Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part I: Cardiac Signals
Jazyk angličtina Země Švýcarsko Médium electronic
Typ dokumentu časopisecké články, přehledy
Grantová podpora
CZ.02.1.01/0.0/0.0/16_019/0000867
European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project within the Operational Programme Research, Development and Education
SP2021/32
Ministry of Education of the Czech Republic
PubMed
34372424
PubMed Central
PMC8346990
DOI
10.3390/s21155186
PII: s21155186
Knihovny.cz E-zdroje
- Klíčová slova
- biomedical signals, cardiac signals, electrocardiography, fetal electrocardiography, vectorcardiography,
- MeSH
- algoritmy * MeSH
- elektrokardiografie * MeSH
- lidé MeSH
- počítačové zpracování signálu MeSH
- srdce MeSH
- vlnková analýza MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today's clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.
Zobrazit více v PubMed
Lin Q., Song S., Castro I.D., Jiang H., Konijnenburg M., van Wegberg R., Biswas D., Stanzione S., Sijbers W., van Hoof C., et al. Wearable Multiple Modality Bio-signal Recording and Processing on Chip: A Review. IEEE Sens. J. 2020;21:1108–1123. doi: 10.1109/JSEN.2020.3016115. DOI
Gifta G., Rani D.G.N. Power Approaches for Biosensors based Bio-Medical Devices. ECS J. Solid State Sci. Technol. 2020;9:121005.
Choi H.S. Drowsy driving detection using neural network with backpropagation algorithm implemented by FPGA. Concurr. Comput. Pract. Exp. 2020;32:e5471. doi: 10.1002/cpe.5471. DOI
Hadjileontiadis L.J., Rekanos I.T., Panas S.M. Wiley Encyclopedia of Biomedical Engineering. John Wiley Sons, Inc.; Hoboken, NJ, USA: 2006. Bioacoustic signals.
Kaniusas E. Biomedical Signals and Sensors II. Springer; Berlin/Heidelberg, Germany: 2015. Sensing by acoustic biosignals; pp. 1–90.
Inan O.T., Migeotte P.F., Park K.S., Etemadi M. Ballistocardiography and seismocardiography: A review of recent advances. IEEE J. Biomed. Health Inform. 2015;19:1414–1427. doi: 10.1109/JBHI.2014.2361732. PubMed DOI
Criée C., Sorichter S., Smith H., Kardos P., Merget R., Heise D., Berdel D., Köhler D., Magnussen H., Marek W., et al. Body plethysmography—Its principles and clinical use. Respir. Med. 2011;105:959–971. doi: 10.1016/j.rmed.2011.02.006. PubMed DOI
Fortino G., Giampà V. PPG-based methods for non invasive and continuous blood pressure measurement: An overview and development issues in body sensor networks; Proceedings of the 2010 IEEE International Workshop on Medical Measurements and Applications; Ottawa, ON, Canada. 30 April–1 May 2010; pp. 10–13.
Korostynska O., Arshak K., Gill E., Arshak A. Materials and techniques for in vivo pH monitoring. IEEE Sens. J. 2007;8:20–28. doi: 10.1109/JSEN.2007.912522. DOI
Waddell W.J., Bates R.G. Intracellular pH. Physiol. Rev. 1969;49:285–329. doi: 10.1152/physrev.1969.49.2.285. PubMed DOI
Ring E., McEvoy H., Jung A., Zuber J., Machin G. New standards for devices used for the measurement of human body temperature. J. Med. Eng. Technol. 2010;34:249–253. doi: 10.3109/03091901003663836. PubMed DOI
Sund-Levander M., Grodzinsky E. Assessment of body temperature measurement options. Br. J. Nurs. 2013;22:942–950. doi: 10.12968/bjon.2013.22.16.942. PubMed DOI
Singh Y.N., Singh S.K., Ray A.K. Bioelectrical Signals as Emerging Biometrics: Issues and Challenges. ISRN Signal Process. 2012;2012:1–13. doi: 10.5402/2012/712032. DOI
Shortliffe E.H., Barnett G.O. Biomedical Data: Their Acquisition, Storage, and Use. In: Shortliffe E.H., Cimino J.J., editors. Biomedical Informatics. Springer; New York, NY, USA: 2006. pp. 46–79. DOI
Rangayyan R.M. Biomedical Signal Analysis. 2nd ed. IEEE Press; Piscataway, NJ, USA: John Wiley & Sons, Inc.; Hoboken, NJ, USA: 2015. (IEEE Press Series in Biomedical Engineering).
Bruce E.N. Biomedical Signal Processing and Signal Modeling. Wiley; New York, NY, USA: 2001. (Wiley Series in Telecommunications and Signal Processing).
Clifford G.D., editor. Advanced Methods and Tools for ECG Data Analysis. Engineering in Medicine & Biology, Artech House; Boston, MA, USA: 2006.
Kahankova R., Martinek R., Jaros R., Behbehani K., Matonia A., Jezewski M., Behar J.A. A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography. IEEE Rev. Biomed. Eng. 2020;13:51–73. doi: 10.1109/RBME.2019.2938061. PubMed DOI
Sameni R., Clifford G.D. A Review of Fetal ECG Signal Processing Issues and Promising Directions. Open Pacing Electrophysiol. Ther. J. 2010 doi: 10.2174/1876536X01003010004. PubMed DOI PMC
Macfarlane P.W., Edenbrandt L., Pahlm O. 12-Lead Vectorcardiography. Butterworth Heinemann; Oxford, UK: Boston, MA, USA: 1995.
Vozda M., Cerny M. Methods for Derivation of Orthogonal Leads from 12-Lead Electrocardiogram: A Review. Biomed. Signal Process. Control. 2015;19:23–34. doi: 10.1016/j.bspc.2015.03.001. DOI
Jurcak V., Tsuzuki D., Dan I. 10/20, 10/10, and 10/5 Systems Revisited: Their Validity as Relative Head-Surface-Based Positioning Systems. NeuroImage. 2007;34:1600–1611. doi: 10.1016/j.neuroimage.2006.09.024. PubMed DOI
Ball T., Kern M., Mutschler I., Aertsen A., Schulze-Bonhage A. Signal Quality of Simultaneously Recorded Invasive and Non-Invasive EEG. NeuroImage. 2009;46:708–716. doi: 10.1016/j.neuroimage.2009.02.028. PubMed DOI
Luo J., Gao X., Zhu X., Wang B., Lu N., Wang J. Motor Imagery EEG Classification Based on Ensemble Support Vector Learning. Comput. Methods Programs Biomed. 2020;193:105464. doi: 10.1016/j.cmpb.2020.105464. PubMed DOI
Markand O.N. Clinical Evoked Potentials. Springer International Publishing; Cham, Switzerland: 2020. Basic Techniques of Evoked Potential Recording; pp. 1–23. DOI
Wang D.D., Chen W., Starr P.A., de Hemptinne C. Local Field Potentials and ECoG. In: Pouratian N., Sheth S.A., editors. Stereotactic and Functional Neurosurgery. Springer International Publishing; Cham, Switzerland: 2020. pp. 107–117. DOI
Nakasatp N., Levesque M.F., Barth D.S., Baumgartner C., Rogers R.L., Sutherling W.W. Comparisons of MEG, EEG, and ECoG Source Localization in Neocortical Partial Epilepsy in Humans. Electroencephalogr. Clin. Neurophysiol. 1994;91:171–178. doi: 10.1016/0013-4694(94)90067-1. PubMed DOI
Langmeier J., Krejčířová D., Langmeier M. Vývojová Psychologie s Úvodem do Vývojové Neurofyziologie. H & H; Praha, Czech Republic: 1998.
Abdelghani M.N., Abbas J.J., Jung R. Peripheral Nerve Interface Applications, EMG/ENG. In: Jaeger D., Jung R., editors. Encyclopedia of Computational Neuroscience. Springer; New York, NY, USA: 2014. pp. 1–10. DOI
Rash G.S., Quesada P. [(accessed on 30 July 2021)];Electromyography Fundamentals. 2003 Volume 4 Retrieved February. Available online: http://people.stfx.ca/smackenz/Courses/HK474/Labs/EMG%20Lab/EMGfundamentals.pdf.
Merletti R., Parker P., editors. Electromyography: Physiology, Engineering, and Noninvasive Applications. IEEE/John Wiley & Sons; Hoboken, NJ, USA: 2004. (IEEE Press Series in Biomedical Engineering).
Komorowski D., Pietraszek S., Tkacz E., Provaznik I. The Extraction of the New Components from Electrogastrogram (EGG), Using Both Adaptive Filtering and Electrocardiographic (ECG) Derived Respiration Signal. BioMed. Eng. Online. 2015;14:60. doi: 10.1186/s12938-015-0054-0. PubMed DOI PMC
Riezzo G., Russo F., Indrio F. Electrogastrography in Adults and Children: The Strength, Pitfalls, and Clinical Significance of the Cutaneous Recording of the Gastric Electrical Activity. BioMed Res. Int. 2013;2013:1–14. doi: 10.1155/2013/282757. PubMed DOI PMC
Heide W., Koenig E., Trillenberg P., Kömpf D., Zee D.S. Electrooculography: Technical Standards and Applications. Electroencephalogr. Clin. Neurophysiol. Suppl. 1999;52:223–240. PubMed
López A., Ferrero F., Villar J.R., Postolache O. High-Performance Analog Front-End (AFE) for EOG Systems. Electronics. 2020;9:970. doi: 10.3390/electronics9060970. DOI
Brigell M., Bach M., Barber C., Kawasaki K., Kooijman A. Guidelines for Calibration of Stimulus and Recording Parameters Used in Clinical Electrophysiology of Vision. Doc. Ophthalmol. 1998;95:1–14. doi: 10.1023/A:1001724411607. PubMed DOI
Heckenlively J.R., Arden G.B., editors. Principles and Practice of Clinical Electrophysiology of Vision. 2nd ed. MIT Press; Cambridge, MA, USA: 2006.
Marque C., Duchene J.M.G., Leclercq S., Panczer G.S., Chaumont J. Uterine EHG Processing for Obstetrical Monitorng. IEEE Trans. Biomed. Eng. 1986;BME-33:1182–1187. doi: 10.1109/TBME.1986.325698. PubMed DOI
Lucovnik M., Maner W.L., Chambliss L.R., Blumrick R., Balducci J., Novak-Antolic Z., Garfield R.E. Noninvasive Uterine Electromyography for Prediction of Preterm Delivery. Am. J. Obstet. Gynecol. 2011;204:228.e1–228.e10. doi: 10.1016/j.ajog.2010.09.024. PubMed DOI PMC
Rabotti C., Mischi M., van Laar J.O.E.H., Oei G.S., Bergmans J.W.M. Estimation of Internal Uterine Pressure by Joint Amplitude and Frequency Analysis of Electrohysterographic Signals. Physiol. Meas. 2008;29:829–841. doi: 10.1088/0967-3334/29/7/011. PubMed DOI
Islam M.K., Rastegarnia A., Sanei S. Signal Processing Techniques for Computational Health Informatics. Springer; Berlin/Heidelberg, Germany: 2021. Signal Artifacts and Techniques for Artifacts and Noise Removal; pp. 23–79.
Sweeney K., Ward T., Mcloone S. Artifact Removal in Physiological Signals-Practices and Possibilities. IEEE Trans. Inf. Technol. Biomed. 2012;16:488–500. doi: 10.1109/TITB.2012.2188536. PubMed DOI
Tudosa I., Adochiei N. LMS algorithm derivatives used in real-time filtering of ECG signals: A study case on performance evaluation; Proceedings of the 2012 International Conference and Exposition on Electrical and Power Engineering; Iasi, Romania. 25–27 October 2012; pp. 565–570.
Ren A., Du Z., Li J., Hu F., Yang X., Abbas H. Adaptive Interference Cancellation of ECG Signals. Sensors. 2017;17:942. doi: 10.3390/s17050942. PubMed DOI PMC
Suchetha M., Kumaravel N. Empirical Mode Decomposition-Based Subtraction Techniques for 50 Hz Interference Reduction from Electrocardiogram. IETE J. Res. 2013;59:55. doi: 10.4103/0377-2063.110631. DOI
Watford C. Understanding ECG Filtering. [(accessed on 30 July 2021)];2014 Available online: https://www.rigacci.org/wiki/lib/exe/fetch.php/tecnica/misc/ecg90a/understanding-ecg-filtering.pdf.
Dixon A.M., Allstot E.G., Gangopadhyay D., Allstot D.J. Compressed sensing system considerations for ECG and EMG wireless biosensors. IEEE Trans. Biomed. Circuits Syst. 2012;6:156–166. doi: 10.1109/TBCAS.2012.2193668. PubMed DOI
Allstot E.G., Chen A.Y., Dixon A.M., Gangopadhyay D., Allstot D.J. Compressive sampling of ECG bio-signals: Quantization noise and sparsity considerations; Proceedings of the 2010 Biomedical Circuits and Systems Conference (BioCAS); Paphos, Cyprus. 3–5 November 2010; pp. 41–44.
Williams G. An Oversampled Analog to Digital Converter for Acquiring Neural Signals. [(accessed on 30 July 2021)];2009 Available online: https://openscholarship.wustl.edu/etd/462/
Zaidi A., Athley F., Medbo J., Gustavsson U., Durisi G., Chen X. 5G Physical Layer: Principles, Models and Technology Components. Academic Press; Cambridge, MA, USA: 2018.
Bovik A.C. The Essential Guide to Image Processing. Academic Press; Cambridge, MA, USA: 2009.
Rouphael T.J. Wireless Receiver Architectures and Design: Antennas, RF, Synthesizers, Mixed Signal, and Digital Signal Processing. Academic Press; Cambridge, MA, USA: 2014.
Daniel Ţ.D., Neagu M. Compendium of New Techniques in Harmonic Analysis. IntechOpen; London, UK: 2018. Cancelling harmonic power line interference in biopotentials; p. 19.
Zivanovic M., González-Izal M. Simultaneous powerline interference and baseline wander removal from ECG and EMG signals by sinusoidal modeling. Med. Eng. Phys. 2013;35:1431–1441. doi: 10.1016/j.medengphy.2013.03.015. PubMed DOI
Chowdhury R., Reaz M., Ali M., Bakar A., Chellappan K., Chang T. Surface Electromyography Signal Processing and Classification Techniques. Sensors. 2013;13:12431–12466. doi: 10.3390/s130912431. PubMed DOI PMC
Brown E.A., Ross J.D., Blum R.A., Nam Y., Wheeler B.C., DeWeerth S.P. Stimulus-artifact elimination in a multi-electrode system. IEEE Trans. Biomed. Circuits Syst. 2008;2:10–21. doi: 10.1109/TBCAS.2008.918285. PubMed DOI
Wilson F.N., Macleod A., Barker P.S. The Potential Variations Produced by the Heart Beat at the Apices of Einthoven’s Triangle. Am. Heart J. 1931;7:207–211. doi: 10.1016/S0002-8703(31)90411-0. DOI
Herrmann G.R., Wilson F.N. Ventricular Hypertrophy. A Comparison of Electrocardiographic and Postmortem Observations. Heart. 1922;9:1921–1922.
Penhaker M., Augustynek M. Zdravotnické Elektrické Přístroje 1. VSB—Technical University of Ostrava; Ostrava, Czech Republic: 2013.
Nyni K., Vincent L.K., Varghese L., Liya V., Johny A.N., Yesudas C. Wireless Health Monitoring System for ECG, EMG and EEG Detecting; Proceedings of the 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS); Coimbatore, India. 17–18 March 2017; pp. 1–5. DOI
Vogel B., Claessen B.E., Arnold S.V., Chan D., Cohen D.J., Giannitsis E., Gibson C.M., Goto S., Katus H.A., Kerneis M., et al. ST-segment elevation myocardial infarction. Nat. Rev. Dis. Prim. 2019;5:1–20. doi: 10.1038/s41572-019-0090-3. PubMed DOI
Ribas Mercau N.A. Characterization and Handling of Disturbances within Electrocardiographic Recordings of Different Origin. [(accessed on 30 July 2021)];2014 Available online: https://upcommons.upc.edu/handle/2099.1/21684.
Jaros R., Martinek R., Kahankova R. Non-Adaptive Methods for Fetal ECG Signal Processing: A Review and Appraisal. Sensors. 2018;18:3648. doi: 10.3390/s18113648. PubMed DOI PMC
Lu J., Luo J., Xie Z., Xie K., Cheng Y., Xie S. Dual temporal convolutional network for single-lead fibrillation waveform extraction. Neural Comput. Appl. 2021:1–12. doi: 10.1007/s00521-021-06148-7. DOI
Ay A.N., Yildiz M.Z., Boru B. NI LabVIEW Kullanarak EKG Sinyallerinin Gerçek Zamanlı Özellik Çıkarımı. SAÜ Fen Bilim. Enstitüsü Derg. 2017;21:576–583. doi: 10.16984/saufenbilder.287418. DOI
Vojtech L., Bortel R., Neruda M., Kozak M. Wearable Textile Electrodes for ECG Measurement. Adv. Electr. Electron. Eng. 2013;11:410–414. doi: 10.15598/aeee.v11i5.889. DOI
Jagtap S.K., Uplane M.D. The Impact of Digital Filtering to ECG Analysis: Butterworth Filter Application; Proceedings of the 2012 International Conference on Communication, Information & Computing Technology (ICCICT); Mumbai, India. 19–20 October 2012; pp. 1–6. DOI
Sun C.Y., Lee S.Y. A fifth-order butterworth OTA-C LPF with multiple-output differential-input OTA for ECG applications. IEEE Trans. Circuits Syst. II Express Briefs. 2017;65:421–425. doi: 10.1109/TCSII.2017.2695366. DOI
Singhal A., Singh P., Fatimah B., Pachori R.B. An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique. Biomed. Signal Process. Control. 2020;57:101741. doi: 10.1016/j.bspc.2019.101741. DOI
Rahman M.Z.U., Shaik R.A., Rama Koti Reddy D. Efficient Sign Based Normalized Adaptive Filtering Techniques for Cancelation of Artifacts in ECG Signals: Application to Wireless Biotelemetry. Signal Process. 2011;91:225–239. doi: 10.1016/j.sigpro.2010.07.002. DOI
Zhang Z., Silva I., Wu D., Zheng J., Wu H., Wang W. Adaptive Motion Artefact Reduction in Respiration and ECG Signals for Wearable Healthcare Monitoring Systems. Med. Biol. Eng. Comput. 2014;52:1019–1030. doi: 10.1007/s11517-014-1201-7. PubMed DOI
Jobert M., Tismer C., Poiseau E., Schulz H. Wavelets—A New Tool in Sleep Biosignal Analysis. J. Sleep Res. 1994;3:223–232. doi: 10.1111/j.1365-2869.1994.tb00135.x. PubMed DOI
Addison P., Walker J., Guido R. Time–Frequency Analysis of Biosignals. IEEE Eng. Med. Biol. Mag. 2009;28:14–29. doi: 10.1109/MEMB.2009.934244. PubMed DOI
Amri M.F., Rizqyawan M.I., Turnip A. ECG Signal Processing Using Offline-Wavelet Transform Method Based on ECG-IoT Device; Proceedings of the 2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE); Semarang, Indonesia. 19–20 October 2016; pp. 1–6. DOI
Kumar A., Komaragiri R., Kumar M. Design of Wavelet Transform Based Electrocardiogram Monitoring System. ISA Trans. 2018;80:381–398. doi: 10.1016/j.isatra.2018.08.003. PubMed DOI
Kumar A., Komaragiri R., Kumar M. Heart Rate Monitoring and Therapeutic Devices: A Wavelet Transform Based Approach for the Modeling and Classification of Congestive Heart Failure. ISA Trans. 2018;79:239–250. doi: 10.1016/j.isatra.2018.05.003. PubMed DOI
Subramanian B., Ramasamy A. Investigation on the Compression of Electrocardiogram Signals Using Dual Tree Complex Wavelet Transform. IETE J. Res. 2017;63:392–402. doi: 10.1080/03772063.2016.1275988. DOI
Sudarshan V.K., Acharya U., Oh S.L., Adam M., Tan J.H., Chua C.K., Chua K.P., Tan R.S. Automated Diagnosis of Congestive Heart Failure Using Dual Tree Complex Wavelet Transform and Statistical Features Extracted from 2 s of ECG Signals. Comput. Biol. Med. 2017;83:48–58. doi: 10.1016/j.compbiomed.2017.01.019. PubMed DOI
Sharma T., Sharma K.K. QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform. IETE J. Res. 2016;62:885–892. doi: 10.1080/03772063.2016.1221744. DOI
Flandrin P., Rilling G., Goncalves P. Empirical Mode Decomposition as a Filter Bank. IEEE Signal Process. Lett. 2004;11:112–114. doi: 10.1109/LSP.2003.821662. DOI
Blanco-Velasco M., Weng B., Barner K.E. ECG Signal Denoising and Baseline Wander Correction Based on the Empirical Mode Decomposition. Comput. Biol. Med. 2008;38:1–13. doi: 10.1016/j.compbiomed.2007.06.003. PubMed DOI
Pal S., Mitra M. Empirical Mode Decomposition Based ECG Enhancement and QRS Detection. Comput. Biol. Med. 2012;42:83–92. doi: 10.1016/j.compbiomed.2011.10.012. PubMed DOI
Xiong P., Wang H., Liu M., Liu X. Denoising autoencoder for eletrocardiogram signal enhancement. J. Med. Imaging Health Inform. 2015;5:1804–1810. doi: 10.1166/jmihi.2015.1649. DOI
Chiang H.T., Hsieh Y.Y., Fu S.W., Hung K.H., Tsao Y., Chien S.Y. Noise reduction in ECG signals using fully convolutional denoising autoencoders. IEEE Access. 2019;7:60806–60813. doi: 10.1109/ACCESS.2019.2912036. DOI
Antczak K. Deep recurrent neural networks for ECG signal denoising. arXiv. 20181807.11551
Arsene C.T., Hankins R., Yin H. Deep learning models for denoising ECG signals; Proceedings of the 2019 27th European Signal Processing Conference (EUSIPCO); A Coruna, Spain. 2–6 September 2019; pp. 1–5.
Fatimah B., Singh P., Singhal A., Pramanick D., Pranav S., Pachori R.B. Efficient detection of myocardial infarction from single lead ECG signal. Biomed. Signal Process. Control. 2021;68:102678. doi: 10.1016/j.bspc.2021.102678. DOI
Yang X., Hu Q., Li S. Electrocardiogram classification of lead convolutional neural network based on fuzzy algorithm. J. Intell. Fuzzy Syst. 2020;38:3539–3548. doi: 10.3233/JIFS-179576. DOI
Mousavi S., Afghah F., Khadem F., Acharya U.R. ECG Language processing (ELP): A new technique to analyze ECG signals. Comput. Methods Programs Biomed. 2021;202:105959. doi: 10.1016/j.cmpb.2021.105959. PubMed DOI PMC
Rodrigues J., Belo D., Gamboa H. Noise Detection on ECG Based on Agglomerative Clustering of Morphological Features. Comput. Biol. Med. 2017;87:322–334. doi: 10.1016/j.compbiomed.2017.06.009. PubMed DOI
Kumar S., Panigrahy D., Sahu P. Denoising of Electrocardiogram (ECG) Signal by Using Empirical Mode Decomposition (EMD) with Non-Local Mean (NLM) Technique. Biocybern. Biomed. Eng. 2018;38:297–312. doi: 10.1016/j.bbe.2018.01.005. DOI
Rakshit M., Das S. An Efficient ECG Denoising Methodology Using Empirical Mode Decomposition and Adaptive Switching Mean Filter. Biomed. Signal Process. Control. 2018;40:140–148. doi: 10.1016/j.bspc.2017.09.020. DOI
Lu L., Yan J., de Silva C.W. Feature Selection for ECG Signal Processing Using Improved Genetic Algorithm and Empirical Mode Decomposition. Measurement. 2016;94:372–381. doi: 10.1016/j.measurement.2016.07.043. DOI
Xiong P., Wang H., Liu M., Zhou S., Hou Z., Liu X. ECG signal enhancement based on improved denoising auto-encoder. Eng. Appl. Artif. Intell. 2016;52:194–202. doi: 10.1016/j.engappai.2016.02.015. DOI
Rajankar S.O., Talbar S.N. An optimum ECG denoising with wavelet neural network; Proceedings of the 2015 International Conference on Pervasive Computing (ICPC); Pune, India. 8–10 January 2015; pp. 1–4.
Poungponsri S., Yu X.H. An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks. Neurocomputing. 2013;117:206–213. doi: 10.1016/j.neucom.2013.02.010. DOI
Liang Y., Yin S., Tang Q., Zheng Z., Elgendi M., Chen Z. Deep learning algorithm classifies heartbeat events based on electrocardiogram signals. Front. Physiol. 2020;11:569050. doi: 10.3389/fphys.2020.569050. PubMed DOI PMC
Schmitt O.H. Symposium on Electrocardiography and Vectorcardiography: The Present Status of Vectorcardiography. AMA Arch. Intern. Med. 1955;96:574. doi: 10.1001/archinte.1955.00250160016002. PubMed DOI
Abeysekera R. Some Physiologically Meaningful Features Obtained from the Vectorcardiography. IEEE Eng. Med. Biol. Mag. 1991;10:58–63. doi: 10.1109/51.84192. PubMed DOI
Rubel P., Benhadid I., Fayn J. Quantitative Assessment of Eight Different Methods for Synthesizing Frank VCGs from Simultaneously Recorded Standard ECG Leads. J. Electrocardiol. 1991;24:197–202. doi: 10.1016/S0022-0736(10)80045-7. PubMed DOI
Levkov C.L. Orthogonal Electrocardiogram Derived from the Limb and Chest Electrodes of the Conventional 12-Lead System. Med. Biol. Eng. Comput. 1987;25:155–164. doi: 10.1007/BF02442844. PubMed DOI
Hyttinen J., Viik J., Eskola H., Malmivuo J. Computers in Cardiology 1995. IEEE; Vienna, Austria: 1995. Optimization and Comparison of Derived Frank VECG Lead Systems Employing an Accurate Thorax Model; pp. 385–388. DOI
McFee R., Parungao A. An Orthogonal Lead System for Clinical Electrocardiography. Am. Heart J. 1961;62:93–100. doi: 10.1016/0002-8703(61)90488-4. DOI
Malmivuo J. The SVEC III Vectorcardiographic Lead System. IEEE Eng. Med. Biol. Mag. 2004;23:47–51. doi: 10.1109/MEMB.2004.1378633. PubMed DOI
Fischmann E.J., Elliott B.J. Experimental Comparison of “Parallel Grid Leads” with Simple Bipolar, and the SVEC-III, Frank, and McFee-Parungao Systems. I. Sagittal Leads. Am. Heart J. 1964;67:792–803. doi: 10.1016/0002-8703(64)90180-2. PubMed DOI
Malmivuo J., Plonsey R. Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. Oxford University Press; New York, NY, USA: 1995.
Kors J.A., Van Herpen G., Sittig A.C., Van Bemmel J.H. Reconstruction of the Frank Vectorcardiogram from Standard Electrocardiographic Leads: Diagnostic Comparison of Different Methods. Eur. Heart J. 1990;11:1083–1092. doi: 10.1093/oxfordjournals.eurheartj.a059647. PubMed DOI
Dawson D., Yang H., Malshe M., Bukkapatnam S.T., Benjamin B., Komanduri R. Linear Affine Transformations between 3-Lead (Frank XYZ Leads) Vectorcardiogram and 12-Lead Electrocardiogram Signals. J. Electrocardiol. 2009;42:622–630. doi: 10.1016/j.jelectrocard.2009.05.007. PubMed DOI
Lingman M., Hartford M., Karlsson T., Herlitz J., Rubulis A., Caidahl K., Bergfeldt L. Transient Repolarization Alterations Dominate the Initial Phase of an Acute Anterior Infarction—A Vectorcardiography Study. J. Electrocardiol. 2014;47:478–485. doi: 10.1016/j.jelectrocard.2014.04.017. PubMed DOI
Sederholm M. The Origin of Monitoring of Acute Myocardial Infarction with Continuous Vectorcardiography. J. Electrocardiol. 2014;47:418–424. doi: 10.1016/j.jelectrocard.2014.04.002. PubMed DOI
Cortez D., Bos J.M., Ackerman M.J. Vectorcardiography Identifies Patients with Electrocardiographically Concealed Long QT Syndrome. Heart Rhythm. 2017;14:894–899. doi: 10.1016/j.hrthm.2017.03.003. PubMed DOI
Correa R., Arini P.D., Valentinuzzi M.E., Laciar E. Novel Set of Vectorcardiographic Parameters for the Identification of Ischemic Patients. Med. Eng. Phys. 2013;35:16–22. doi: 10.1016/j.medengphy.2012.03.005. PubMed DOI
van Bemmel J.H., Kors J.A., van Herpen G. Combination of Diagnostic Classifications from ECG and VCG Computer Interpretations. J. Electrocardiol. 1992;25:126–130. doi: 10.1016/0022-0736(92)90078-E. PubMed DOI
Edenbrandt L., Pahlm O. Vectorcardiogram Synthesized from a 12-Lead ECG: Superiority of the Inverse Dower Matrix. J. Electrocardiol. 1988;21:361–367. doi: 10.1016/0022-0736(88)90113-6. PubMed DOI
Schreck D.M., Fishberg R.D. Derivation of the 12-Lead Electrocardiogram and 3-Lead Vectorcardiogram. Am. J. Emerg. Med. 2013;31:1183–1190. doi: 10.1016/j.ajem.2013.04.037. PubMed DOI
Burger H.C., Van Milaan J.B., Den Boer W. Comparison of Different Systems of Vectorcardiography. Heart. 1952;14:401–405. doi: 10.1136/hrt.14.3.401. PubMed DOI PMC
Guillem M.S., Climent A.M., Bollmann A., Husser D., Millet J., Castells F. Limitations of Dower’s Inverse Transform for the Study of Atrial Loops During Atrial Fibrillation: Limitations of Dower’s Inverse Transform for AF. Pacing Clin. Electrophysiol. 2009;32:972–980. doi: 10.1111/j.1540-8159.2009.02426.x. PubMed DOI
Medhat M., Abdelraheem T. Human Identification Using Main Loop of the VCG Contour; Proceedings of the 8th Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI) Association of Thailand—Conference 2011; Khon Kaen, Thailand. 17–19 May 2011; pp. 1011–1014. DOI
Ge D. Detecting Myocardial Infraction Using VCG Leads; Proceedings of the 2008 2nd International Conference on Bioinformatics and Biomedical Engineering; Shanghai, China. 16–18 May 2008; pp. 2217–2220. DOI
Guillem M.S., Sahakian A.V., Swiryn S. Derivation of Orthogonal Leads from the 12-Lead ECG. Accuracy of a Single Transform for the Derivation of Atrial and Ventricular Waves; Proceedings of the 2006 Computers in Cardiology; Valencia, Spain. 17–20 September 2006; pp. 249–252.
Correa R., Laciar E., Arini P., Jane R. Analysis of QRS Loop Changes in the Beat-to-Beat Vectocardiogram of Ischemic Patients Undergoing PTCA; Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Minneapolis, MN, USA. 3–6 September 2009; pp. 1750–1753. PubMed DOI
Correa R., Arini P., Correa L., Valentinuzzi M., Laciar E. Analysis of Vectorcardiographic Dynamic Changes in Patients with Acute Myocardial Ischemia. J. Phys. Conf. Ser. 2013;477:012032. doi: 10.1088/1742-6596/477/1/012032. DOI
Yang H. Multiscale Recurrence Quantification Analysis of Spatial Cardiac Vectorcardiogram Signals. IEEE Trans. Biomed. Eng. 2011;58:339–347. doi: 10.1109/TBME.2010.2063704. PubMed DOI
Tripathy R.K., Dandapat S. Detection of Myocardial Infarction from Vectorcardiogram Using Relevance Vector Machine. Signal Image Video Process. 2017;11:1139–1146. doi: 10.1007/s11760-017-1068-9. DOI
Dıker A., Avci E., Cömert Z., Avci D., Kaçar E., Serhatlioğlu İ. Classification of ECG signal by using machine learning methods; Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU); Izmir, Turkey. 2–5 May 2018; pp. 1–4.
Gustafson D.E., Willsky A.S., Wang J.Y., Lancaster M.C., Triebwasser J.H. ECG/VCG Rhythm Diagnosis Using Statistical Signal Analysis-I. Identification of Persistent Rhythms. IEEE Trans. Biomed. Eng. 1978;BME-25:344–353. doi: 10.1109/TBME.1978.326260. PubMed DOI
Prabhakararao E., Dandapat S. Automated Detection of Posterior Myocardial Infarction From VCG Signals Using Stationary Wavelet Transform Based Features. IEEE Sens. Lett. 2020;4:1–4. doi: 10.1109/LSENS.2020.2992760. DOI
Prabhakararao E., Dandapat S. A Weighted SVM Based Approach for Automatic Detection of Posterior Myocardial Infarction Using VCG Signals; Proceedings of the 2019 National Conference on Communications (NCC); Bangalore, India. 20–23 February 2019; Bangalore, India: IEEE; 2019. pp. 1–6. DOI
Vozda M. Ph.D. Thesis. VSB-Technical University of Ostrava; Ostrava, Czech Republic: 2016. Spatio-Temporal Analysis of Vectorcardiographic.
Karsikas M. Ph.D. Thesis. Acta Universitatis Oululensis, University of Oulu; Oulu, Finland: 2011. New Methods for Vectorcardiographic Signal Processing.
Lipponen J.A., Tarvainen M.P., Laitinen T., Lyyra-Laitinen T., Karjalainen P.A. A Principal Component Regression Approach for Estimation of Ventricular Repolarization Characteristics. IEEE Trans. Biomed. Eng. 2010;57:1062–1069. doi: 10.1109/TBME.2009.2037492. PubMed DOI
Lipponen J.A., Gladwell V.F., Kinnunen H., Karjalainen P.A., Tarvainen M.P. The Correlation of Vectorcardiographic Changes to Blood Lactate Concentration during an Exercise Test. Biomed. Signal Process. Control. 2013;8:491–499. doi: 10.1016/j.bspc.2013.05.002. DOI
Kahankova R., Jaros R., Martinek R., Jezewski J., Wen H., Jezewski M., Kawala-Janik A. Non-Adaptive Methods of Fetal ECG Signal Processing. Adv. Electr. Electron. Eng. 2017;15:476–490. doi: 10.15598/aeee.v15i3.2196. DOI
Jagannath D., Selvakumar A.I. Issues and Research on Foetal Electrocardiogram Signal Elicitation. Biomed. Signal Process. Control. 2014;10:224–244. doi: 10.1016/j.bspc.2013.11.001. DOI
Clifford G.D., Silva I., Behar J., Moody G.B. Non-Invasive Fetal ECG Analysis. Physiol. Meas. 2014;35:1521–1536. doi: 10.1088/0967-3334/35/8/1521. PubMed DOI PMC
Jezewski J., Matonia A., Kupka T., Roj D., Czabanski R. Determination of Fetal Heart Rate from Abdominal Signals: Evaluation of Beat-to-Beat Accuracy in Relation to the Direct Fetal Electrocardiogram. Biomed. Tech. Eng. 2012;57 doi: 10.1515/bmt-2011-0130. PubMed DOI
Kotas M., Jezewski J., Matonia A., Kupka T. Towards Noise Immune Detection of Fetal QRS Complexes. Comput. Methods Programs Biomed. 2010;97:241–256. doi: 10.1016/j.cmpb.2009.09.005. PubMed DOI
Jezewski J., Wrobel J., Horoba K. Comparison of Doppler Ultrasound and Direct Electrocardiography Acquisition Techniques for Quantification of Fetal Heart Rate Variability. IEEE Trans. Biomed. Eng. 2006;53:855–864. doi: 10.1109/TBME.2005.863945. PubMed DOI
Cohen W.R., Hayes-Gill B. Influence of Maternal Body Mass Index on Accuracy and Reliability of External Fetal Monitoring Techniques. Acta Obstet. Gynecol. Scand. 2014;93:590–595. doi: 10.1111/aogs.12387. PubMed DOI
Sänger N., Hayes-Gill B., Schiermeier S., Hatzmann W., Yuan J., Herrmann E., Louwen F., Reinhard J. Prenatal Foetal Non-invasive ECG instead of Doppler CTG—A Better Alternative? Geburtshilfe Und Frauenheilkd. 2012;72:630–633. doi: 10.1055/s-0032-1315012. PubMed DOI PMC
Jaros R., Martinek R., Kahankova R., Koziorek J. Novel Hybrid Extraction Systems for Fetal Heart Rate Variability Monitoring Based on Non-Invasive Fetal Electrocardiogram. IEEE Access. 2019;7:131758–131784. doi: 10.1109/ACCESS.2019.2933717. DOI
Liu G., Luan Y. An Adaptive Integrated Algorithm for Noninvasive Fetal ECG Separation and Noise Reduction Based on ICA-EEMD-WS. Med. Biol. Eng. Comput. 2015;53:1113–1127. doi: 10.1007/s11517-015-1389-1. PubMed DOI
Gupta A., Srivastava M.C., Khandelwal V., Gupta A. A Novel Approach to Fetal ECG Extraction and Enhancement Using Blind Source Separation (BSS-ICA) and Adaptive Fetal ECG Enhancer (AFE); Proceedings of the 2007 6th International Conference on Information, Communications & Signal Processing; Singapore. 10–13 December 2007; pp. 1–4. DOI
Martín-Clemente R., Camargo-Olivares J.L., Hornillo-Mellado S., Elena M., Román I. Fast Technique for Noninvasive Fetal ECG Extraction. IEEE Trans. Biomed. Eng. 2011;58:227–230. doi: 10.1109/TBME.2010.2059703. PubMed DOI
Gao P., Chang E.C., Wyse L. Blind Separation of Fetal ECG from Single Mixture Using SVD and ICA; Proceedings of the Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia, Proceedings of the 2003 Joint; Singapore. 15–18 December 2003; pp. 1418–1422. DOI
Jang J.S. ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. Syst. Man, Cybern. 1993;23:665–685. doi: 10.1109/21.256541. DOI
Swarnalath R., Prasad D. Maternal ECG Cancellation in Abdominal Signal Using ANFIS and Wavelets. J. Appl. Sci. 2010;10:868–877. doi: 10.3923/jas.2010.868.877. DOI
Assaleh K. Adaptive Neuro-Fuzzy Inference Systems for Extracting Fetal Electrocardiogram; Proceedings of the 2006 IEEE International Symposium on Signal Processing and Information Technology; Vancouver, BC, Canada. 27–30 August 2006; pp. 122–126. DOI
Jothi S., Prabha K. Fetal Electrocardiogram Extraction Using Adaptive Neuro-Fuzzy Inference Systems and Undecimated Wavelet Transform. IETE J. Res. 2012;58:469. doi: 10.4103/0377-2063.106753. DOI
Camps G., Martinez M., Soria E. Fetal ECG Extraction Using an FIR Neural Network; Proceedings of the Computers in Cardiology 2001, Volume 28 (Cat. No. 01CH37287); Rotterdam, The Netherlands. 23–26 September 2001; pp. 249–252. DOI
Sehamby R., Singh B. Noise Cancellation Using Adaptive Filtering in ECG Signals: Application to Biotelemetry. Int. J. Bio-Sci. Bio-Technol. 2016;8:237–244. doi: 10.14257/ijbsbt.2016.8.2.22. DOI
Zeng Y., Liu S., Zhang J. Extraction of Fetal ECG Signal via Adaptive Noise Cancellation Approach; Proceedings of the 2008 2nd International Conference on Bioinformatics and Biomedical Engineering; Shanghai, China. 16–18 May 2008; pp. 2270–2273. DOI
Liu S.J., Liu D.L., Zhang J.Q., Zeng Y.J. Extraction of Fetal Electrocardiogram Using Recursive Least Squares and Normalized Least Mean Squares Algorithms; Proceedings of the 2011 3rd International Conference on Advanced Computer Control; Harbin, China. 18–20 January 2011; pp. 333–336. DOI
Kahankova R., Martinek R., Bilik P. Fetal ECG Extraction from Abdominal ECG Using RLS Based Adaptive Algorithms; Proceedings of the 2017 18th International Carpathian Control Conference (ICCC); Sinaia, Romania. 28–31 May 2017; pp. 337–342. DOI
Karvounis E., Papaloukas C., Fotiadis D., Michalis L. Computers in Cardiology 2004. IEEE; Chicago, IL, USA: 2004. Fetal Heart Rate Extraction from Composite Maternal ECG Using Complex Continuous Wavelet Transform; pp. 737–740. DOI
Hassanpour H., Parsaei A. Fetal ECG Extraction Using Wavelet Transform; Proceedings of the 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA’06); Sydney, NSW, Australia. 28 November–1 December 2006; p. 179. DOI
Desai K.D., Sankhe M.S. A Real-Time Fetal ECG Feature Extraction Using Multiscale Discrete Wavelet Transform; Proceedings of the 2012 5th International Conference on BioMedical Engineering and Informatics; Chongqing, China. 16–18 October 2012; pp. 407–412. DOI
Agostinelli A., Sbrollini A., Burattini L., Fioretti S., Di Nardo F., Burattini L. Noninvasive Fetal Electrocardiography Part II: Segmented-Beat Modulation Method for Signal Denoising. Open Biomed. Eng. J. 2017;11:25–35. doi: 10.2174/1874120701711010025. PubMed DOI PMC
Lipponen J.A., Tarvainen M.P. Advanced Maternal ECG Removal and Noise Reduction for Application of Fetal QRS Detection; Proceedings of the Computing in Cardiology 2013; Zaragoza, Spain. 22–25 September 2013; pp. 161–164.
Matonia A., Jezewski J., Horoba K., Gacek A., Labaj P. The Maternal ECG Suppression Algorithm for Efficient Extraction of the Fetal ECG from Abdominal Signal; Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society; New York, NY, USA. 30 August–3 September 2006; pp. 3106–3109. PubMed DOI
Goldberger A.L., Amaral L.A.N., Glass L., Hausdorff J.M., Ivanov P.C., Mark R.G., Mietus J.E., Moody G.B., Peng C.K., Stanley H.E. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation. 2000;101 doi: 10.1161/01.CIR.101.23.e215. PubMed DOI
Silva I., Behar J., Sameni R., Zhu T., Oster J., Clifford G.D., Moody G.B. Noninvasive Fetal ECG: The PhysioNet/Computing in Cardiology Challenge 2013; Proceedings of the Computing in Cardiology 2013; Zaragoza, Spain. 22–25 September 2013; pp. 149–152. PubMed PMC
Ghosh P.K., Poonia D. Comparison of Some EMD Based Technique for Baseline Wander Correction in Fetal ECG Signal. Int. J. Comput. Appl. 2015;116:48–52.
Ghobadi Azbari P., Abdolghaffar M., Mohaqeqi S., Pooyan M., Ahmadian A., Ghanbarzadeh Gashti N. A Novel Approach to the Extraction of Fetal Electrocardiogram Based on Empirical Mode Decomposition and Correlation Analysis. Australas. Phys. Eng. Sci. Med. 2017;40:565–574. doi: 10.1007/s13246-017-0560-4. PubMed DOI
Manorost P., Theera-Umpon N., Auephanwiriyakul S. Fetal Electrocardiogram Extraction by Independent Component Analysis; Proceedings of the 2017 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE); Penang, Malaysia. 24–26 November 2017; pp. 220–225. DOI
John S.T., Goyal M., Singh S., Mukherjee A. Ambulatory Fetal Heart Monitoring with QRS Detection Employing Independent Component Analysis; Proceedings of the 2017 IEEE Region 10 Symposium (TENSYMP); Cochin, India. 14–16 July 2017; pp. 1–4. DOI
Kotas M., Giraldo J., Contreras-Ortiz S.H., Lasprilla G.I.B. Fetal ECG Extraction Using Independent Component Analysis by Jade Approach. In: Brieva J., García J.D., Lepore N., Romero E., editors. Proceedings of the 13th International Conference on Medical Information Processing and Analysis; San Andres Island, Colombia. 5–7 October 2017; San Andres Island, Colombia: SPIE; 2017. p. 55. DOI
Bacharakis E., Nandi A.K., Zarzoso V. Foetal ECG Extraction Using Blind Source Separation Methods; Proceedings of the 1996 8th European Signal Processing Conference (EUSIPCO 1996); Trieste, Italy. 10–13 September 1996; pp. 1–4.
Petrolis R., Krisciukaitis A. Multi Stage Principal Component Analysis Based Method for Detection of Fetal Heart Beats in Abdominal ECGs; Proceedings of the Computing in Cardiology 2013; Zaragoza, Spain. 22–25 September 2013; pp. 301–304.
Raj C.G., Harsha V.S., Gowthami B.S., Sunitha R. Virtual Instrumentation Based Fetal ECG Extraction. Procedia Comput. Sci. 2015;70:289–295. doi: 10.1016/j.procs.2015.10.093. DOI
Alvarez L.O.S., Gonzalez A., Millet J. Hybrid BSS Techniques for Fetal ECG Extraction Using a Semi-Synthetic Database; Proceedings of the 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA); Bogota, Colombia. 2–4 September 2015; pp. 1–6. DOI
Billeci L., Varanini M. A Combined Independent Source Separation and Quality Index Optimization Method for Fetal ECG Extraction from Abdominal Maternal Leads. Sensors. 2017;17:1135. doi: 10.3390/s17051135. PubMed DOI PMC
Panigrahy D., Sahu P.K. Extraction of Fetal Electrocardiogram (ECG) by Extended State Kalman Filtering and Adaptive Neuro-Fuzzy Inference System (ANFIS) Based on Single Channel Abdominal Recording. Sadhana. 2015;40:1091–1104. doi: 10.1007/s12046-015-0381-7. DOI
A Review of Patient Bed Sensors for Monitoring of Vital Signs