A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments

The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.

[1]  A. Taddei,et al.  Long-term ST database: A reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia , 2003, Medical and Biological Engineering and Computing.

[2]  Christoph Busch,et al.  Biometric Presentation Attack Detection: Beyond the Visible Spectrum , 2019, IEEE Transactions on Information Forensics and Security.

[3]  Rafael L. Gomes,et al.  Electrocardiogram Fiducial Points Detection for Health Care Systems , 2018, 2018 IEEE Symposium on Computers and Communications (ISCC).

[4]  Swati D. Shirke,et al.  Iris Recognition Using Visible Wavelength Light Source and Near Infrared Light Source Image Database: A Short Survey□ , 2019, 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI).

[5]  Richard D. Gitlin,et al.  A Random Access Scheme for Large Scale 5G/IoT Applications , 2018, 2018 IEEE 5G World Forum (5GWF).

[6]  Yuanjian Liu,et al.  Millimeter-Wave Channel Simulation and Statistical Channel Model in the Cross-Corridor Environment at 28 GHz for 5G Wireless System , 2018, 2018 International Conference on Microwave and Millimeter Wave Technology (ICMMT).

[7]  Vijay Kumar Sharma,et al.  Detection and classification of ECG noises using decomposition on mixed codebook for quality analysis , 2020, Healthcare technology letters.

[8]  Parmveer Singh,et al.  IoT based low-cost distant patient ECG monitoring system , 2017, 2017 International Conference on Computing, Communication and Automation (ICCCA).

[9]  Celia Shahnaz,et al.  Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains , 2012, Biomed. Signal Process. Control..

[10]  Abdulmotaleb El-Saddik,et al.  ECG Authentication for Mobile Devices , 2016, IEEE Transactions on Instrumentation and Measurement.

[11]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[12]  Ana L. N. Fred,et al.  Finger ECG signal for user authentication: Usability and performance , 2013, 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[13]  Nalini C. Iyer,et al.  A survey on Iris recognition system , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[14]  Syed Shahnawazuddin,et al.  Significance of modified empirical mode decomposition for ECG denoising , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[15]  H. S. Fadewar,et al.  Face recognition: A holistic approach review , 2014, 2014 International Conference on Contemporary Computing and Informatics (IC3I).

[16]  Hüseyin Yildirim,et al.  A model for predicting user intention to use wearable IoT devices at the workplace , 2019, J. King Saud Univ. Comput. Inf. Sci..

[17]  Angappa Gunasekaran,et al.  Vision, applications and future challenges of Internet of Things: A bibliometric study of the recent literature , 2016, Ind. Manag. Data Syst..

[18]  Akram Alomainy,et al.  A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning , 2020, IEEE Reviews in Biomedical Engineering.

[19]  Thirimachos Bourlai,et al.  A Novel Approach for ECG-Based Human Identification Using Spectral Correlation and Deep Learning , 2020, IEEE Transactions on Biometrics, Behavior, and Identity Science.

[20]  Xiangyu Zhang,et al.  Signal Quality Assessment and Lightweight QRS Detection for Wearable ECG SmartVest System , 2019, IEEE Internet of Things Journal.

[21]  Christian Jutten,et al.  A Nonlinear Bayesian Filtering Framework for ECG Denoising , 2007, IEEE Transactions on Biomedical Engineering.

[22]  Rama Chellappa,et al.  Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges , 2016, IEEE Signal Processing Magazine.

[23]  Abraham O. Fapojuwo,et al.  A Survey of Enabling Technologies of Low Power and Long Range Machine-to-Machine Communications , 2017, IEEE Communications Surveys & Tutorials.

[24]  Georgios Lampropoulos,et al.  Internet of Things in the Context of Industry 4.0: An Overview , 2019, International Journal of Entrepreneurial Knowledge.

[25]  M. A. Slonim,et al.  The use of simple FIR filters for filtering of ECG signals and a new method for post-filter signal reconstruction , 1993, Proceedings of Computers in Cardiology Conference.

[26]  Imran A. Zualkernan,et al.  Internet of things (IoT) security: Current status, challenges and prospective measures , 2015, 2015 10th International Conference for Internet Technology and Secured Transactions (ICITST).

[27]  Malti Bansal,et al.  IoT & Big Data in Smart Healthcare (ECG Monitoring) , 2019, 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon).

[28]  Jin Wang,et al.  Wearable IoT enabled real-time health monitoring system , 2018, EURASIP Journal on Wireless Communications and Networking.

[29]  Prachi Kamble,et al.  IoT Based Portable ECG Monitoring Device for Smart Healthcare , 2019, 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM).

[30]  Erich Elsen,et al.  Deep Speech: Scaling up end-to-end speech recognition , 2014, ArXiv.

[31]  Fatma A. Omara,et al.  A survey of internet of things technologies and projects for healthcare services , 2018, 2018 International Conference on Innovative Trends in Computer Engineering (ITCE).

[32]  Kazuhiko Hamamoto,et al.  Robustness study of ECG biometric identification in heart rate variability conditions , 2014 .

[33]  R. K. Sharma,et al.  Detection of heart conditions using HRV processor in Matlab simulink , 2017, 2017 International Conference on Trends in Electronics and Informatics (ICEI).

[34]  Kyung-Sup Kwak,et al.  The Internet of Things for Health Care: A Comprehensive Survey , 2015, IEEE Access.

[35]  Irena Jekova,et al.  Classification of supraventricular and ventricular beats by QRS template matching and decision tree , 2014, Computing in Cardiology 2014.

[36]  Hyeuknam Kwon,et al.  Standalone IoT Bioimpedance Device Supporting Real-Time Online Data Access , 2019, IEEE Internet of Things Journal.

[37]  Lars Bauer,et al.  From Cloud Down to Things: An Overview of Machine Learning in Internet of Things , 2019, IEEE Internet of Things Journal.

[38]  Guangyu Xu,et al.  IoT-Assisted ECG Monitoring Framework With Secure Data Transmission for Health Care Applications , 2020, IEEE Access.

[39]  Angappa Gunasekaran,et al.  IoT powered servitization of manufacturing – an exploratory case study , 2017 .

[40]  Jean-Marc Vesin,et al.  Heart rhythm classification using short-term ECG atrial and ventricular activity analysis , 2017, 2017 Computing in Cardiology (CinC).

[41]  Chalee Vorakulpipat,et al.  A Secure Four-factor Attendance System for Smartphone Device , 2020, 2020 22nd International Conference on Advanced Communication Technology (ICACT).

[42]  Christophe Rosenberger,et al.  Challenge-based speaker recognition for mobile authentication , 2012, 2012 BIOSIG - Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG).

[43]  Wei Xiang,et al.  An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare , 2016, Journal of Medical Systems.

[44]  Amitava Das,et al.  Multilingual spoken-password based user authentication in emerging economies using cellular phone networks , 2008, 2008 IEEE Spoken Language Technology Workshop.

[45]  Tal Hassner,et al.  Deep Face Recognition: A Survey , 2018, 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI).

[46]  Maria Lindén,et al.  Evaluation of wavelet based methods in removing motion artifact from ECG signal , 2015 .

[47]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[48]  Gerhard P. Hancke,et al.  A Survey on 5G Networks for the Internet of Things: Communication Technologies and Challenges , 2018, IEEE Access.

[49]  Eric L. Miller,et al.  Nonlocal Means Denoising of ECG Signals , 2012, IEEE Transactions on Biomedical Engineering.

[50]  Gayadhar Pradhan,et al.  Exploring the Non-Local Similarity Present in Variational Mode Functions for Effective ECG Denoising , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[51]  Neetu Sood,et al.  Detection of Heart rate through Speech Using Mel Frequency cepstrum coefficients , 2019, 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI).

[52]  Kostas Marias,et al.  Secure access to patient's health records using SpeechXRays a mutli-channel biometrics platform for user authentication , 2016, EMBC.

[53]  Ratko Magjarević,et al.  Seamless connectivity architecture and methods for IoT and wearable devices , 2020 .

[54]  Hee-Cheol Kim,et al.  A Method of Analyzing ECG to Diagnose Heart Abnormality utilizing SVM and DWT , 2016, J. Multim. Inf. Syst..

[55]  Dimitrios Hatzinakos,et al.  On Evaluating ECG Biometric Systems: Session-Dependence and Body Posture , 2014, IEEE Transactions on Information Forensics and Security.

[56]  Shintaro Izumi,et al.  Noise tolerant QRS detection using template matching with short-term autocorrelation , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[57]  Xuan Zeng,et al.  PulsePrint: Single-arm-ECG biometric human identification using deep learning , 2017, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON).

[58]  Mohamed Elhoseny,et al.  The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems , 2017, Journal of Ambient Intelligence and Humanized Computing.

[59]  Miguel C. Soriano,et al.  A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection , 2019, Front. Phys..

[60]  Md. Shohrab Hossain,et al.  IoT-based Portable ECG Monitoring System for Smart Healthcare , 2019, 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT).

[61]  Jean-Marc Vesin,et al.  Real-time respiratory rate estimation using imaging photoplethysmography inter-beat intervals , 2016, 2016 Computing in Cardiology Conference (CinC).

[62]  Jean-Luc Dugelay,et al.  Recent Advances in Biometric Technology for Mobile Devices , 2018, 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[63]  Javier Preciozzi,et al.  Fingerprint Biometrics From Newborn to Adult: A Study From a National Identity Database System , 2020, IEEE Transactions on Biometrics, Behavior, and Identity Science.

[64]  Farzad Samie,et al.  IoT technologies for embedded computing: A survey , 2016, 2016 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[65]  Hee-Cheol Kim,et al.  Utilizing ECG Waveform Features as New Biometric Authentication Method , 2018 .

[66]  Guy Carrault,et al.  ECG biometric analysis in different physiological recording conditions , 2016, Signal Image Video Process..

[67]  Rabindranath Bera,et al.  A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems , 2020, IEEE Internet of Things Journal.

[68]  Clemens Elster,et al.  Verification of humans using the electrocardiogram , 2007, Pattern Recognit. Lett..

[69]  Shivendra S. Panwar,et al.  Fast Inter-Base Station Ring (FIBR): A New Millimeter Wave Cellular Network Architecture , 2019, IEEE Journal on Selected Areas in Communications.

[70]  A. A. Zaidan,et al.  Finger Vein Biometrics: Taxonomy Analysis, Open Challenges, Future Directions, and Recommended Solution for Decentralised Network Architectures , 2020, IEEE Access.

[71]  Wei Tang,et al.  A Real-Time QRS Detection System With PR/RT Interval and ST Segment Measurements for Wearable ECG Sensors Using Parallel Delta Modulators , 2018, IEEE Transactions on Biomedical Circuits and Systems.

[72]  Kang G. Shin,et al.  Continuous Authentication for Voice Assistants , 2017, MobiCom.

[73]  Chandra Mukherjee,et al.  Analysis of Human Physiological Parameters Using Real-Time HRV Estimation from Acquired ECG Signals , 2019, 2019 International Conference on Data Science and Communication (IconDSC).

[74]  Maryam Mohebbi,et al.  An Adaptive Kalman Filter Bank for ECG Denoising , 2020, IEEE Journal of Biomedical and Health Informatics.

[75]  Gayadhar Pradhan,et al.  A New ECG Denoising Framework Using Generative Adversarial Network , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[76]  N. Sriraam,et al.  Ventricular Ectopic Beat Classification Using KNN Multi-Feature Classifier , 2018, 2018 International Conference on Communication, Computing and Internet of Things (IC3IoT).

[77]  Patrick E. McSharry,et al.  A dynamical model for generating synthetic electrocardiogram signals , 2003, IEEE Transactions on Biomedical Engineering.

[78]  J. Couderc.  The telemetric and holter ECG warehouse initiative (THEW): A data repository for the design, implementation and validation of ECG-related technologies , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[79]  Anil Kumar,et al.  Riemann Liouvelle Fractional Integral Based Empirical Mode Decomposition for ECG Denoising , 2018, IEEE Journal of Biomedical and Health Informatics.

[80]  Cyril Rakovski,et al.  A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients , 2020, Scientific Data.

[81]  M B Shamsollahi,et al.  A model-based Bayesian framework for ECG beat segmentation , 2009, Physiological measurement.

[82]  N.V. Thakor,et al.  Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection , 1991, IEEE Transactions on Biomedical Engineering.

[83]  Roberto Sassi,et al.  ECG biometric recognition: Permanence analysis of QRS signals for 24 hours continuous authentication , 2013, 2013 IEEE International Workshop on Information Forensics and Security (WIFS).

[84]  Navrati Saxena,et al.  Next Generation 5G Wireless Networks: A Comprehensive Survey , 2016, IEEE Communications Surveys & Tutorials.

[85]  Abdel-Ouahab Boudraa,et al.  EMD-Based Signal Filtering , 2007, IEEE Transactions on Instrumentation and Measurement.

[86]  Masatoshi Okutomi,et al.  Inter-Beat Interval Estimation from Facial Video Based on Reliability of BVP Signals , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[87]  David A. Clifton,et al.  Signal-Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring , 2015, IEEE Journal of Biomedical and Health Informatics.

[88]  Ola Pettersson,et al.  ECG analysis: a new approach in human identification , 2001, IEEE Trans. Instrum. Meas..