Revolutionizing Sleep Diagnostics: A Novel Deep Learning Approach for Real-Time Sleep Stage Analysis
Keywords:
Automated Sleep Stage Classification (ASSC), Deep Learning, Electroencephalogram (EEG) Signals, Sleep Disorders and Brain Diseases, Feature Extraction ,WDT, PCA, Hybrid Classifiers, BiLSTM, LightGBM, Real-time AnalysisAbstract
Sleep stage scoring, traditionally done manually by specialists through the inspection of neurophysiological data from sleep studies, is a labor-intensive, monotonous, and time-consuming activity. This has led to an increased interest in the development of Automated Sleep Stage Classification (ASSC) technologies. Such systems are vital for assisting medical professionals in diagnosing and managing sleep-related disorders and neurological conditions, including Alzheimer's disease. This paper presents a cutting-edge classification technique that combines deep learning strategies, delivering outstanding outcomes. It also reviews progress and hurdles in current methods of sleep stage determination using Electroencephalogram (EEG) signals, covering steps like preprocessing, feature detection, and categorization. The paper's goal is to unveil a new classifier design that promises real-time, high-accuracy solutions recognized by the scientific community. This includes the classification of EEG signals into different patient sleep stages: Wake, N1, N2, N3, and REM. Employing a robust classifier system within the Electroencephalography Analysis System (EAS) based on a Brain-Computer Interface (BCI), this system utilizes hybrid classifiers beginning with feature extraction methods such as WDT and PCA, followed by a combination of BiLSTM and LightGBM classifiers. The process starts with training the BiLSTM model on raw EEG data to learn temporal patterns and feature extraction. Features from the BiLSTM outputs are then used as inputs for LightGBM, creating a potent classification system. Unlike previous approaches that often required multiple EEG channels and longer epochs, this research introduces an effective method for 10-second epochs from a single-channel EEG, incorporating novel statistical features and utilizing the PhysioNet Sleep Database, EDFx sleep DATA. The proposed method has shown an average classification sensitivity of 92.1%, specificity of 98.8%, and an overall accuracy of 97.42% using a decision tree classifier, outperforming previous studies in classification accuracy.
Downloads
References
Estrada, E.; Nazeran, H.; Nava, P.; Behbehani, K.; Burk, J.; Lucas, E. Itakura distance: A useful similarity measure between EEG and EOG signals in computer-aided classification of sleep stages. In Proceedings of the 27th IEEE Annual International Conference of Engineering in Medicine and Biology Society, Shanghai, China, 1–4 September 2005; pp. 1189–1192.
Li, Y.; Yingle, F.; Gu, L.; Qinye, T. Sleep stage classification based on EEG Hilbert–Huang transform. In Proceedings of the 4th IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi’an, China, 25–27 May 2009; pp. 3676–3681.
Aboalayon, K.A.; Faezipour, M. Multi-class SVM based on sleep stage identification using EEG signal. In Proceedings of the IEEE Healthcare Innovation Conference (HIC), Seattle, WA, USA, 8–10 October 2014; pp. 181–184.
Huang, C.-S.; Lin, C.-L.; Ko, L.-W.; Liu, S.-Y.; Sua, T.-P.; Lin, C.-T. A hierarchical classification system for sleep stage scoring via forehead EEG signals. In Proceedings of the IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), Singapore, 16–19 April 2013; pp. 1–5.
Huang, C.-S.; Lin, C.-L.; Yang, W.-Y.; Ko, L.-W.; Liu, S.-Y.; Lin, C.-T. Applying the fuzzy c-means based dimension reduction to improve the sleep classification system. In Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ), Hyderabad, India, 7–10 July 2013; pp. 1–5.
Lee, Y.-H.; Chen, Y.-S.; Chen, L.-F. Automated sleep staging using single EEG channel for REM sleep deprivation. In Proceedings of the Ninth IEEE International Conference on Bioinformatics and BioEngineering, Taichung, Taiwan, 22–24 June 2009; pp. 439–442.
Hassan, A.R.; Bhuiyan, M.I.H. Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybern. Biomed. Eng. 2016, 36, 248–255. [Google Scholar] [CrossRef]
Khalighi, S.; Sousa, T.; Pires, G.; Nunes, U. Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels. Expert Syst. Appl. 2013, 40, 7046–7059. [Google Scholar] [CrossRef][Green Version]
Şen, B.; Peker, M.; Çavuşoğlu, A.; Çelebi, F.V. A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms. J. Med. Syst. 2014, 38, 1–21. [Google Scholar] [CrossRef] [PubMed]
Radha, M.; Garcia-Molina, G.; Poel, M.; Tononi, G. Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal. In Proceedings of the 36th IEEE Annual International Conference of Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; pp. 1876–1880.
Hassan, A.R.; Bashar, S.K.; Bhuiyan, M.I.H. On the classification of sleep states by means of statistical and spectral features from single channel electroencephalogram. In Proceedings of the IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, India, 10–13 August 2015; pp. 2238–2243.
Hassan, A.R.; Bhuiyan, M.I.H. Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomed. Signal Process. Control 2016, 24, 1–10. [Google Scholar] [CrossRef]
Ge, J.; Zhou, P.; Zhao, X.; Wang, M. Sample entropy analysis of sleep EEG under different stages. In Proceedings of the IEEE/ICME International Conference on Complex Medical Engineering, Beijing, China, 23–27 May 2007.
Kuo, C.-E.; Liang, S.-F. Automatic stage scoring of single-channel sleep EEG based on multiscale permutation entropy. In Proceedings of the IEEE Conference on Biomedical Circuits and Systems (BioCAS), San Diego, CA, USA, 10–12 November 2011; pp. 448–451.
Liang, S.-F.; Kuo, C.-E.; Hu, Y.-H.; Cheng, Y.-S. A rule-based automatic sleep staging method. In Proceedings of the 33rd IEEE EMBS Annual International Conference of the Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August–3 September 2011; pp. 6067–6070.
Rodríguez-Sotelo, J.L.; Osorio-Forero, A.; Jiménez-Rodríguez, A.; Cuesta-Frau, D.; Cirugeda-Roldán, E.; Peluffo, D. Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques. Entropy 2014, 16, 6573–6589. [Google Scholar] [CrossRef]
Lan, K.-C.; Chang, D.-W.; Kuo, C.-E.; Wei, M.-Z.; Li, Y.-H.; Shaw, F.-Z.; Liang, S.-F. Using off-the-shelf lossy compression for wireless home sleep staging. J. Neurosci. Methods 2015, 246, 142–152. [Google Scholar] [CrossRef] [PubMed]
Estrada, E.; Nazeran, H.; Ebrahimi, F.; Mikaeili, M. EEG signal features for computer-aided sleep stage detection. In Proceedings of the 4th International IEEE/EMBS Conference on Neural Engineering, Antalya, Turkey, 29 April–2 May 2009; pp. 669–672.
Estrada, E.; Nazeran, H. EEG and HRV signal features for automatic sleep staging and apnea detection. In Proceedings of the 20th IEEE International Conference on Electronics, Communications and Computer (CONIELECOMP), Cholula, Mexico, 22–24 February 2010; pp. 142–147.
Leistedt, S.; Dumont, M.; Lanquart, J.-P.; Jurysta, F.; Linkowski, P. Characterization of the sleep EEG in acutely depressed men using detrended fluctuation analysis. Neurophysiol. Clin. 2007, 118, 940–950. [Google Scholar] [CrossRef] [PubMed]
Redmond, S.J.; Heneghan, C. Cardiorespiratory-based sleep staging in subjects with obstructive sleep apnea. IEEE Trans. Biomed. Eng. 2006, 53, 485–496. [Google Scholar] [CrossRef] [PubMed]
Alvarez, D.; Hornero, R.; Marcos, J.V.; del Campo, F.; Lopez, M. Spectral analysis of electroencephalogram and oximetric signals in obstructive sleep apnea diagnosis. In Proceedings of the 31st IEEE Annual International Conference of the Engineering in Medicine and Biology Society, Minneapolis, MN, USA, 3–6 September 2009; pp. 400–403.
Correa, A.G.; Leber, E.L. An automatic detector of drowsiness based on spectral analysis and wavelet decomposition of EEG records. In Proceedings of the 32nd IEEE EMBS Annual International Conference of the Engineering in Medicine and Biology, Buenos Aires, Argentina, 31 August–4 September 2010; pp. 1405–1408.
Gurudath, N.; Riley, H.B. Drowsy driving detection by EEG analysis using wavelet transform and k-means clustering. Procedia Comput. Sci. 2014, 34, 400–409. [Google Scholar] [CrossRef]
Correa, A.G.; Orosco, L.; Laciar, E. Automatic detection of drowsiness in EEG records based on multimodal analysis. Med. Eng. Phys. 2014, 36, 244–249. [Google Scholar] [CrossRef] [PubMed]
Kumari, K. Review on drowsy driving: Becoming dangerous problem. Int. J. Sci. Res. 2014, 3, 49–51. [Google Scholar]
Tsai, P.-Y.; Hu, W.; Kuo, T.B.; Shyu, L.-Y. A portable device for real time drowsiness detection using novel active dry electrode system. In Proceedings of the 31st IEEE EMBS Annual International Conference of the Engineering in Medicine and Biology Society, Minneapolis, MN, USA, 3–6 September 2009; pp. 3775–3778.
Yu, S.; Li, P.; Lin, H.; Rohani, E.; Choi, G.; Shao, B.; Wang, Q. Support vector machine based detection of drowsiness using minimum EEG features. In Proceedings of the IEEE International Conference on Social Computing (SocialCom), Alexandria, VA, USA, 8–14 September 2013; pp. 827–835.
Fraiwan, L.; Lweesy, K.; Khasawneh, N.; Fraiwan, M.; Wenz, H.; Dickhaus, H. Time frequency analysis for automated sleep stage identification in fullterm and preterm neonates. J. Med. Syst. 2011, 35, 693–702. [Google Scholar] [CrossRef] [PubMed]
Fraiwan, L.; Lweesy, K.; Khasawneh, N.; Wenz, H.; Dickhaus, H. Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier. Comput. Methods Progr. Biomed. 2012, 108, 10–19. [Google Scholar] [CrossRef] [PubMed]
Ebrahimi, F.; Mikaili, M.; Estrada, E.; Nazeran, H. Assessment of itakura distance as a valuable feature for computer-aided classification of sleep stages. In Proceedings of the 29th IEEE EMBS Annual International Conference of the Engineering in Medicine and Biology Society, Lyon, France, 22–26 August 2007; pp. 3300–3303.
Mora, A.M.; Fernandes, C.M.; Herrera, L.J.; Castillo, P.A.; Merelo, J.; Rojas, F.; Rosa, A.C. Sleeping with ants, SVMs, multilayer perceptrons and SOMs. In Proceedings of the 10th International Conference on Intelligent Systems Design and Applications, Cairo, Egypt, 29 November–1 December 2010; pp. 126–131.
Vatankhah, M.; Akbarzadeh-T, M.R.; Moghimi, A. An intelligent system for diagnosing sleep stages using wavelet coefficients. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, 18–23 July 2010; pp. 1–5.
Brignol, A.; Al-Ani, T.; Drouot, X. EEG-based automatic sleep-wake classification in humans using short and standard epoch lengths. In Proceedings of the 12th IEEE International Conference on Bioinformatics & Bioengineering (BIBE), Larnaca, Cyprus, 11–13 November 2012; pp. 276–281.
Jo, H.G.; Park, J.Y.; Lee, C.K.; An, S.K.; Yoo, S.K. Genetic fuzzy classifier for sleep stage identification. Comput. Biol. Med. 2010, 40, 629–634. [Google Scholar] [CrossRef] [PubMed]
Zhu, G.; Li, Y.; Wen, P.P. Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal. IEEE J. Biomed. Health Inform. 2014, 18, 1813–1821. [Google Scholar] [CrossRef] [PubMed]
Gudmundsson, S.; Runarsson, T.P.; Sigurdsson, S. Automatic sleep staging using support vector machines with posterior probability estimates. In Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC), Vienna, Austria, 28–30 November 2005; pp. 366–372.
Hsu, Y.-L.; Yang, Y.-T.; Wang, J.-S.; Hsu, C.-Y. Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing 2013, 104, 105–114. [Google Scholar] [CrossRef]
Le, Q.K.; Truong, Q.D.K.; Vo, V.T. A tool for analysis and classification of sleep stages. In Proceedings of the IEEE International Conference on Advanced Technologies for Communications (ATC), Da Nang, Vietnam, 2–4 August 2011; pp. 307–310.
Shuyuan, X.; Bei, W.; Jian, Z.; Qunfeng, Z.; Junzhong, Z.; Nakamura, M. An improved k-means clustering algorithm for sleep stages classification. In Proceedings of the 54th IEEE Annual Conference on Society of Instrument and Control Engineers of Japan (SICE), Hangzhou, China, 28–30 July 2015; pp. 1222–1227.
Lajnef, T.; Chaibi, S.; Ruby, P.; Aguera, P.-E.; Eichenlaub, J.-B.; Samet, M.; Kachouri, A.; Jerbi, K. Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines. J. Neurosci. Methods 2015, 250, 94–105. [Google Scholar] [CrossRef] [PubMed]
Krakovská, A.; Mezeiová, K. Automatic sleep scoring: A search for an optimal combination of measures. Artif. Intell. Med. 2011, 53, 25–33. [Google Scholar] [CrossRef] [PubMed]
Güneş, S.; Polat, K.; Yosunkaya, Ş. Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting. Expert Syst. Appl. 2010, 37, 7922–7928. [Google Scholar] [CrossRef]
Charbonnier, S.; Zoubek, L.; Lesecq, S.; Chapotot, F. Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging. Comput. Biol. Med. 2011, 41, 380–389. [Google Scholar] [CrossRef] [PubMed]
Koley, B.; Dey, D. An ensemble system for automatic sleep stage classification using single channel EEG signal. Comput. Biol. Med. 2012, 42, 1186–1195. [Google Scholar] [CrossRef] [PubMed]
Ronzhina, M.; Janoušek, O.; Kolářová, J.; Nováková, M.; Honzík, P.; Provazník, I. Sleep scoring using artificial neural networks. Sleep Med. Rev. 2012, 16, 251–263. [Google Scholar] [CrossRef] [PubMed]
Zoubek, L.; Charbonnier, S.; Lesecq, S.; Buguet, A.; Chapotot, F. Feature selection for sleep/wake stages classification using data driven methods. Biomed. Signal Process. Control 2007, 2, 171–179. [Google Scholar] [CrossRef]
Phan, H.; Do, Q.; Do, T.-L.; Vu, D.-L. Metric learning for automatic sleep stage classification. In Proceedings of the 35th IEEE Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3–7 July 2013; pp. 5025–5028.
Kayikcioglu, T.; Maleki, M.; Eroglu, K. Fast and accurate PLS-based classification of EEG sleep using single channel data. Expert. Syst. Appl. 2015, 42, 7825–7830. [Google Scholar] [CrossRef]
Sousa, T.; Cruz, A.; Khalighi, S.; Pires, G.; Nunes, U. A two-step automatic sleep stage classification method with dubious range detection. Comput. Biol. Med. 2015, 59, 42–53. [Google Scholar] [CrossRef] [PubMed]
Khalighi, S.; Sousa, T.; Oliveira, D.; Pires, G.; Nunes, U. Efficient feature selection for sleep staging based on maximal overlap discrete wavelet transform and SVM. In Proceedings of the 33rd IEEE EMBS Annual International Conference of the Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August–3 September 2011; pp. 3306–3309.
Zhovna, I.; Shallom, I.D. Automatic detection and classification of sleep stages by multichannel EEG signal modeling. In Proceedings of the 30th IEEE EMBS 2008 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada, 20–25 August 2008; pp. 2665–2668.
Yu, S.; Chen, X.; Wang, B.; Wang, X. Automatic sleep stage classification based on ECG and EEG features for day time short nap evaluation. In Proceedings of the 10th World Congress on Intelligent Control and Automation (WCICA), Beijing, China, 6–8 July 2012; pp. 4974–4977.
Ebrahimi, F.; Mikaeili, M.; Estrada, E.; Nazeran, H. Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients. In Proceedings of the 30th IEEE EMBS Annual International Conference of the Engineering in Medicine and Biology Society, Vancouver, BC, Canada, 20–25 August 2008; pp. 1151–1154.
Fell, J.; Röschke, J.; Mann, K.; Schäffner, C. Discrimination of sleep stages: A comparison between spectral and nonlinear EEG measures. Electroencephalogr. Clin. Neurophysiol. 1996, 98, 401–410. [Google Scholar] [CrossRef]
Weiss, B.; Clemens, Z.; Bódizs, R.; Halász, P. Comparison of fractal and power spectral EEG features: Effects of topography and sleep stages. Brain Res. Bull. 2011, 84, 359–375. [Google Scholar] [CrossRef] [PubMed]
Liu, Y.; Yan, L.; Zeng, B.; Wang, W. Automatic sleep stage scoring using Hilbert–Huang transform with BP neural network. In Proceedings of the 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), Chengdu, China, 18–20 June 2010; pp. 1–4.
Dursun, M.; Gunes, S.; Ozsen, S.; Yosunkaya, S. Comparison of artificial immune clustering with fuzzy c-means clustering in the sleep stage classification problem. In Proceedings of the International Symposium on Innovations in Intelligent Systems and Applications (INISTA), Trabzon, Turkey, 2–4 July 2012; pp. 1–4.
Estrada, E.; Nazeran, H.; Nava, P.; Behbehani, K.; Burk, J.; Lucas, E. EEG feature extraction for classification of sleep stages. In Proceedings of the 26th IEEE EMBS Annual International Conference of the engineering in Medicine and Biology Society, San Francisco, CA, USA, 1–5 September 2004; pp. 196–199.
Obayya, M.; Abou-Chadi, F. Automatic classification of sleep stages using EEG records based on fuzzy c-means (fcm) algorithm. In Proceedings of the 31st National Radio Science Conference (NRSC), Cairo, Egypt, 28–30 April 2014; pp. 265–272.
Ahmad, R.F.; Malik, A.S.; Kamel, N.; Amin, H.; Zafar, R.; Qayyum, A.; Reza, F. Discriminating the different human brain states with EEG signals using fractal dimension: A nonlinear approach. In Proceedings of the IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), Kuala Lumpur, Malaysia, 25–25 November 2014; pp. 1–5.
Borzenko, A. Neuron mechanism of human languages. In Proceedings of the International Joint Conference on Neural Networks, Atlanta, GA, USA, 14–19 June 2009; pp. 375–382.
Myelin. Available online: https://en.wikipedia.org/wiki/Myelin (accessed on 22 August 2016).
Khalifa, W.; Salem, A.; Roushdy, M.; Revett, K. A survey of EEG based user authentication schemes. In Proceedings of the 8th International Conference on Informatics and Systems (INFOS), Cairo, Egypt, 14–16 May 2012.
Hu, X.-S.; Hong, K.-S.; Ge, S.S. Fnirs-based online deception decoding. J. Neural Eng. 2012, 9, 26012–26019. [Google Scholar] [CrossRef] [PubMed]
Aqil, M.; Hong, K.-S.; Jeong, M.-Y.; Ge, S.S. Detection of event-related hemodynamic response to neuroactivation by dynamic modeling of brain activity. Neuroimage 2012, 63, 553–568. [Google Scholar] [CrossRef] [PubMed]
Khan, M.J.; Hong, K.-S. Passive BCI based on drowsiness detection: An fNIRS study. Biomed. Opt. Express 2015, 6, 4063–4078. [Google Scholar] [CrossRef] [PubMed]
Naseer, N.; Hong, K.-S. FNIRS-based brain-computer interfaces: A review. Front. Hum. Neurosci. 2015, 9, 3. [Google Scholar] [CrossRef] [PubMed]
Bhutta, M.R.; Hong, M.J.; Kim, Y.-H.; Hong, K.-S. Single-trial lie detection using a combined fNIRS-polygraph system. Front. Psychol. 2015, 6, 709. [Google Scholar] [CrossRef] [PubMed]
Hong, K.-S.; Naseer, N. Reduction of delay in detecting initial dips from functional near-infrared spectroscopy signals using vector-based phase analysis. Int. J. Neural. Syst. 2016, 26, 1650012. [Google Scholar] [CrossRef] [PubMed]
Hong, K.-S.; Santosa, H. Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy. Hear. Res. 2016, 333, 157–166. [Google Scholar] [CrossRef] [PubMed]
Rao, R.; Derakhshani, R. A comparison of EEG preprocessing methods using time delay neural networks. In Porceedings of the 2nd IEEE EMBS International Conference on Neural Engineering, Arlington, VA, USA, 16–19 March 2005; pp. 262–264.
Mantri, S.; Patil, V.; Mitkar, R. EEG based emotional distress analysis–a survey. Int. J. Eng. Dev. 2012, 4, 24–28. [Google Scholar]
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
Achermann, P., and Tarokh, L. (2014). Human sleep and its regulation. Kosmos 2, 173–180.
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
Omlin, X., Crivelli, F., Näf, M., Heinicke, L., Skorucak, J., Malafeev, A., et al. (2018). The effect of a slowly rocking bed on sleep. Sci. Rep. 8:2156. doi: 10.1038/s41598-018-19880-3
Iber, C., Ancoli-Israel, S., Chesson, A., and Quan, S. F. (2007). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. Westchester, IL: American Academy of Sleep Medicine.
Bishop, C. (2016). Pattern Recognition and Machine Learning. New York, NY: Springer-Verlag.
Babadi, B., and Brown, E. N. (2014). A review of multitaper spectral analysis. IEEE Trans. Biomed. Eng. 61, 1555–1564. doi: 10.1109/TBME.2014.2311996
Malafeev, A., Omlin, X., Wierzbicka, A., Wichniak, A., Jernajczyk, W., Riener, R., et al. (2018). Automatic artefact detection in single-channel sleep EEG recordings. J. Sleep Res. doi: 10.1111/jsr.12679 [Epub ahead of print].
Breiman, L. (2001). Random forests. Mach. Learn. 45, 5–32. doi: 10.1023/A:1010933404324
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., and Lang, K. J. (1989). Phoneme recognition using time-delay neural networks. IEEE Trans. Acoust. Speech Signal Proc. 37, 328–339. doi: 10.1109/29.21701
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
