Mobile Notification System for Blood Pressure and Heartbeat Anomaly Detection
DOI:
https://doi.org/10.13052/jwe1540-9589.19568Keywords:
Anomaly Detection, Pan-Tompkins algorithm, Firebase Cloud Messaging service, Diastolic Blood Pressure, Systolic Blood Pressure, ECGAbstract
In today’s fast-paced world where patients may need to be remotely monitored while they are away or out of hospitals, there is a need for mobile applications that can gather the biometric and biomedical signals from any number of devices and sensors, collecting biometric or biomedical data from a patient. This work presents the improvement of a circulatory strain and heartbeat anomaly detection and notice apparatus as an Android application that permits quick discovery of any variations from the norm in a patient's fundamental dependent on the Pan-Tompkins algorithm and reports it to the pertinent emergency clinic or clinical staff. The blood ECG information can be gotten from the health tracker sensors by means of a Bluetooth association and the patient can enter their Blood pressure esteems. In this case, the information gathered from a set of reproduced data, which is identified with a triggering notice from Firebase Cloud function. This notification is further acknowledged by the enrolled specialist (or any clinical faculty or health sector laborer having a similar application). The system's security part is represented by the fine-grained consent procedure which directs that solitary significant authorizations are required for the best possible working of the application which ought to be given to the application. An encryption method using the Blowfish algorithm is included as a feature of the developed mobile Android application to provide secure data transfer of the patient’s vital signals.
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A. Secerbegovic, A. Mujčić, N. Suljanović, M. Nurkic, and J. Tasic, “The research mHealth platform for ECG monitoring,” Proceedings of the 11th International Conference on Telecommunications, Graz, 2011, pp. 103–108.
P. Kakria, N. Tripathi and P. Kitipawang, “A Real-Time Health Monitoring System for Remote Cardiac Patients Using Smartphone and Wearable Sensors”, International Journal of Telemedicine and Applications, vol. 2015, pp. 1–11, 2015.
M. Sarkaleh, “Classification Of Ecg Arrhythmias Using Discrete Wavelet Transform and Neural Networks”, International Journal of Computer Science, Engineering and Applications, vol. 2, no. 1, pp. 1–13, 2012.
“Number of mobile phone users worldwide 2015-2020 | Statista”, Statista, 2018. [Online]. Available: https://www.statista.com/statistics/274774/forecast-of-mobile-phone-users-worldwide/.
G. Friesen, T. Jannett, M. Jadallah, S. Yates, S. Quint,and H. Nagle, “A comparison of the noise sensitivity of nine QRS detection algorithms”, IEEE Transactions on Biomedical Engineering, vol. 37, no. 1, pp. 85–98, 1990.
Pan and W. Tompkins, “A Real-Time QRS Detection Algorithm”, IEEE Transactions on Biomedical Engineering, vol. -32, no. 3, pp. 230–236, 1985.
Enck, W., Gilbert, P., Han, S., Tendulkar, V., Chun, B., Cox, L., Jung, J., McDaniel, P. and Sheth, A. (2014). “TaintDroid”. ACM Transactions on Computer Systems, 32(2), pp. 1–29.
J. Jeon, K. Micinski, J. Vaughan, A. Fogel, N. Reddy, J. Foster,and T. Millstein .“Dr. Android and Mr. Hide: Fine-grained Permissions in Android Applications”, SPSM’12 Proceedings of the second ACM workshop on Security and privacy in smartphones and mobile devices, pp. 3–14, 2012.
J. Fu, Y. Zhou, H. Liu, Y. Kang and X. Wang, “Perman: Fine-Grained Permission Management for Android Applications,” 2017 IEEE 28th International Symposium on Software Reliability Engineering (ISSRE), Toulouse, 2017, pp. 250–259.
Z. Fang, W. Han, and Y. Li, “Permission based Android security: Issues and countermeasures”, Computers & Security, vol. 43, pp. 205–218, 2014.
M. K. Hasan, N. Ahmed and A. H. M. S. Islam, “Android mobile application: Remote monitoring of blood pressure,” 2012 15th International Conference on Computer and Information Technology (ICCIT), Chittagong, 2012, pp. 215–220.
D. Lou, X. Chen, Z. Zhao, Y. Xuan, Z. Xu, H. Jin, X. Guo,and Z. Fang, “A Wireless Health Monitoring System based on Android Operating System”, IERI Procedia, vol. 4, pp. 208–215, 2013.
Singh, G., Kr. Singla, A. and S. Sandha, K. (2012). A Study of New Trends in Blowfish Algorithm. International Journal of Engineering Research and Applications (IJERA), 1(2), pp. 321–326.
B. Rashidi, C. Fung,and T. Vu, “Android fine-grained permission control system with real-time expert recommendations”, Pervasive and Mobile Computing, vol. 32, pp. 62–77, 2016.
D. Geneiatakis, I. Fovino, I. Kounelis,and P. Stirparo, “A Permission verification approach for android mobile applications”, Computers & Security, vol. 49, pp. 192–205, 2015.
D. Hee, A. Rabbi, J. Choi,and R. Fazel-Rezai, “Development of a Mobile Phone Based e-Health Monitoring Application”, International Journal of Advanced Computer Science and Applications, vol. 3, no. 3, 2012.
A. BOUROUIS, M. FEHAM,and A. BOUCHACHIA, “Ubiquitous Mobile Health Monitoring System for Elderly (UMHMSE)”, International Journal of Computer Science and Information Technology, vol. 3, no. 3, pp. 74–82, 2011.
A. Triantafyllidis, V. Koutkias, I. Chouvarda,and N. Maglaveras, “A Pervasive Health System Integrating Patient Monitoring, Status Logging, and Social Sharing”, IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 1, pp. 30–37, 2013.
A. Abdullah, A. Ismael, A. Rashid, A. Abou-Elnour and M. Tarique, “Real Time Wireless Health Monitoring Application Using Mobile Devices”, International Journal of Computer Networks & Communications, vol. 7, no. 3, pp. 13–30, 2015.
L. Clifton, D. Clifton, M. Pimentel, P. Watkinson,and L. Tarassenko, “Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors”, IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 3, pp. 722–730, 2014.
R. Treskes, E. van der Velde, R. Barendse and N. Bruining, “Mobile health in cardiology: a review of currently available medical apps and equipment for remote monitoring”, Expert Review of Medical Devices, vol. 13, no. 9, pp. 823–830, 2016.
People.eecs.berkeley.edu, 2018. Available: https://people.eecs.berkeley.edu/~daw/papers/perms-webapps11.pdf. [Accessed: 05-Nov-2018].
L. Wu, X. Du and H. Zhang, “An effective access control scheme for preventing permission leak in Android,” 2015 International Conference on Computing, Networking and Communications (ICNC), Garden Grove, CA, pp. 57–61, 2015.
W. Ahmad, C. Kästner, J. Sunshine and J. Aldrich, “Inter-app Communication in Android: Developer Challenges,” 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR), Austin, TX, pp. 177–188, 2016.
K. AlSharqi, A. Abdelbari, A. Elnour,and M. Tarique, “Zigbee Based Wearable Remote Healthcare Monitoring System for Elderly Patients”, International Journal of Wireless & Mobile Networks, vol. 6, no. 3, pp. 53–67, 2014.
F. Wu, H. Zhao, Y. Zhao and H. Zhong, “Development of a Wearable-Sensor-Based Fall Detection System”, International Journal of Telemedicine and Applications, vol. 2015, pp. 1–11, 2015.
A. Nadeem and M. Y. Javed, “A Performance Comparison of Data Encryption Algorithms,” 2005 International Conference on Information and Communication Technologies, Karachi, Pakistan, pp. 84–89, 2005.
R. Bhanot and R. Hans, “A Review and Comparative Analysis of Various Encryption Algorithms”, International Journal of Security and Its Applications, vol. 9, no. 4, pp. 289–306, 2015.
G. Singh and S. Supriya, “A Study of Encryption Algorithms (RSA, DES, 3DES,and AES) for Information Security”, International Journal of Computer Applications, vol. 67, no. 19, pp. 33–38, 2013.
S. Omer AL Faroog Mohammed Koko and D. Babiker A/Nabi Mustafa, “Comparison of Various Encryption Algorithms and Techniques for improving secured data Communication”, pp 62–69, 2015.
P. Kasturia and K. Maheshwar, ”Critical Analysis of Various Cryptographic Algorithms”, Journal Of Ultra Computer & Information Technology, Feb. 2017.
J.X. Sun, A.T. Reisner, R.G. Mark,”A signal abnormality index for arterial blood pressure waveforms”, ComputCardiol, pp. 13–16, 2006.
R. Treskes, E. van der Velde, R. Barendse and N. Bruining, “Mobile health in cardiology: a review of currently available medical apps and equipment for remote monitoring”, Expert Review of Medical Devices, vol. 13, no. 9, pp. 823–830, 2016.
P. Tirumala Rao, S. Koteswarao Rao, G. Manikanta and S. Ravi Kumar, “Distinguishing Normal and Abnormal ECG Signal”, Indian Journal of Science and Technology, vol. 9, no. 10, 2016.
R. Álvarez, A. Penín and X. Sobrino, “A Comparison of Three QRS Detection Algorithms Over a Public Database”, Procedia Technology, vol. 9, pp. 1159–1165, 2013.
S. Saraswat, G. Srivastava,and S. Shukla, “Review: Comparison of QRS detection algorithms,” International Conference on Computing, Communication & Automation, Noida, pp. 354–359, 2015.
Almootassem, O., Husain, S., Parthipan, D. and Mahmoud, Q. (2017). A Cloud-based Service for Real-Time Performance Evaluation of NoSQL Databases.
Jason W. Cornwell,“Blowfish Survey”, Department of Computer Science Columbus State University.
Valmik, M. and Kshirsagar, P. (2014). Blowfish Algorithm. IOSR Journal of Computer Engineering, 16(2), pp. 80–83.
Khawas, C. and Shah, P. (2018). Application of Firebase in Android App Development-A Study. International Journal of Computer Applications, 179(46), pp. 49–53.
Overview of Backend as a Service platform –
P. K. Gakare, A. M. Patel, J. R. Vaghela and R. N. Awale, “Real time feature extraction of ECG signal on android platform,” 2012 International Conference on Communication, Information & Computing Technology (ICCICT), Mumbai, 2012, pp. 1–5. doi: 10.1109/ICCICT.2012.6398230
Manku, S. and Vasanth, K. (2015). Blowfish Encryption Algorithm for Information Security. ARPN Journal of Engineering and Applied Sciences, 10(10), pp. 4717–4719.
StefanGradl, Patrick Kugler, Clemens Lohmüller, and BjoernEskofier, Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices, in 34th Annual International Conference of the IEEE EMBS, 2012, pp. 2452–2455.
R. Fensli, E. Gunnarson and T. Gundersen, “A wearable ECG-recording system for continuous arrhythmia monitoring in a wireless tele-home-care situation,” 18th IEEE Symposium on Computer-Based Medical Systems (CBMS’05), Dublin, 2005, pp. 407–412. doi: 10.1109/CBMS.2005.22
N. Vuong, T. Nguyen, L. D. Tran, and T. Van Huynh, “Detect QRS complex in ECG,” 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Siem Reap, 2017, pp. 2022–2027. doi: 10.1109/ICIEA.2017.8283170
Tirumala Rao, P., Koteswarao Rao, S., Manikanta, G. and Ravi Kumar, S. (2016). Distinguishing Normal and Abnormal ECG Signal. Indian Journal of Science and Technology, 9(10).
Gaurav, D., Tiwari, S. M., Goyal, A., Gandhi, N., & Abraham, A. (2019a). Machine intelligence-based algorithms for spam filtering on document labeling. Soft Computing, 1–14.
Rahul, M., Kohli, N., Agarwal, R., & Mishra, S. (2019). Facial expression recognition using geometric features and modified hidden Markov model. International Journal of Grid and Utility Computing, 10(5), 488–496.
Mishra, S., Sagban, R., Yakoob, A., & Gandhi, N. (2018). Swarm intelligence in anomaly detection systems: an overview. International Journal of Computers and Applications, 1–10.
Goyal, A., Hossain, G., Chatrati, S. P., Bhattacharya, S., Bhan, A., Gaurav, D., &Tiwari, S. M. (2020). Smart Home Health Monitoring System for Predicting Type 2 Diabetes and Hypertension. Journal of King Saud University-Computer and Information Sciences.
Gaurav D., Yadav J.K.P.S., Kaliyar R.K., Goyal A. (2019). Detection of False Positive Situation in Review Mining. In: Wang J., Reddy G., Prasad V., Reddy V. (eds) Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing, vol 900. Springer, Singapore.
Tiwari, S. M., Jain, S., Abraham, A., & Shandilya, S. (2018). Secure Semantic Smart HealthCare (S3HC). Journal of Web Engineering, 17(8), 617–646.
Aditya, T. R., Pai, S. S., Bhat, K., Manjunath, P., & Jagadamba, G. (2020, July). Real Time Patient Activity Monitoring and Alert System. In2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 708-712). IEEE.
Fang, L., Li, Y., Liu, Z., Yin, C., Li, M., & Cao, Z. J. (2020). A Practical Model Based on Anomaly Detection for Protecting Medical IoT Control Services Against External Attacks. IEEE Transactions on Industrial Informatics.
Sihombing, P., Barus, Y. E., Sembiring, S., & Zamzami, E. M. (2020, June). The Development of Heart Rate Detection Using Arduino Microcontroller and Android. InJournal of Physics: Conference Series (Vol. 1566, No. 1, p. 012027). IOP Publishing.
Srinivasan, P., Khan, A. A., Prabu, T., Manoj, M., Ranjan, M., & Karthik, K. (2020). Heart beat sensor using fingertip through Arduino. Journal of Critical Reviews, 7(7), 1058–1060.
Li, H., & Boulanger, P. (2020). A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG). Sensors, 20(5), 1461.
Ben Amor, L., Lahyani, I., & Jmaiel, M. (2020). AUDIT: AnomaloUs data Detection and Isolation approach for mobile healThcare systems. Expert Systems, 37(1), e12390.
Phaltankar, S., Tyagi, K., Prabhu, M., Jaguste, P., Sahu, S., & Kalbande, D. CuraBand: Health Monitoring and Warning System. InInternational Conference on Innovative Computing and Communications (pp. 1017–1026). Springer, Singapore.