A comprehensive survey on low-cost ECG acquisition systems: Advances on design specifications, challenges and future directionA comprehensive survey on low-cost ECG acquisition systems: Advances on design specifications, challenges and future direction

• Autorzy: Faruk Nasir , Abdulkarim Abubakar , Emmanuel Ifada , Folawiyo Yusuf Y. , Adewole Kayode S. , Mojeed Hammed A. , Oloyede Abdukareem A. , Olawoyin Lukman A. , Sikiru Ismaeel A. , Nehemiah Musa , Ya’u Gital Abdulsalam , Chiroma Haruna , Ogunmodede James A. , Almutairi Mubarak , Katibi Ibraheem A.

Identyfikatory

DOI

10.1016/j.bbe.2021.02.007

• Języki publikacji: EN

Abstrakty

EN

Availability of low-cost, reliable, and portable Electrocardiography (ECG) devices is still very important in the medical world today. Despite the tremendous technological advancement, Cardiovascular Diseases (CVDs) remain a serious health burden claiming millions of lives on an annual basis globally. This is more prevalent in Low and Middle-Income Countries (LMICs) where there are huge financial instability and lack of critical infrastructure and support services for the health care system. Efforts aimed at reducing the prevalence of CVDs are confounded by late diagnosis, frequently, caused by lack of access to or nonavailability of basic diagnostic modalities such as the ECG. Hence effective mitigation of the effect of CVDs in LMICs depend on the development of such devices at low-cost with reliability, accuracy and energy efficiency. This paper therefore, was developed to understand the state of the art of low-cost ECG acquisition systems with respect to design features and system capabilities for different use cases. In addition, different design options and taxonomies of available low-cost ECG devices, case studies reports of efficacy tests have been provided. The paper proposes a generalised ECG framework and provides implementation challenges and open research directions that should be considered when developing such devices for proper management of CVDs.

Słowa kluczowe

EN

electrocardiography; ECG; cardiovascular disease; ECG acquisition systems; CVD; low-cost ECG devices

PL

elektrokardiografia; EKG; choroba układu krążenia; system akwizycji; CVD

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 2
• Strony: 474--502
• Opis fizyczny: Bibliogr. 155 poz., rys., tab., wykr.

Twórcy

autor

Faruk Nasir

• Department of Telecommunication Science, University of Ilorin, Nigeria

autor

Abdulkarim Abubakar

• Department of Electrical & Electronics Engineering, Ahmadu Bello University Zaria, Nigeria

autor

Emmanuel Ifada

• Department of Telecommunication Science, University of Ilorin, Nigeria

autor

Folawiyo Yusuf Y.

• Department of Telecommunication Science, University of Ilorin, Nigeria

autor

Adewole Kayode S.

• Department of Computer Science, University of Ilorin, Nigeria

autor

Mojeed Hammed A.

• Department of Computer Science, University of Ilorin, Nigeria

autor

Oloyede Abdukareem A.

• Department of Telecommunication Science, University of Ilorin, Nigeria

autor

Olawoyin Lukman A.

• Department of Telecommunication Science, University of Ilorin, Nigeria

autor

Sikiru Ismaeel A.

• Department of Information and Communication Science, University of Ilorin, Nigeria

autor

Nehemiah Musa

• Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria

autor

Ya’u Gital Abdulsalam

• Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria

autor

Chiroma Haruna

• Future Technology Research Center, National Yunlin University of Science and Technology, Taiwan; Department of Computer Science, Federal College of Education, Gombe, Nigeria; Department of Computer Science and Engineering, University of Hafr Albatin, Saudi Arabia

autor

Ogunmodede James A.

• Department of Telecommunication Science, University of Ilorin, Nigeria

autor

Almutairi Mubarak

• Department of Computer Science and Engineering, University of Hafr Albatin, Saudi Arabia

autor

Katibi Ibraheem A.

• Department of Medicine, University of Ilorin, Nigeria

Bibliografia

• [1] Mendis STK, Kuulasmaa K, Giampaoli S, Ma¨ho¨nen M, Ngu Blackett K, Lisheng L. World Health Organization definition of myocardial infarction: 2008–09 revision. Int J Epidemiol 2009;40(1):139–46.
• [2] Lawrence JL, Peter AJ, Bernard A, Joseph PD, Frances S, Wilson SR, Chris P, Menolly H. The worldwide environment of cardiovascular disease: prevalence, diagnosis, therapy, and policy issues. J Am Coll Cardiol 2012;60(25).
• [3] WHO, http://www.who.int/news-room/fact-sheets/ detail/cardiovascular-diseases-(cvds), 2018.
• [4] IHME, Global Burden of Disease (GBD), http://www. healthdata.org/node/835, 2016.
• [5] Thisday, Nigeria records 150,000 death annually due to heart diseases, https://www.thisdaylive.com/index.php/2017/10/ 05/nigeria-records-150000-deaths-annually-due-to-heartdiseases/ accessed on Nigeria, 2017.
• [6] Mukadas AO, Misbau U, Incidence and patterns of cardiovascular disease in north western Nigeria, 50(1), 2009, 35–57.
• [7] Zimmermann-Schlegel V, Wild B, Nawroth P, Kopf S, Herzog W, Hartmann M. Impact of depression and psychosocial treatment on heart rate variability in patients with type 2 diabetes mellitus: an exploratory analysis based on the HEIDIS t. Exp Clin Endocrinol Diabetes 2019;127(6):367–76.
• [8] Liu JE, Palmieri V, Roman MJ. The impact of diabetes on left ventricular filling pattern in normotensive and hypertensive adults: the Strong Heart Study. J Am Coll Cardiol 2001;37:1943–9.
• [9] Mahoney, D.W., Jacobsen, S.J., Rodeheffer, R.J., Burnett Jr, J.C., Redfield, M.M., & Bailey, K.R. (2003). Burden of systolic and diastolic ventricular dysfunction in the community. Jama, 289, 194.
• [10] Bizino MB, Jazet IM, Westenberg JJ, van Eyk HJ, Paiman EH, Smit JW, et al. Effect of liraglutide on cardiac function in patients with type 2 diabetes mellitus: randomized placebocontrolled trial. Cardiovascular Diabetology 2019;18(1):55.
• [11] He B, Cohen R. Body surface Laplacian ECG mapping. IEEE Trans Biomed Eng 1992;39(11):1179–97.
• [12] Pipberger HV, Simonson E, Lopez Jr E. The electrocardiogram in epidemiologic investigations. A new classification system. Circulation 1982;65(7):1456–64.
• [13] F. Nemocnice, Elektrokardiografie (EKG), March 2018. [Online]. Available: http://kard.fnplzen.cz/cs/node/107. [Accessed March 2020].
• [14] P. Tomáš, M. Novotný, Low-Cost Portable ECG, in 2019 8th Mediterranean Conference On Embedded Computing (Meco), Budva, Montenegro, 2019.
• [15] R. Fensli, E. Gunnarson, O. Hejlesen, A Wireless ECG System for Continuous Event Recording and Communication to a Clinical Alarm Station, in Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA, USA, 2004.
• [16] Faruk N, Surajudeen-Bakinde NT, Abdulkarim A, Oloyede AA, Olawoyin L, Bello OW, et al. Rural healthcare delivery in Sub-Saharan Africa: An ICT-driven approach. Int J Healthc Inf Syst Inf IJHISI 2020;15(3):1–12.
• [17] C. Christopher C, K. Charles R and D. K. Andrew. Comparing 14-day adhesive patch with 24-h Holter monitoring. Future Cardiol 2014;10(3):319–22.
• [18] C. Víctor, J. H. Francisco, L. Gregorio, M. Jos éIgnacio, A Review on Architectures and Communications Technologies for Wearable Health-Monitoring Systems, MDPI Sensors, 12, 2012.
• [19] Shyamal P, Hyung P, Paolo B, Leighton C, Mary R. A review of wearable sensors and systems with application in rehabilitation. J Neuro Eng Rehab 2012;9(21).
• [20] Mirza MB, Hamid G, Martin JC. A comprehensive survey of wearable and wireless ECG monitoring systems for older adults. Int Feder Med Biol Eng 2013.
• [21] Vidyashree NK, Sathyanarayana BS, Kumar SCP, Sudarshan BG. A review on design of portable ECG system. Int J Innov Res Dev 2013;2(5).
• [22] K.J. Puneet, K.T. Anil, Heart Monitoring Systems – A Review, 2014. [Online]. Available: https://doi.org/10.1016/ j.compbiomed.2014.08.014.
• [23] Nicole L, Ines K, Lis N, Julie R, Andrew MJ, Alexandra BA, Ben FS. Atrial fibrillation screening in pharmacies using an iPhone ECG: A qualitative review of implementation. Int J Clin Phar 2015;10(169).
• [24] Butta S, Amandeep K, Jugraj S. A review of ECG data compression techniques. Int J Comput Appl 2015;116(11).
• [25] Khudhur AA, Yusoff MM, Abdulrahman MS. Design and review of wireless ecg monitoring systems. Int Acad Eng Med Res 2016;1(2).
• [26] SabarimalaiSur M, Dandapat S. Wavelet-based electrocardiogram signal compression methods and their performances: A prospective review. Biomed Signal Process Control 2014;14:73–107.
• [27] Rene J, Radek M, Radana K. Non-adaptive methods for fetal ECG signal processing: A review and appraisal. Sensors. MDPI 2018;18(3648).
• [28] Meenakshi R, Hemanth C, Menaka R. Review of Energy Efficient factors for ECG Devices. WSEAS Trans Commun 2016;16.
• [29] Merone M, Paolo S, Mario S, Carlo S. ECG databases for biometric systems: A systematic review. Expert Syst Appl 2016;10(1016).
• [30] Albahri OS, Albahri AS, Mohammed KI, Zaidan AA, Zaidan BB, Hashim M, et al. Systematic review of realtime remote health monitoring systemin triage and priority-based sensor technology: taxonomy, openchallenges, motivation and recommendations. J Med Syst 2018:42–80.
• [31] Furrukh S, Eric IM, Jagmeet SP, Kevin HE, Bhupesh P, Antonis AA. Wearable devices for ambulatory cardiac monitoring. J Am College Cardiol 2020;75(13).
• [32] D. Chieng, V. Paul, R. Denman, Current device therapies for sudden cardiac death prevention–the icd, subcutaneous icd and wearable ICD, 2019.
• [33] A.S. Mohamed, H.T. Kassabi, I. Heba, N.N. Alramzana, ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges, Sensors, MDPI, 2020.
• [34] Faust O, Acharya R, Eddie N, Fujita H. A review of ECG-Based diagnosis support systems for obstructive sleep apnea. J Mech Med Biol 2016;16(1640004).
• [35] K.B. Sagar, D.H. Gawali, Review of ECG Compression Techniques and Implementations, in 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication, 2016.
• [36] Guan K, Shao M, Wu S. A remote health monitoring system for the elderly based on smart home gateway. L Health Care 2017.
• [37] Tuli S, Basumatary N, Gill S, Kahani M, Arya R, Wander G, et al. HealthFog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments. Futur Gener Comput Syst 2020;104:187–200.
• [38] C. Ji, F. Liu, Z. Wang, Y. Li, C. Qi, Z. Li, Mobile cloud ECG intelligent monitoring and data processing system, in Proceedings of the 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services Healthcom, Dalian, China, 2017.
• [39] Akrivopoulos O, Amaxilatis D, Mavrommati I, Chatzigiannakis I. Utilising fog computing for developing a person-centric heart monitoring system. in Proceedings of the 2018 International Conference on Intelligent, 2018.
• [40] O. Akrivopoulos, A. Antoniou, D. Amaxilatis, I. Chatzigiannakis, Design and evaluation of a person-centric heart monitoring system over fog computing infrastructure, in 1st International Workshop on Human-Centered Sensing, Networking, and Systems Part of SenSys, , Delft, The Netherlands, 2017.
• [41] Xia H, Asif I, Zhao X. Cloud-ECG for real time ECG monitoring and analysis. Comput Methods Programs Biomed 2013;110:253–9.
• [42] M. Bansal, B. Gandhi, IoT big data in smart healthcare (ECG monitoring), in Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects COMITCon,, Faridabad, India, 2019.
• [43] Neyja M, Mumtaz S, Huq K, Busari S, Rodriguez J, Zhou Z. An IoT-based e-health monitoring system using ECG signal. Proceedings of the GLOBECOM 2017 IEEE Global Communications Conference, 2017.
• [44] E. Jovanov, D. Raskovic, J. Price, J. Chapman, A. Moore, A. Krishnamurthy, Patient monitoring using personal, Area networks of wireless intelligent sensors. Biomed. Sci. Instrum., 373–378, 2001.
• [45] M. Smoleń, E. Kańtoch, P. Augustyniak, P. Kowalski, Wearable patient home monitoring based on ECG, in and ACC sensors. IFMBE, IFMBE, 2011.
• [46] M. Karaoğuz, E. Yurtseven, G. Aslan, B. Deliormanlı, Ö . Adıgüzel, M. Gönen, K. Li, E. Yılmaz, The quality of ECG data acquisition, and diagnostic performance of a novel adhesive patch for ambulatory cardiac rhythm monitoring in arrhythmia detection, J. Electrocardiol, 54, 28–35, 2019.
• [47] Rachim V, Chung W. Wearable noncontact armband for mobile ECG monitoring system. IEEE Trans Biomed Circ Syst 2016:1112–8.
• [48] Attin M, Feld G, Lemus H, Najarian K, Shandilya S, Wang L, et al. Electrocardiogram characteristics prior to in-hospital cardiac arrest. J Clin Monit Comput 2015;29:385–92.
• [49] Rafiq S, Alam A, Islam M. Development of ECG home monitoring system. Proceedings of the IEEE Regional Symposium on Micro and Nanoelectronics RSM, 2017.
• [50] Lackner H, Eglmaier M, Hackl-Wimmer S, Paechter M, Rominger C, Eichen L, et al. How to use heart rate variability: Quantification of vagal activity in toddlers and adults in long-term ECG. Sensors 2020;20(5959):1–21.
• [51] Holmes C, Fedewa M, Winchester L, Macdonald H, Wind S, Esco M. Validity of smartphone heart rate variability pre-and post-resistance exercise. Sensors 2020, 2020,;20:1–13.
• [52] S. Preejith, R. Dhinesh, J. Joseph and M. Sivaprakasam, Wearable ECG platform for continuous cardiac monitoring, in Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC}, Lake Buena Vista, FL, USA, 2016.
• [53] Mansouri S, Farahmand F, Vossoughi G, Ghavidel A. A comprehensive multimodality heart motion prediction algorithms for robotics asisted beating heart surgery. Int J Med Robot Comput Asist Surgery 2019:1–12.
• [54] Ma Y, Zhang Y, Wan J, Zhang D, Pan N, Org D. Robot and cloud-assisted multi-modal healthcare enabling comfortable sport theraphy for petients: A Novel light weight and durable ECG monitoring system vie project VEDNET. Comput Netw 2015:1295–306.
• [55] E. &. S. A. Alickovic, ‘‘Medical decision support system for diagnosis of heart arrhythmia using DWT and random forests classifier,” p. 108, 2016.
• [56] Ye C, Kumar BVKV, Coimbra MT. An automatic subjectadaptable heartbeat classifier based on multiview learning. J Biomed Health Inf Nov 2016;20(6):1485–92.
• [57] Wang T, Lu C, Sun Y, Yang M, Liu C, Ou C. Automatic ECG classification using continuous wavelet transform and convolutional neural network. Entropy 2021;23(1):119.
• [58] P. Malleswari, C. Bindu, K. Prasad, Cardiac Severity Classification Using Pre Trained Neural Networks., Interdiscip Sci Comput Life Sci, 2021.
• [59] Zeng W, Yuan J, C. e. a. Yuan. A novel technique for the detection of myocardial dysfunction using ECG signals based on hybrid signal processing and neural networks. Soft Comput 2021.
• [60] Cairns AWBRRFDDGDBFLGLSJ. A decision support system and rule-based algorithm to augment the human interpretation of the 12-lead electrocardiogram. J Electrocardiol 2017:781–6.
• [61] Ripoll, V.J.R., Wojdel, A., Romero, E., Ramos, P., & Brugada, J. (2016). ECG assessment based on neural networks with pretraining. Applied Soft Computing, 49, 399-406.
• [62] R. Czabanski, K. Horoba, J. Wrobel, A. Matonia, R. Martinek, T.L.J.M. Kupka, Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine, 2020.
• [63] R.R. Sharma, A. Kumar, R.B.A.U.R. Pachori, Accurate automated detection of congestive heart failure using eigenvalue decomposition based features extracted from HRV signals, 2019.
• [64] M. Alfaras, S. Ortín, A fast machine learning model for ECGbased heartbeat classification and arrhythmia detection, 2019.
• [65] B. Va, R. Pujeri, G.D.D. &. GB-SVNN, Genetic BAT assisted support vector neural network for arrhythmia classification using ECG signals, J King Saud Univ Comput Inf Sci, 2018.
• [66] T.N. Alotaiby, S.R. Alrshoud, S.A.A. &. L.M. Aljafar, ECGBased Subject Identification Using Statistical Features and Random Forest, Hindawi J Sensors, pp. 1–14, 2019.
• [67] S. Brás, J.H.T. Ferreira, S.C.S. &. A.J. Pinho, Biometric and Emotion Identification: An ECG Compression Based Method, Front Psychol, 9, 467, pp. 1–11, 2018.
• [68] Yeen CC, Ramli DA. Development of heartbeat based biometric system using wavelet transform. J Eng Sci 2018;14:15–33.
• [69] Qiong QY, Huawen Y, Lin S, Wenya G, Hongxing L, Junfeng S, et al. Automatic identifying of maternal ECG source when applying ICA in fetal ECG extraction. Biocybern Biomed Eng 2018:1–8.
• [70] S. Zhou, B. Bo Tan, Electrocardiogram soft computing using hybrid deep learning CNN-ELM, Appl Soft Comput, https:// dx.doi.org/10.1016/j.asoc.2019.105778, 2019.
• [71] Bansal A, Joshi R. Portable out-of-hospital electrocardiography: A review of current technologies. J Arryhthmia 2017;34:129–38.
• [72] S.M. Lobodzinski, ECG instrumentation: Application and design, Comprehensive Electrocardiology, London, Springer, 2010, pp. 427–480.
• [73] J. Webster, Medical instrumentation, application and design, 3, Ed., New York, John Wiley and Son, 1998.
• [74] P.W. Macfarlane, Lead Systems, in Comprehensive Electrocardiology, London, Spriger, 2010, pp. 375–425.
• [75] L. Yan, M. Sriyudthsaka, Design and development of standard 12-lead ECG data acquisition and monitoring system, in 2016 International Electrical Engineering Congress, iEECON2016,, Chiang Mai, Thailand, 2016.
• [76] C.S. Chandra, C.G. Chandra, ECG Data Acquisition System with 0.5v supply by using digital front end architecture, Int J Adv Eng Res Sci (IJAERS), no. 4, pp. 121–126, 2017.
• [77] B.J. Abhishek, R. Bagree, K.R. Anil, Design of a Low-power, Low-cost ECG & EMG Sensor for Wearable Biometric and Medical Application, 2017.
• [78] D. Jayita, S. Sukanta, C. Sayantan, A. Aditya, Development of An ECG Signal Acquisition Module, in Devices for Integrated Circuit (DevIC), Kalyani, India, 2017.
• [79] C. Ciprian, P. Alexandru, A. Gladiola, D. Bogdan, R. Cristian, A Wireless ECG Acquisition Device for Remote Monitoring of Heart Rate and Arrhythmia Detection, in 5th IEEE International Conference on E-Health and Bioengineering, Iasi, Romania, 2015.
• [80] Pallás-Areny R. Interference-rejection characteristics of biopotential amplifiers: A comparative analysis. IEEE Trans Biomed Eng 1988;35:953–9.
• [81] Winter BB, Webster JG. Reduction of interference due to common-mode voltage in biopotential amplifiers. IEEE Trans Biomed Eng 1983;30:58–62.
• [82] L. Federica, P. Eros, Hardware Design of a Wearable ECGSensor: Strategies Implementation for improving CMRR and reducing noise, 2017.
• [83] B.R. Manju, M. Sneha, ECG Denoising Using Wiener Filter and Kalman Filter, 2020.
• [84] G. Wang, L. Yang, M. Liu, X. Yuan, P. Xiong, F. Lin, X. Liu, ECG Signal denoising based on deep factor analysis, 2020.
• [85] TI, Improving Common-Mode Rejection Using the Right-Leg Drive Amplifier, Texas Instruments, Application Report, SBAA188, 2011.
• [86] A.A.P.a.C.G. MettingVanRijn, High quality recording of bioelectric events. Part : Interference reduction, theory and practice, Med Biol Eng Comput, 28, pp. 389–397, 1990.
• [87] Thakor NV, Webster JG. Ground-free ECG recording with two electrodes. IEEE Trans Biomed Eng 1980;27:699–704.
• [88] Winter BB, Webster JG. Driven-right-leg circuit design. IEEE Trans Biomed Eng Jan 1983;30(1):62–6.
• [89] H. Sohn, C.R. Farrar, F.M. Hemez, D.D. Shunk, D.W. Stinemates, B.R. Nadler, J.J. Czarnecki, A Review of Structural Health Monitoring Literature: 1996–2001, Los Alamos National Laboratory, Los Alamos, NM USA, pp. 1–7, 2003.
• [90] W. Fan, W. Taiyang, R.Y. Mehmet, An internet-of-things (IoT) network system for connected safety and health monitoring applications, Sensor, MDPI, 19, 21, 2018.
• [91] A. Affanni, Wireless sensors system for stress detection by means of ECG and EDA acquisition, Sensors, 2020.
• [92] E. Nemati, M. Deen, T. Mondal, A wireless wearable ECG sensor for long-term applications, IEEE Commun Mag, pp. 50, 36–43, 2012.
• [93] Kevin CT, Bor-Shyh L, Lun-De L, Yu-Te W, Yu-Lin W. Development of a wearable mobile electrocardiogram monitoring system by using novel dry foam electrodes. IEEE Syst J 2014;8(3):900–6.
• [94] Lee J, Heo J, Lee W, Lim Y, Kim Y, Park K. Flexible capacitive electrodes for minimizing motion artifacts in ambulatory electrocardiograms. Sensors 2014;14:14732–43.
• [95] V. Randazzo, E. Pasero, S. Navaretti, VITAL-ECG: A portable wearable hospital, in IEEE Sensors, Seoul, South Korea, 2018.
• [96] V. Randazzo, J. Ferretti, E. Pasero, A wearable smart device to monitor multiple vital parameters—VITAL ECG, MDPI, Eelctronics, 9, 300, 2020.
• [97] Lou C, Li R, Li Z, Liang T, Wei Z, Run M, et al. Flexible graphene electrodes for prolonged dynamic ECG monitoring. Sensors 2016;16(11):1833.
• [98] Z. Li, W. Guo, Y. Huang, K.Y.H. Zhu, H. Wu, On-skin graphene electrodes for large area electrophysiological monitoring and human-machine interfaces, 164, 164–170, 2020.
• [99] Du X, Niu Z, Li R, Yang H, Hu W. Hughly adhesive Washable and Stretchable on-skin electrodes based on polydopamine and silk fibroin for ambulatory electrocardiography sensing. J Mater Chem C 2020;8(35):12257–64.
• [100] Yang H, Ji S, Chaturvedi I, Xia H, Wang T, Chen G, et al. Adhesive biocomposite electrodes on sweaty skin for longterm continuous electrophysiological monitoring. ACS Mater Lett 2020;2(5):478–84.
• [101] X. Tao, C. Cochrane, V. Koncar, Understanding the washing damage to textile ECG dry skin electrodes, embroidered and fabric-based; set up of equivalent laboratory tests, 20, 5, p. 1272, 2020.
• [102] A. Pantelopoulos, N.A. Bourbakis, Survey on wearable sensor-based systems for health monitoring and prognosis, IEEE Trans Syst Man Cybern, pp. 40, 1–12, 2010.
• [103] Majumder S, Mondal T, Jamal D. Wearable sensors for remote health monitoring. Sensors, MDPI 2017;17(130):3.
• [104] S. Bashyam, B. Ramachandran, Development of real-time ECG signal monitoring system for telemedicine application, in 2017 3rd International Conference on Biosignals, images and instrumentation (ICBSII), Chennai, 2017.
• [105] L. Lianxi, H. Lei, Y. Zhang, H. Tianyuan, A battery-less portable ECG monitoring systemwith wired audio transmission, IEEE Trans Biomed Circuits Syst, 13, 4, 2019.
• [106] N.A. Abdul-Kadir, N.S. Sahar, H. Chan, F.K.C. Harun, A Portable WiFi ECG, in 38th International Electronics Manufacturing Technology Conference, Johor, 2018.
• [107] D.A. Nilanjan, S. Ashour, F. Shi, J.F. Simon, S.R. Simon, Developing residential wireless sensor networks for ECG healthcare monitoring, IEEE Trans Cons Electron, 63, 4, 2017.
• [108] G.E.B. Stanley, V. Joel, T.A. Christopher, D.M. Shaira, U.R.J. Wainwright, A.M. Zamora, Development of a 12-lead ECG signal processing algorithm using NI LabVIEW and NI ELVIS,‘‘ in IEEE 7th Global Conference on Consumer Electronics, Makati City, 2018.
• [109] Y. Ungson, M.A. Reyna, M.E. Bravo-Zanoguera, Development of an ambulatory ECG system based on Arduino and mobile telephony for wireless transmission, in Pan American Health Care Exchanges (PAHCE) Conference, workshops, and exhibition, 2014.
• [110] L. Chao, H. Xiangpei, Z. Lili, The IoT-based heart disease monitoring system for pervasive health care service, in International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Marseille, 2017.
• [111] Ee-May F, Wan-Young C. Mobile cloud-computing-based healthcare service by non-contact ECG monitoring. MDPI Sens 2013;2013(13):16451–73.
• [112] L. Yuan, Y. Yuan, Z. Zhou, Y. Bai, S. Wu, A fetal ECG monitoring system based on the android smartphone, Sensor, pp. 1–14, 2019.
• [113] Y. Sung-Nien, C. Jen-Chieh, Wireless physiological signal monitoring system with integrated bluetooth and WiFi technologies, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, 2005.
• [114] S.M. Ahmad, M.N. Hasliza, Development of smart ECG machine using labview for biomedical engineering students, in 4th International Conference on Engineering Technology and Technopreneuship (ICE2T), Selangor, 2014.
• [115] L. Tai, T. Le-Lan, H. Thai-Hoc, H. Huy-Dung, T.P. Ngoc, N. VuCong, A.N. Tuan, T.T.P. Nga, C. Hung, K.N. Cuong, A portable ECG monitoring device with active dry electrodes and its validation, in 2019 IEEE MTT-S International Microwave Biomedical Conference, Hanoi, 2019.
• [116] S. Elisa, D.P. Stefano, I. Giuseppe, Low-power wearable ECG monitoring system for multiple-patient remote monitoring, IEEE Sens J, 16, 13, 2016.
• [117] E. Ifada, N.T. Surajudeen-Bakinde, F. Nasir, A. Abubakar, O.O. Mohammed, A.O. Otuoze, Implementation of a data transmission system using Li-Fi technology, in The role of Computing in the Evolution and Development of Emerging and Alternative Technologies, 2nd International Conference of the IEEE Nigeria Computer Conference, Zaria, Nigeria, 2019.
• [118] L. Tai, H. Huy-Dung, H. Thai-Hoc, V.C. Nguyen, C.K. Nguyen, A low cost mobile ECG monitoring device using two active dry electrodes, 2019.
• [119] H. Yang, J. Chai,‘‘A portable wireless ECG monitor based on MSP430FG439, in International Conference on Intelligent Computation and Biomedical Instrumentation, Hubei, China, 2011.
• [120] C. Komkrit, M. Pongpat, Wireless Heart Rate Monitoring System using MQTT, in 2016 International Electrical Engineering Congress, iEECON2016, Chiang Mai, Thailand, 2016.
• [121] N.C. Jian, Y.T. Yan, M.C. Liu, L. Yang, The Development of a portable ECG monitor based on DSP, in International Conference on Medical Physics and Biomedical Engineering, 2012.
• [122] Y. Bin, X. Lisheng, L. Yongxu, Bluetooth low energy (BLE) based mobile electrocardiogram monitoring system, in IEEE International Conference on Information and Automation, Shenyang, China, 2012.
• [123] M. Bandana, K.P. Ajit, Heart rate monitoring system using finger tip through arduino and processing software, Int J Sci Eng Technol Res IJSETR, 5, 1, January 2016.
• [124] Majdi B, Hlaing M, Lakshman T. Apnea MedAssist: Real-time sleep apnea monitor using single-lead ECG. IEEE Trans Inf Technol Biomed May 2011;15(3).
• [125] Aleksandra R, Matjaž D, Tomašic I, Viktor A. Medical-grade ECG sensor for long-term monitoring. Sensors, MDPI March 2020;20(1695).
• [126] Katya A, Andrea KW, Allison PA. Textile electrocardiogram (ECG) electrodes for wearable health monitoring. Sensors, MDPI February 2020;20(1013).
• [127] Tomas U, Vacius J. Development of a modular board for EEG signal acquisition. Sensors, MDPI July 2018;18(2140).
• [128] F. Pinho, J.H. Correia, N.J. Sousa, J.J. Cerqueira, N.S. Dias, Wireless and wearable EEG acquisition platform for ambulatory monitoring, in IEEE 3nd International Conference on Serious Games and Applications for Health (SeGAH), Rio de Janeiro, Brazil, 2014.
• [129] Randazzo V, Ferretti J, Pasero E. A wearable smart device to monitor multiple vital parameters. MDPI Eelctron February 2020;9(300).
• [130] Shahrudin NSN, Sidek KA, Jusoh AZ. Electrocardiogram (ECG) based stress recognition integrated with different classification of age and gender. Indonesian J Electr Eng Comput Sci 2019;15:199–210.
• [131] Burke MJ, Gleeson DT. A micropower dry-electrode ECG preamplifier. Biomed Eng IEEE Trans 2000;4(2):155–62.
• [132] Nagaraju M, Silver J, Zhang F, Liao Y, Pandey J, Morrison T, et al. Circuit techniques for wireless bioelectrical interfaces. International Symposium on VLSI Design Automation and Test (VLSI-DAT), 2010.
• [133] Bansal A, Joshi R. Portable out-of-hospital electrocardiography: A review of current technologies. J Arrhythmia 2018;34(2):129–38.
• [134] Haverkamp HT, Fosse SO, Schuster P. Accuracy and usability of single-lead ECG from smartphones – A clinical study. Indian Pacing Electrophysiol J 2019;19(4):145–9.
• [135] Mehta DD, Nazir NT, Trohman RG, Volgman AS. Single-lead portable ECG devices: Perceptions and clinical accuracy compared to conventional cardiac monitoring. J Electrocardiol 2015;48(4):710–6.
• [136] M.S. Kraus, M. Rishniw, T.J. Divers, V.B. Reef, A.R. Gelzer, Utility and accuracy of a smartphone-based electrocardiogram device as compared to a standard baseapex electrocardiogram in the horse, Research in veterinary science, pp. 125–141, 2019.
• [137] I. Nault, P. André, B. Plourde, F. Leclerc, J.F. Sarrazin, F. Philippon, L. Blier, Validation of a novel single lead ambulatory ECG monitor–CardiostatTM–Compared to a standard ECG Holter monitoring, J Electrocardiol, pp. 53, 57, 2019.
• [138] Kumar S, Nagesh CM, Singh M, Pandian A, Delurgio D, Khan B, et al. Assessment of diagnostic accuracy of SanketLife–A wireless, pocket-sized ECG biosensor, in comparison to standard 12-lead ECG in the detection of cardiovascular diseases in a tertiary care setting. Indian Pacing and Electrophysiol J 2020;20(2):54–9.
• [139] Halcox JP, Wareham K, Cardew A, Gilmore M, Barry JP, Phillips C, et al. Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation: the REHEARSE-AF study. Circulation 2017;136(19):1784–94.
• [140] T.J. Lumikari, J. Putaala, A. Kerola, G. Sibolt, J. Pirinen, S. Pakarinen, T. Nieminen, Continuous 4-week ECG monitoring with adhesive electrodes reveals AF in patients with recent embolic stroke of undetermined source, Annals Noninvasive Electrocardiol, 24,5, p. e12649, 2019.
• [141] Barrett PM, Komatireddy R, Haaser S, Topol S, Sheard J, Encinas J, et al. Comparison of 24-hour Holter monitoring with 14-day novel adhesive patch electrocardiographic monitoring. Am J Med 2014;127(1):95–e11.
• [142] Singh N, Chun S, Hadley D, Froelicher V. Clinical implications of technological advances in screening for atrial fibrillation. Prog Cardiovasc Dis 2018;4(5):550–9.
• [143] Healey JS, Alings M, Ha A, Leong-Sit P, Birnie DH, de Graaf JJ, et al. Subclinical atrial fibrillation in older patients. Circulation 2017;136(14):1276–83.
• [144] Kane SA, Blake JR, McArdle FJ, Langley P, Sims AJ. Opportunistic detection of atrial fibrillation using blood pressure monitors: A systematic review. Open Heart 2016;3 (1) e000362.
• [145] Lowres N, Neubeck L, Salkeld G, Krass I, McLachlan AJ, Redfern J, et al. Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. Thromb Haemost 2014;111(06):1167–76.
• [146] Karunadas CP, Mathew C. Comparison of arrhythmia detection by conventional Holter and a novel ambulatory ECG system using a patch and Android App, over 24 h period. Indian Pacing Electrophysiol J 2020;20(2):49–53.
• [147] Rosenberg MA, Samuel M, Thosani A, Zimetbaum PJ. Use of a noninvasive continuous monitoring device in the management of atrial fibrillation: a pilot study. Pacing Clin Electrophysiol 2013;36(3):328–33.
• [148] Hindricks G, Taborsky M, Wohlgemuth P, Rieger G, Beckers F, Albers B. Atrial fibrillation detection by a subcutaneous monitoring device. Comput Cardiol September 2008:413–6.
• [149] Chan PH, Wong CK, Pun L, Wong YF, Wong MMY, Chu DWS, et al. Head-to-Head comparison of the aliveCor heart monitor and microlife watchBP office AFIB for atrial fibrillation screening in a primary care setting. Circulation 2017;135(1):110–2.
• [150] Kearley KSM, Van-den-Bruel A, Thompson M, Mant D, Hobbs FR, Heneghan C. Triage tests for identifying atrial fibrillation in primary care: A diagnostic accuracy study comparing single-lead ECG and modified BP monitors. BMJ Open 2014;4(5) e004565.
• [151] G. Marazzi, F. Iellamo, M. Volterrani, M. Lombardo, F. Pelliccia, D. Righi, G. Rosano, Erratum to: comparison of microlife BP A200 plus and omron M6 blood pressure monitors to detect atrial fibrillation in hypertensive patients, Adv Therapy, 31, 12, 1317, 2014.
• [152] Tung CE, Su D, Turrakhia MP, Lansberg MG. Diagnostic yield of extended cardiac patch monitoring in patients with stroke or TIA. Front Neurol 2015;5:266.
• [153] A. Abdulkarim, S.M. Abdelkader, D.J. Morrow, Reliability importance measures of components for stand-alone hybrid renewable energy microgrid, in 2nd International Congress on Energy Efficiency and Energy Related Materials (ENEFM2014), Springer, Cham, 2015, pp. 3–7.
• [154] Abdulkarim A, Nasir F, Olayede A, Olawoyin L, Akorede M, Madugu I, et al. Reliability study of stand-alone hybrid renewable energy microgrids. Iran J Sci Technol Trans Electr Eng, Springer 2018: 1-15.
• [155] N. Faruk, N.T. Surajudeen-Bakinde, A.A. Oloyede, O.O. Bello, S.I. Popoola, A. Abdulkarim, L.A. Olawoyin, On green virtual clinics: A framework for extending health care services to rural communities in Sub-Saharan Africa, in Proc. International Rural and Elderly Health Informatics Conference (IREHI), Benin Republic, 2017.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).