker can monitor and handle multiple situations at once