Novel Biometric Approach Based on Diaphragmatic Respiratory Movements Using Single-Lead EMG Signals

References

• J. S. Kim, M. G. Kim, and S. B. Pan, “Two-step biometrics using electromyogram signal based on convolutional neural network-long short-term memory networks,” Appl. Sci., Vol. 11, 2021. DOI:10.3390/app11156824.
   Google Scholar
• A. J. Prakash, “Capsule network for the identification of individuals using quantized ECG signal images,” IEEE Sens. Lett., Vol. 6, pp. 8–11, 2022. DOI:10.1109/LSENS.2022.3195174.
   Google Scholar
• K. K. Patro, A. Jaya Prakash, M. Jayamanmadha Rao, and P. Rajesh Kumar, “An efficient optimized feature selection with machine learning approach for ECG biometric recognition,” IETE J. Res., Vol. 68, pp. 2743–2754, 2022. DOI:10.1080/03772063.2020.1725663.
   Web of Science ®Google Scholar
• A. Reşit Kavsaoglu, K. Polat, and M. Recep Bozkurt, “A novel feature ranking algorithm for biometric recognition with PPG signals,” Comput. Biol. Med., Vol. 49, pp. 1–14, 2014. DOI:10.1016/j.compbiomed.2014.03.005.
   PubMed Web of Science ®Google Scholar
• S. Prabhakar, and S. Pankanti, “Biometric recognition: security and privacy concerns B,” IEEE Secur. Priv., Vol. 1, pp. 33–42, 2003.
   Google Scholar
• X. Jiang, K. Xu, X. Liu, D. Liu, C. Dai, and W. Chen. “High-density surface electromyogram-based biometrics for personal identification,” in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS. 2020-July, 2020, pp. 728–731. DOI:10.1109/EMBC44109.2020.9175370.
   Google Scholar
• L. Lu, J. Mao, W. Wang, G. Ding, and Z. Zhang, “A study of personal recognition method based on EMG signal,” IEEE Trans. Biomed. Circ. Syst., Vol. 14, pp. 681–691, 2020. DOI:10.1109/TBCAS.2020.3005148.
   PubMed Web of Science ®Google Scholar
• M. Suresh, P. G. Krishnamohan, and M. S. Holi. “GMM modeling of person information from EMG signals,” in 2011 IEEE Recent Adv. Intell. Comput. Syst. RAICS 2011, 2011, pp. 712–717. DOI:10.1109/RAICS.2011.6069403.
   Google Scholar
• A. K. Jain, “Are biometric techniques the future of personal identification?,” Nature, Vol. 449, pp. 38–40, 2007.
   PubMed Web of Science ®Google Scholar
• X. Niu, X. Gao, Y. Liu, and H. Liu, “Surface bioelectric dry electrodes: a review,” Meas. J. Int. Meas. Confed., Vol. 183, pp. 109774, 2021. DOI:10.1016/j.measurement.2021.109774.
   Web of Science ®Google Scholar
• Y. Fu, J. Zhao, Y. Dong, and X. Wang, “Dry electrodes for human bioelectrical signal monitoring,” Sensors (Switzerland), Vol. 20, pp. 1–30, 2020. DOI:10.3390/s20133651.
   Web of Science ®Google Scholar
• Y. N. Singh, S. K. Singh, and A. K. Ray, “Bioelectrical signals as emerging biometrics: issues and challenges,” ISRN Signal Process, Vol. 2012, pp. 1–13, 2012. DOI:10.5402/2012/712032.
   Google Scholar
• U. Ullas Pradhan, N. Reddy, K. Chandrashekar, and C. B. Mohan, “Titanium dioxide based bioelectric sensor for the acquisition of electrocardiogram signals,” Microchem. J, Vol. 160, pp. 105656, 2021. DOI:10.1016/j.microc.2020.105656.
   Web of Science ®Google Scholar
• A. Jaya Prakash, K. K. Patro, M. Hammad, R. Tadeusiewicz, and P. Pławiak, “BAED: A secured biometric authentication system using ECG signal based on deep learning techniques,” Biocybern. Biomed. Eng., Vol. 42, pp. 1081–1093, 2022. DOI:10.1016/j.bbe.2022.08.004.
   Web of Science ®Google Scholar
• K. K. Patro, A. J. Prakash, S. Samantray, J. Pławiak, R. Tadeusiewicz, and P. Pławiak, “A hybrid approach of a deep learning technique for real-time Ecg beat detection,” Int. J. Appl. Math. Comput. Sci., Vol. 32, pp. 455–465, 2022. DOI:10.34768/amcs-2022-0033.
   Web of Science ®Google Scholar
• M. Hammad, S. A. Chelloug, R. Alkanhel, and A. J. Prakash. Automated detection of myocardial infarction and heart, 2022.
   Google Scholar
• K. Gorur, and B. Eraslan, “The single-channel dry electrode SSVEP-based biometric approach: data augmentation techniques against overfitting for RNN-based deep models,” Phys. Eng. Sci. Med., 2022. DOI:10.1007/s13246-022-01189-1.
   PubMedGoogle Scholar
• X. Jiang, et al., “ Enhancing IoT security via cancelable HD-sEMG-based biometric authentication password, encoded by gesture,” IEEE Internet Things J., Vol. 8, pp. 16535–16547, 2021. DOI:10.1109/JIOT.2021.3074952.
   Web of Science ®Google Scholar
• R. Donida Labati, E. Muñoz, V. Piuri, R. Sassi, and F. Scotti, “Deep-ECG: convolutional neural networks for ECG biometric recognition,” Pattern Recognit. Lett, Vol. 126, pp. 78–85, 2019. DOI:10.1016/j.patrec.2018.03.028.
   Web of Science ®Google Scholar
• R. Shalbaf, H. Behnam, J. W. Sleigh, D. A. Steyn-Ross, and M. L. Steyn-Ross, “Frontal-temporal synchronization of EEG signals quantified by order patterns cross recurrence analysis during propofol anesthesia,” IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 23, pp. 468–474, 2015. DOI:10.1109/TNSRE.2014.2350537.
   PubMed Web of Science ®Google Scholar
• X. Wang, F. Li, Y. Xie, S. Yang, and Y. Wang, “Gait and respiration-based user identification using Wi-Fi signal,” IEEE Internet Things J., Vol. 9, pp. 3509–3521, 2022. DOI:10.1109/JIOT.2021.3097892.
   Web of Science ®Google Scholar
• D. S. Putra, A. D. Wibawa, and M. H. Purnomo. “Classification of EMG during walking using principal component analysis and learning vector quantization for biometrics study,” in Proceeding – 2016 Int. Semin. Intell. Technol. Its Appl. ISITIA 2016 Recent Trends Intell. Comput. Technol. Sustain. Energy, 2017, pp. 145–150. DOI:10.1109/ISITIA.2016.7828649.
   Google Scholar
• S. Shin, J. Jung, M. Kang, K. H. Kang, and Y. T. Kim. “Electromyogram-based algorithm using bagged trees for biometric person authentication and motion recognition,” in Dig. Tech. Pap. - IEEE Int. Conf. Consum. Electron. 2020-Janua, 2020, pp. 18–21. DOI:10.1109/ICCE46568.2020.9043030.
   Google Scholar
• R. H. Chowdhury, M. B. I. Reaz, M. A. Bin Mohd Ali, A. A. A. Bakar, K. Chellappan, and T. G. Chang, “ Surface electromyography signal processing and classification techniques,” Sensors (Switzerland), Vol. 13, pp. 12431–12466, 2013. DOI:10.3390/s130912431.
   PubMed Web of Science ®Google Scholar
• Y. Wu, S. J. Ruan, and Y. H. Lee, “An ultra-low power surface emg sensor for wearable biometric and medical applications,” Biosensors., Vol. 11, 2021. DOI:10.3390/bios11110411.
   Google Scholar
• Q. Li, P. Dong, and J. Zheng, “Enhancing the security of pattern unlock with surface emg-based biometrics,” Appl. Sci, Vol. 10, 2020. DOI:10.3390/app10020541.
   Google Scholar
• S. Spasojevic, A. Rodrigues, K. Mahdaviani, W. D. Reid, A. Mihailidis, and S. S. Khan. “Onset and offset detection of respiratory EMG data based on energy operator signal,” 2021, pp. 7–10.
   Google Scholar
• H. Y. AbuNurah, D. W. Russell, and J. D. Lowman, “The validity of surface EMG of extra-diaphragmatic muscles in assessing respiratory responses during mechanical ventilation: A systematic review,” Pulmonology, Vol. 26, pp. 378–385, 2020. DOI:10.1016/j.pulmoe.2020.02.008.
   PubMed Web of Science ®Google Scholar
• Y. Ucal, and A. Ozpinar, “Proteomics in thyroid cancer and other thyroid-related diseases: A review of the literature,” Biochim. Biophys. Acta Proteins Proteom., Vol. 1868, pp. 140510, 2020. DOI:10.1016/j.bbapap.2020.140510.
   PubMed Web of Science ®Google Scholar
• G. Biagetti, V. P. Carnielli, P. Crippa, L. Falaschetti, V. Scacchia, L. Scalise, and C. Turchetti, “Dataset from spirometer and sEMG wireless sensor for diaphragmatic respiratory activity monitoring,” Data Br., Vol. 25, pp. 104217, 2019. DOI:10.1016/j.dib.2019.104217.
   PubMed Web of Science ®Google Scholar
• S. Said, I. Boulkaibet, M. Sheikh, A. S. Karar, S. Alkork, and A. Nait-Ali, “ Machine-learning-based muscle control of a 3D-printed bionic arm,” Sensors (Switzerland), Vol. 20, pp. 1–16, 2020. DOI:10.3390/s20113144.
   Google Scholar
• A. Pal, and Y. N. Singh. ECG biometric recognition. Singapore: Springer, 2018.
   Google Scholar
• K. N. Plataniotis, D. Hatzinakos, and J. K. M. Lee, “ECG biometric recognition without fiducial detection,” Biometrics Symp. BCC, Vol. 2006, pp. 6–11, 2006. DOI:10.1109/BCC.2006.4341628.
   Google Scholar
• D. A. Alduwaile, and M. S. Islam, “ Using convolutional neural network and a single heartbeat for ecg biometric recognition,” Entropy, Vol. 23, 2021. DOI:10.3390/e23060733.
   PubMed Web of Science ®Google Scholar
• I. Odinaka, P. H. Lai, A. D. Kaplan, J. A. O’Sullivan, E. J. Sirevaag, and J. W. Rohrbaugh, “.: ECG biometric recognition: A comparative analysis,” IEEE Trans. Inf. Forens. Secur., Vol. 7, pp. 1812–1824, 2012. DOI:10.1109/TIFS.2012.2215324.
   Web of Science ®Google Scholar
• D. Chakladar, P. Kumar, P. P. Roy, D. P. Dogra, E. Scheme, and V. Chang, “A multimodal-Siamese Neural Network (mSNN) for person verification using signatures and EEG,” Inf. Fusion, Vol. 71, pp. 17–27, 2021. DOI:10.1016/j.inffus.2021.01.004.
   Web of Science ®Google Scholar
• M. R. Boubakeur, and G. Wang, “Self-relative evaluation framework for eeg-based biometric systems,” Sensors, Vol. 21, pp. 1–19, 2021. DOI:10.3390/s21062097.
   Web of Science ®Google Scholar
• H. Y. Yap, Y. H. Choo, Z. I. Mohd Yusoh, and W. H. Khoh, “Person authentication based on eye-closed and visual stimulation using EEG signals,” Brain. Inform., Vol. 8, 2021. DOI:10.1186/s40708-021-00142-4.
   PubMedGoogle Scholar
• C. M. Jijomon, and A. P. Vinod, “Person-identification using familiar-name auditory evoked potentials from frontal EEG electrodes,” Biomed. Signal Process. Contr., Vol. 68, pp. 102739, 2021. DOI:10.1016/j.bspc.2021.102739.
   Web of Science ®Google Scholar
• S. Salem, and Z. Lachiri. “CNN-SVM approach for EEG-Based Person Identification using Emotional dataset,” 2019 Int. Conf. Signal, Control Commun. SCC 2019, 2019, pp. 241–245. DOI:10.1109/SCC47175.2019.9116175.
   Google Scholar
• M. Wang, K. Kasmarik, A. Bezerianos, K. C. Tan, and H. Abbass, “On the channel density of EEG signals for reliable biometric recognition,” Patt. Recognit. Lett., Vol. 147, pp. 134–141, 2021. DOI:10.1016/j.patrec.2021.04.003.
   Web of Science ®Google Scholar
• B. Nguyen, W. Ma, and D. Tran, “Biometric recognition system performance measures for lossy compression on EEG signals,” Log. J. IGPL, Vol. 29, pp. 889–905, 2021. DOI:10.1093/jigpal/jzaa033.
   Web of Science ®Google Scholar
• E. Maiorana, “Learning deep features for task-independent EEG-based biometric verification,” Patt. Recognit. Lett., Vol. 143, pp. 122–129, 2021. DOI:10.1016/j.patrec.2021.01.004.
   Web of Science ®Google Scholar
• Y. Zhang, et al., “Affective EEG-based person identification using channel attention convolutional neural dense connection network,” Secur. Commun. Netw., Vol. 2021, pp. 1–10, 2021. DOI:10.1155/2021/7568460.
   Google Scholar
• H. Zhao, Y. Chen, W. Pei, H. Chen, and Y. Wang, “Towards online applications of EEG biometrics using visual evoked potentials,” Expert Syst. Appl., Vol. 177, pp. 114961, 2021. DOI:10.1016/j.eswa.2021.114961.
   Web of Science ®Google Scholar
• M. R. F. Mota, P. H. L. Silva, E. J. S. Luz, G. J. P. Moreira, T. Schons, L. A. G. Moraes, and D. Menotti, “ A deep descriptor for cross-tasking EEG-based recognition,” PeerJ Comput. Sci., Vol. 7, pp. 1–27, 2021. DOI:10.7717/PEERJ-CS.549.
   Google Scholar
• D. Carrión-Ojeda, R. Fonseca-Delgado, and I. Pineda, “Analysis of factors that influence the performance of biometric systems based on EEG signals,” Expert Syst. Appl., Vol. 165, pp. 113967, 2021. DOI:10.1016/j.eswa.2020.113967.
   Web of Science ®Google Scholar
• M. Lee, J. Ryu, and I. Youn. “Biometric personal identification based on gait analysis using surface EMG signals,” 2017 2nd IEEE Int. Conf. Comput. Intell. Appl. ICCIA 2017, 2017, pp. 318–321. DOI:10.1109/CIAPP.2017.8167230.
   Google Scholar
• R. K. Raji, M. Adjeisah, X. Miao, and A. Wan, “A novel respiration pattern biometric prediction system based on artificial neural network,” Sens. Rev., Vol. 40, pp. 8–16, 2020. DOI:10.1108/SR-10-2019-0235.
   Web of Science ®Google Scholar
• S. Kanoga, A. Kanemura, and H. Asoh, “Are armband sEMG devices dense enough for long-term use? – Sensor placement shifts cause significant reduction in recognition accuracy,” Biomed. Signal Process. Contr., Vol. 60, pp. 101981, 2020. DOI:10.1016/j.bspc.2020.101981.
   Web of Science ®Google Scholar
• S. Wei, X. Chen, X. Yang, S. Cao, and X. Zhang, “A component-based vocabulary-extensible sign language gesture recognition framework,” Sensors (Switzerland), Vol. 16, 2016. DOI:10.3390/s16040556.
   Google Scholar
• A. Phinyomark, and E. Scheme. “A feature extraction issue for myoelectric control based on wearable EMG sensors,” in 2018 IEEE Sensors Appl. Symp. SAS 2018 – Proc. 2018-Janua, 2018, pp. 1–6. DOI:10.1109/SAS.2018.8336753.
   Google Scholar
• J. Yang, J. Pan, and J. Li. “SEMG-based continuous hand gesture recognition using GMM-HMM and threshold model,” in 2017 IEEE Int. Conf. Robot. Biomimetics, ROBIO 2017, 2018, pp. 1509–1514. DOI:10.1109/ROBIO.2017.8324631.
   Google Scholar
• C. Tepe, and M. C. Demir, “The effects of the number of channels and gyroscopic data on the classification performance in EMG data acquired by Myo armband,” J. Comput. Sci., Vol. 51, pp. 101348, 2021. DOI:10.1016/j.jocs.2021.101348.
   Web of Science ®Google Scholar
• I. Ozer, C. K. Ozer, A. C. Karaca, K. Gorur, I. Kocak, and O. Cetin, “Species-level microfossil identification for globotruncana genus using hybrid deep learning algorithms from the scratch via a low-cost light microscope imaging,” Multimed. Tools Appl., 2022. DOI:10.1007/s11042-022-13810-2.
   Web of Science ®Google Scholar
• K. Gorur, M. R. Bozkurt, M. S. Bascil, and F. Temurtas, “Glossokinetic potential based tongue–machine interface for 1-D extraction,” Australas. Phys. Eng. Sci. Med., Vol. 41, pp. 379–391, 2018. DOI:10.1007/s13246-018-0635-x.
   PubMed Web of Science ®Google Scholar
• B. Taşar. Control of the multifunctıonal prosthetic hand simulator via EMG signals people. Fırat University Institute of Science, 2016.
   Google Scholar
• A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “Feature reduction and selection for EMG signal classification,” Expert Syst. Appl., Vol. 39, pp. 7420–7431, 2012. DOI:10.1016/j.eswa.2012.01.102.
   Web of Science ®Google Scholar
• M. F. Wahid, R. Tafreshi, M. Al-Sowaidi, and R. Langari, “Subject-independent hand gesture recognition using normalization and machine learning algorithms,” J. Comput. Sci, Vol. 27, pp. 69–76, 2018. DOI:10.1016/j.jocs.2018.04.019.
   Web of Science ®Google Scholar
• M. Zardoshti-kermani, B. C. Wheeler, K. Badie, and R. M. Hashemi, “Serie: Nueva Historia Mínima de México Ilustrada,” De la Independencia a la Consolidación Republicana. DVD, Vol. 3, pp. 324–333, 1995.
   Google Scholar
• B. K. Iwana, and S. Uchida, “An empirical survey of data augmentation for time series classification with neural networks,” PLoS One, Vol. 16, pp. 1–43, 2021. DOI:10.1371/journal.pone.0254841.
   Web of Science ®Google Scholar
• C. He, J. Liu, Y. Zhu, and W. Du, “Data augmentation for deep neural networks model in EEG classification task: A review,” Front. Hum. Neurosci., Vol. 15, 2021. DOI:10.3389/fnhum.2021.765525.
   Web of Science ®Google Scholar
• E. Lashgari, D. Liang, and U. Maoz, “Data augmentation for deep-learning-based electroencephalography,” J. Neurosci. Methods, Vol. 346, pp. 108885, 2020. DOI:10.1016/j.jneumeth.2020.108885.
   PubMed Web of Science ®Google Scholar
• F. F. Abushmmala, and H. A. Abughali. “Color harmony classification using machine learning algorithms: KNN and SVM,” Proc. - 2020 Int. Conf. Promis. Electron. Technol. ICPET 2020, 2020, pp. 150–154. DOI:10.1109/ICPET51420.2020.00037.
   Google Scholar
• M. U. Khan, Z. A. Choudry, S. Aziz, S. Z. H. Naqvi, A. Aymin, and M. A. Imtiaz. “Biometric authentication based on EMG signals of speech,” 2nd Int. Conf. Electr. Commun. Comput. Eng. ICECCE 2020, 2020, pp. 2–6. DOI:10.1109/ICECCE49384.2020.9179354.
   Google Scholar
• G. Pomboza-Junez, and J. H. Terriza. “Hand gesture recognition based on sEMG signals using Support Vector Machines,” IEEE Int. Conf. Consum. Electron. - Berlin, ICCE-Berlin, 2016, pp. 174–178. DOI:10.1109/ICCE-Berlin.2016.7684748.
   Google Scholar
• C. Wallraven, B. Caputo, and A. Graf. Recognition with local features: The kernel recipe. Proc. IEEE Int. Conf. Comput. Vis. 1, 257–264 (2003). DOI:10.1109/iccv.2003.1238351.
   Google Scholar
• T. B. Sasongko, O. Arifin, and H. Al Fatta. “Optimization of hyper parameter bandwidth on naïve Bayes kernel density estimation for the breast cancer classification,” 2019 Int. Conf. Inf. Commun. Technol. ICOIACT 2019, 2019, pp. 226–231. DOI:10.1109/ICOIACT46704.2019.8938497.
   Google Scholar
• R. R. Sethuraman, and J. S. K. Athisayam, “An improved feature selection based on naive Bayes with Kernel density estimator for opinion mining,” Arab. J. Sci. Eng., Vol. 46, pp. 4059–4071, 2021. DOI:10.1007/s13369-021-05381-5.
   Web of Science ®Google Scholar
• P. M. Abhilash, and D. Chakradhar, “Sustainability improvement of WEDM process by analysing and classifying wire rupture using kernel-based naive Bayes classifier,” J. Braz. Soc. Mech. Sci. Eng., Vol. 43, pp. 1–9, 2021. DOI:10.1007/s40430-021-02805-z.
   Web of Science ®Google Scholar
• A. Niranjan, K. M. Akshobhya, P. D. Shenoy, and K. R. Venugopal. “EKNIS: Ensemble of KNN, Naïve Bayes Kernel and ID3 for Efficient Botnet Classification Using Stacking,” 2018 Int. Conf. Data Sci. Eng. ICDSE 2018, 2018. DOI:10.1109/ICDSE.2018.8527791.
   Google Scholar
• F. Temurtas, “ A comparative study on thyroid disease diagnosis using neural networks,” Expert. Syst. Appl., Vol. 36, pp. 944–949, 2009. DOI:10.1016/j.eswa.2007.10.010.
   Web of Science ®Google Scholar
• N. Belgacem, R. Fournier, A. Nait-Ali, and F. Bereksi-Reguig, “A novel biometric authentication approach using ECG and EMG signals,” J. Med. Eng. Technol., Vol. 39, pp. 226–238, 2015. DOI:10.3109/03091902.2015.1021429.
   PubMedGoogle Scholar
• C. M. Jijomon, and A. P. Vinod, “ Person-identification using familiar-name auditory evoked potentials from frontal EEG electrodes,” Biomed. Signal Process. Contr., Vol. 68, pp. 102739, 2021. DOI:10.1016/j.bspc.2021.102739.
   Web of Science ®Google Scholar
• F. Ahmed, A. S. M. H. Bari, and M. L. Gavrilova, “ Emotion recognition from body movement,” IEEE ACCESS., Vol. 8, pp. 11761–11781, 2020. DOI:10.1109/Access.2019.2963113.
   Web of Science ®Google Scholar
• C. Rodrigues Bernadelli, and P. R. Da Silva, “Dynamic time warping in iris biometric recognition process,” IEEE Lat. Am. Trans., Vol. 19, pp. 42–49, 2021. DOI:10.1109/TLA.2021.9423825.
   Web of Science ®Google Scholar
• I. Ozer, O. Cetin, K. Gorur, and F. Temurtas, “Improved machine learning performances with transfer learning to predicting need for hospitalization in arboviral infections against the small dataset,” Neural Comput. Appl., Vol. 33, pp. 14975–14989, 2021. DOI:10.1007/s00521-021-06133-0.
   PubMed Web of Science ®Google Scholar