Remote Monitoring System of Heart Conditions for Elderly Persons with ECG Machine Using IOT Platform
الموضوعات :
Ngangbam Phalguni Singh
1
,
Aditya Kanakamalla
2
,
Shaik Azhad Shahzad
3
,
Guntupalli Divya Sai
4
,
Shruti Suman
5
1 - Koneru Lakshmaiah Education Foundation
2 - Koneru Lakshmaiah Education Foundation
3 - Koneru Lakshmaiah Education Foundation
4 - Koneru Lakshmaiah Education Foundation
5 - Koneru Lakshmaiah Education Foundation
الکلمات المفتاحية: IOT, AD8232, Arduino ide, ESP8266, ECG.,
ملخص المقالة :
These days, heart illnesses are viewed as the essential purposes behind unforeseen passing. Along these lines, different clinical gadgets have been created by designers to analyze and examine different infections. Clinical consideration has gotten one of the main issues for the two individuals and government considering enthusiastic advancement in human people and clinical use. Numerous patients experience the ill effects of heart issues making some basic dangers their life, consequently they need ceaseless observing by a conventional checking framework for example, Electrocardiographic (ECG) which is the main procedure utilized in estimating the electrical movement of the heart, this method is accessible just in the emergency clinic which is exorbitant and far for distant patients. The improvement of far-off advancements enables to develop an association of related devices by methods for the web. The proposed ECG checking framework comprises of AD8382 ECG sensor to peruse patient's information, Arduino Uno, ESP8266 Wi-Fi module, and site page. The usage of the proposed ECG medical care framework empowers the specialist to screen the patient's distantly utilizing IoT http application library utilized in Arduino ide compiler to such an extent that it can send that information to website page made, on imagining the patient's ECG signal without human presence site page itself can book arrangement for that persistent, if it is anomalous. The observing cycle should be possible at whenever and anyplace without the requirement for the emergency clinic.
[1] U. Satija, B. Ramkumar and M. Sabarimalai Manikandan, "Real-Time Signal Quality aware ECG Telemetry System for IoT-Based Health Care Monitoring," in IEEE Internet of Things Journal, vol. 4, no. 3, pp. 815-823, June 2017.
[2] R. K. Pathinarupothi, P. Durga and E. S. Rangan, "IoT-Based Smart Edge for Global Health: Remote Monitoring With Severity Detection and Alerts Transmission," in IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2449-2462, April 2019.
[3] M. A. Quiroz-Juárez, O. Jiménez-Ramírez, R. Vázquez-Medina, E. Ryzhii, M. Ryzhii and J. L. Aragón, "Cardiac Conduction Model for Generating 12 Lead ECG Signals With Realistic Heart Rate Dynamics," in IEEE Transactions on Nano Bioscience, vol. 17, no. 4, pp. 525-532, Oct. 2018.
[4] Yang Lei and Zhang Chao, "Design and Realization of Portable Rapid Electrocardiograph," China Medical devices, vol. 8, pp.II-13, 20 10.
[5] Yu Xuefei, "Theory and Design of Modern medical instrumentation," South China University of technology Press, vol. I, 2008.
[6] G. Xu, "IoT-Assisted ECG Monitoring Framework With Secure Data Transmission for Health Care Applications," in IEEE Access, vol. 8, pp. 74586-74594, 2020, doi: 10.1109/ACCESS.2020.2988059.
[7] B. M. Lee and J. Ouyang, "Intelligent Healthcare Service by using Collaborations between IoT Personal Health Devices," International Journal of Bioscience and Biotechnology, vol. 6, no. 1, p. 10, 2014.
[8] A. G. Ismaeel and E. K. Jabar, "Effective System for Pregnant Women using Mobile GIS," I J C A (0975 – 8887), vol. 64, no. 11, p. 7, 2013 2013.
[9] B. Padmavathi and S. T. Rana, "Implementation of IOT Based Health Care Solution Based on Cloud Computing," International Journal of Engineering and Computer Science, vol. 5, no. 9, p. 7, 2016.
[10] A. Ahamed, K. Hasan, and S. Alam, "Design and Implementation of Low-Cost ECG Monitoring System for the Patient Using Smartphone," presented at the (ICEEE), Rajshahi, Bangladesh, 2015.
[11] A. Škraba; A. Koložvari; D. Kofjač; R. Stojanović, Prototype of speech-controlled cloud-based wheelchair platform for disabled persons. Embedded Computing (MECO) 3rd Mediterranean Conference on, Budva, Montenegro. 15-19 June 2014, pp. 162 – 165.
[12] Karagoez, Mehmet Fatih; Turgut, Cevahir, "Design and Implementation of RESTful Wireless Sensor Network Gateways Using Node.js Framework," in European Wireless 2014; 20th European Wireless Conference; Proceedings of, vol., no., pp.1-6, 14-16 May 2014 .
[13] Carlos, R.; Coyle, S.; Corcoran, B.; Diamond, D.; Tomas, W.; Aaron, M.; Stroiescu, F.; Daly, K., "Web-based sensor streaming wearable for respiratory monitoring applications," in Sensors, 2011 IEEE, vol., no., pp.901-903, 28-31 Oct. 2011 doi: 10.1109/ICSENS.2011.6127168.
[14] P.Kalaivani, T.Thamaraiselvi, and G. V. P.Sindhuja, "Real Time ECG and Saline Level Monitoring System Using Arduino UNO Processor," A J A S T, vol. 1, no. 2, p. 5, 2017. [15] D. Barik and A. Thorat, “Issues of unequal access to public health in India,” Frontiers in public health, vol. 3, p. 245, 2015.
[16] U. Lehmann, M. Dieleman, and T. Martineau, “Staffing remote rural areas in middle- and low-income countries: A literature review of attraction and retention,” BMC Health Services Research, vol. 8, no. 1, p. 19, Jan 2008.
[16] A. Nordrum, “Italy launches a new wireless network for the internet of things,” Dec 2017.
[17] B. Gayathri, K. Sruthi, and K. A. U. Menon, “Non-invasive blood glucose monitoring using near infrared spectroscopy,” in 2017 International Conference on Communication and Signal Processing (ICCSP), April 2017, pp. 1139–1142.
[18] E. Nemati, M. J. Deen, and T. Mondal, “A wireless wearable ECG sensor for long-term applications,” IEEE Communications Magazine, vol. 50, no. 1, pp. 36–43, January 2012.
[19] S.Lavanya, G.Lavanya, and J.Divyabharathi, "Remote Prescription and I-Home Healthcare Based on IoT," presented at the I C I G E H T ’17, Coimbatore, India, 02 November 2017, 2017.
[20] C. Lastre-Dominguez, Y. S. Shmaliy, O. Ibarra-Manzano, and M. Vazquez-Olguin, ‘‘Denoising and features extraction of ECG signals in state space using unbiased FIR smoothing,’’ IEEE Access, vol. 7, pp. 152166–152178, 2019.
[21] J. Zhang, A. Liu, M. Gao, X. Chen, X. Zhang, and X. Chen, ‘‘ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network,’’ Artif. Intell. Med., vol. 106, Jun. 2020, Art. no. 101856.
[22] S. K. Pandey, R. R. Janghel, and V. Vani, ‘‘Patient specific machine learning models for ECG signal classification,’’ Procedia Comput. Sci., vol. 167, pp. 2181–2190, Jan. 2020.
[23] S. Nurmaini, A. Darmawahyuni, A. N. Sakti Mukti, M. N. Rachmatullah, F. Firdaus, and B. Tutuko, ‘‘Deep learning-based stacked denoising and autoencoder for ECG heartbeat classification,’’ Electronics, vol. 9, no. 1, p. 135, Jan. 2020.
[24] M. Faezipour and M. Faezipour, ‘‘System dynamics modeling for smartphone-based healthcare tools: Case study on ECG monitoring,’’ IEEE Syst. J., early access, Jul. 23, 2020, doi: 10.1109/JSYST.2020. 3009187.
[25] H.-T. Chiang, Y.-Y. Hsieh, S.-W. Fu, K.-H. Hung, Y. Tsao, and S.-Y. Chien, ‘‘Noise reduction in ECG signals using fully convolutional denoising autoencoders,’’ IEEE Access, vol. 7, pp. 60806–60813, 2019.
[26] F. Liu, C. Liu, L. Zhao, X. Zhang, X. Wu, X. Xu, Y. Liu, C. Ma, S. Wei, Z. He, J. Li, and E. N. Yin Kwee, ‘‘An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection,’’ J. Med. Imag. Health Informat., vol. 8, no. 7, pp. 1368–1373, Sep. 2018.
[27] A. E. Curtin, K. V. Burns, A. J. Bank, and T. I. Netoff, ‘‘QRS complex detection and measurement algorithms for multichannel ECGs in cardiac resynchronization therapy patients,’’ IEEE J. Transl. Eng. Health Med., vol. 6, pp. 1–11, 2018.
[28] S. M. Mathews, C. Kambhamettu, and K. E. Barner, ‘‘A novel application of deep learning for single-lead ECG classification,’’ Comput. Biol. Med., vol. 99, pp. 53–62, Aug. 2018.
[29] C. Venkatesan, P. Karthigaikumar, A. Paul, S. Satheeskumaran, and R. Kumar, ‘‘ECG signal preprocessing and SVM classifier-based abnormality detection in remote healthcare applications,’’ IEEE Access, vol. 6, pp. 9767–9773, 2018.
[30] J. M. Bote, J. Recas, F. Rincon, D. Atienza, and R. Hermida, ‘‘A modular low-complexity ECG delineation algorithm for real-time embedded systems,’’ IEEE J. Biomed. Health Informat., vol. 22, no. 2, pp. 429–441, Mar. 2018.
[31] X. Tang, Q. Hu, and W. Tang, ‘‘A real-time QRS detection system with PR/RT interval and ST segment measurements for wearable ECG sensors using parallel delta modulators,’’ IEEE Trans. Biomed. Circuits Syst., vol. 12, no. 4, pp. 751–761, Aug. 2018.
[32] V. H. Goh and Y. Wen Hau, ‘‘Android-based mobile application for homebased electrocardiogram monitoring device with Google technology and Bluetooth wireless communication,’’ in Proc. IEEE-EMBS Conf. Biomed. Eng. Sci. (IECBES), Dec. 2018, pp. 205–210.
[33] U. Satija, B. Ramkumar, and M. S. Manikandan, ‘‘A review of signal processing techniques for electrocardiogram signal quality assessment,’’ IEEE Rev. Biomed. Eng., vol. 11, pp. 36–52, 2018.
[34] U. Iqbal, T. Ying Wah, M. Habib Ur Rehman, and Q.-U.-A. Mastoi,‘‘Usage of model driven environment for the classification of ECG features: A systematic review,’’ IEEE Access, vol. 6, pp. 23120–23136, 2018.
