Authors:
Jaweria Azam,M. HabibUllah,Asif Nawaz,Muammad Tayyab,Muneeb Saadat,Zeeshan Najam,Sheeraz Ahmed,DOI NO:
https://doi.org/10.26782/jmcms.2020.06.00011Keywords:
ECG,Bio-Signals,Filters,IR Sensors,Quality of Service,Abstract
Electrocardiogram (ECG) plays vital role in diagnosing large number of diseases and disorders related to heart. ECG devices are able to perform multiple parameters by analyzing the patterns of bio-signals. The state-of-art ECG machine uses electrodes attached to human body using gel. The whole process agitates the patient resulting in disturbed ECG report by producing noise due to movement, imbalanced electrodes, and heavy objects. The proposed ECG system is portable finger-based system that generates ECG report in minimum time duration with providing ease to users. The system replaces disturbing electrodes by a single bio signal identification sensor. It takes signals from one finger of patient through sensor in 7 seconds. The sensor is followed up by combination of various capacitors and buffers in order to enhance signals. The signals are then transferred to software using USB port for several medical required filtrations and overall noise removal. The result of the process is an ECG signal representing heart condition of patient. The results can be stored for future medical investigations like improvement or decline in health of patient. The proposed prototype is deployed in several hospitals for testing. The system evaluated through comparison method with current system and results are satisfactory.Refference:
I Al-Ghamdi, Bandar. “Subcutaneous implantable cardioverter defibrillators: an overview of implantation techniques and clinical outcomes.” Current cardiology reviews 15, no. 1 (2019): 38-48.
II Betancourt, Nancy, Carlos Almeida, and Marco Flores-Calero. “T Wave Alternans Analysis in ECG Signal: A Survey of the Principal Approaches.” In International Conference on Information Technology & Systems, pp. 417-426.Springer, Cham, 2019.
III Castro, I. D., Carolina Varon, Jonathan Moeyersons, Amalia Villa Gomez, John Morales, Margot Deviaene, Tom Torfs, Sabine Van Huffel, Robert Puers, and Chris Van Hoof. “Data Quality Assessment of Capacitively-coupled ECG signals.” In Proceedings of the 2019 Computing in Cardiology Conference (CinC), Singapore, pp. 8-11. 2019.
IV Chien, Jun-Chau. “A 1.8-GHz Near-Field Dielectric Plethysmography Heart-Rate Sensor With Time-Based Edge Sampling.” IEEE Journal of Solid-State Circuits (2019).
V Dos Reis, Jesús E., Paul Soullié, Julien Oster, Ernesto PalmeroSoler, Gregory Petitmangin, Jacques Felblinger, and Freddy Odille. “Reconstruction of the 12‐lead ECG using a novel MR‐compatible ECG sensor network.” Magnetic resonance in medicine (2019).
VI El_Rahman, Sahar A. “Biometric human recognition system based on ECG.” Multimedia Tools and Applications (2019): 1-18.
VII Gao, Yang, Varun V. Soman, Jack P. Lombardi, Pravakar P. Rajbhandari, Tara P. Dhakal, Dale Wilson, Mark Poliks, KanadGhose, James N. Turner, and ZhanpengJin. “Heart Monitor Using Flexible Capacitive ECG Electrodes.” IEEE Transactions on Instrumentation and Measurement (2019).
VIII Kamp, Nicholas J., and Sana M. Al-Khatib. “The subcutaneous implantable cardioverter-defibrillator in review.” American heart journal (2019).
IX Lee, Jae-Ho, and Dong-WookSeo. “Development of ECG Monitoring System and Implantable Device with Wireless Charging.” Micromachines 10, no. 1 (2019): 38.
X Lee, Jae-Neung, Sung Bum Pan, and Keun-Chang Kwak. “Individual identification Based on Cascaded PCANet from ECG Signal.” In 2019 International Conference on Electronics, Information, and Communication (ICEIC), pp. 1-4. IEEE, 2019.
XI Majumder, Sumit, Leon Chen, OgnianMarinov, Chih-Hung Chen, Tapas Mondal, and M. Jamal Deen. “Noncontact wearable wireless ECG systems for long-term monitoring.” IEEE reviews in biomedical engineering 11 (2018): 306-321.
XII Marathe, Sachi, DilkasZeeshan, Tanya Thomas, and S. Vidhya. “A Wireless Patient Monitoring System using Integrated ECG module, Pulse Oximeter, Blood Pressure and Temperature Sensor.” In 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), pp. 1-4. IEEE, 2019.
XIII Rahman, Alvee, TahsinurRahman, NawabHaiderGhani, SazzadHossain, and JiaUddin. “IoT Based Patient Monitoring System Using ECG Sensor.” In 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp. 378-382. IEEE, 2019.
XIV Rachim, Vega Pradana, and Wan-Young Chung. “Wearable noncontact armband for mobile ECG monitoring system.” IEEE transactions on biomedical circuits and systems 10, no. 6 (2016): 1112-1118.
XV Roopa, C. K., and B. S. Harish. “A survey on various machine learning approaches for ECG analysis.” International Journal of Computer Applications 163, no. 9 (2017): 25-33.
XVI Steinberg, Christian, François Philippon, Marina Sanchez, Pascal Fortier-Poisson, Gilles O’Hara, Franck Molin, Jean-François Sarrazin et al. “A Novel Wearable Device for Continuous Ambulatory ECG Recording: Proof of Concept and Assessment of Signal Quality.” Biosensors 9, no. 1 (2019): 17.
XVII Saadatnejad, Saeed, MohammadhoseinOveisi, and MatinHashemi. “LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices.” IEEE journal of biomedical and health informatics (2019).
XVIII Wang, Ning, Jun Zhou, Guanghai Dai, Jiahui Huang, and YuxiangXie. “Energy-efficient intelligent ECG monitoring for wearable devices.” IEEE transactions on biomedical circuits and systems 13, no. 5 (2019): 1112-1121.
XIX Zhao, Peng, DekuiQuan, Wei Yu, Xinyu Yang, and Xinwen Fu. “Towards deep learning-based detection scheme with raw ECG signal for wearable telehealth systems.” In 2019 28th International Conference on Computer Communication and Networks (ICCCN), pp. 1-9.IEEE, 2019.
View Download