Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges

Bob Chile-Agada; Laud Charles Ochei; Fubara Egbono

doi:doi:10.11648/j.ijiis.20241302.12

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Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges

Bob Chile-Agada, Laud Charles Ochei^*, Fubara Egbono

Published in International Journal of Intelligent Information Systems (Volume 13, Issue 2)

Received: 19 March 2024 Accepted: 3 April 2024 Published: 17 April 2024

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Abstract

This paper provides a thorough analysis of the use of electromyography (EMG) data in early stroke diagnosis and detection. Stroke continues to be a major global cause of disability and death, which emphasises the critical need for an accurate diagnosis made quickly to improve patient outcomes. Early detection is still difficult to achieve, even with improvements in medical imaging and testing technologies. By detecting minute variations in muscle activity linked to stroke symptoms, EMG data analysis offers a viable method for early stroke identification. The review delves into the diverse methodologies and strategies utilised to leverage EMG data for the purpose of stroke diagnosis, encompassing the application of deep learning models and machine learning algorithms. The paper proposes a structured framework for classifying approaches for early stroke detection and diagnosis using EMG data, providing a systematic way to categorize and compare different methodologies. The paper concludes by highlighting the revolutionary potential of EMG-based techniques in improving the diagnosis of strokes earlier and urging more study to address current issues and make clinical application easier.

Published in	International Journal of Intelligent Information Systems (Volume 13, Issue 2)
DOI	10.11648/j.ijiis.20241302.12
Page(s)	29-42
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Electromyography (EMG), Stroke, Stroke Detection, Stroke Diagnosis, Neuromuscular, Muscle, Machine Learning, Deep learning, Artificial Neural Network

References

[1]	Aguiar-Salazar, A., et al. (2022). Intelligent Electromyograph for Early Detection of Myopathy and Neuropathy Using EMG Signals and Neural Network Model. In J. HerreraTapia, G. RodriguezMorales, Fonseca, & S. BerrezuetaGuzman (Eds.), Information and Communication Technologies (pp. 32–45). Springer International Publishing.
[2]	Alawieh, A., Zhao, J., Feng, W., Yue, J. K., Chiang, C. C., Chaudhary, N.,... Ahmed, S. (2020). Machine learning–enabled automated determination of acute ischemic stroke onset. Stroke, 51(5), 1578-1587.
[3]	Al-Dwairi, A., Ali, A., & Al-Dwairi, M. (2020). Artificial intelligence techniques for diagnosing strokes. In IOP Conference Series: Materials Science and Engineering (Vol. 717, No. 1, p. 012015). IOP Publishing.
[4]	Alzubaidi, L., & Al-Sawad, A. (2021a). Image processing techniques for stroke detection using EMG data. In 2021 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) (pp. 1–6). IEEE.
[5]	Alzubaidi, L., & Al-Sawad, A. (2021b). Transfer learning-based approach for stroke detection using EMG signals. In 2021 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) (pp. 1–6). IEEE.
[6]	Amarenco, P., Lavallée, P. C., Monteiro, T., Sargento-Freitas, J., et al. (2016). Use of non-vitamin K antagonist oral anticoagulants in patients with atrial fibrillation and valvular heart lesions. Journal of the American College of Cardiology, 67(8), 259-267.
[7]	Asadi, S., & Azarnoosh, M. (2019). RNN-based sequential modeling of EMG data for stroke detection. In 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON) (pp. 935–939). IEEE.
[8]	Avian, B., et al. (2022). Estimating finger joint angles on surface EMG using Manifold Learning and Long Short-Term Memory with Attention mechanism. Biomedical Signal Processing and Control, 71, 103099.
[9]	Broderick, J. P., Adeoye, O., Elm, J. (2013). Evolution of the Modified Rankin Scale and its use in future stroke trials. Stroke, 44(6), 1173-1178.
[10]	Burkhart, M. C., Fee, W. F., Leach, J. L., & Moritani, T. (2014). Myositis ossificans mimicking soft tissue sarcoma: diagnosis with magnetic resonance imaging. Skeletal Radiology, 43(8), 1147-1153.
[11]	Carvalho, M., et al. (2023). Review of electromyography onset detection methods for real-time control of robotic exoskeletons. Journal of Neuroengineering and Rehabilitation, 20(1).
[12]	Chen, X., & Lv, J. (2018). Integrated EMG and EEG for comprehensive stroke detection. Journal of Healthcare Engineering, 2018, 1–9.
[13]	Cheng, K., & Gao, J. (2021). Wearable EMG sensors for stroke rehabilitation and early detection. In 2021 9th International Conference on Wireless Communications and Signal Processing (WCSP) (pp. 1–6). IEEE.
[14]	Choi, H., et al. (2021). Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals. Sensors, 21(13), 4269.
[15]	Eissa, A. E., Elfatairy, E., & Ahmed, K. (2020). A hybrid deep learning model for early detection of stroke using EEG and EMG signals. Cognitive Neurodynamics, 14(6), 769–779.
[16]	Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
[17]	Feigin, V. L., Lawes, C. M., Bennett, D. A., & Barker-Collo, S. L. (2014). Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review. The Lancet Neurology, 13(9), 914-924.
[18]	Ganapathy, A., & Munirathnam, K. P. (2019). A novel approach for stroke prediction using deep learning algorithms. In 2019 International Conference on Communication and Signal Processing (ICCSP) (pp. 0131–0135). IEEE.
[19]	Gu, H., Wang, Y., Hong, S., & Gui, G. (2019). Blind channel identification aided generalized automatic modulation recognition based on deep learning. IEEE Access, 7, 110722-110729.
[20]	Hachisuka, K., Ushio, S., & Suzuki, S. (2017). Electromyography-based early detection of stroke onset in human forearm. In 2017 International Conference on Rehabilitation Robotics (ICORR) (pp. 457-462). IEEE.
[21]	Hwang, H.-J., Choi, Y., & Lee, J.-H. (2019). Development and clinical application of wearable EMG sensors. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(9), 1777–1785.
[22]	Islam, M., Nizam, U. D., Rezwan, M., & Rahman, M. S. (2018). Analysis of EMG signals using CNN for stroke detection. Journal of Medical Systems.
[23]	Khorram, S., Gudeloglu, A., & Hellstrom, W. J. (2018). In-office evaluation of premature ejaculation: Considerations for clinical management. World Journal of Clinical Urology, 7(4), 46-54.
[24]	Kobylarz, F. A., Mackenzie, T. A., DeFroda, S. F., et al. (2020). Association of preoperative opioid expectations with postoperative satisfaction and outcomes in orthopaedic surgery: a prospective cohort study. Journal of Bone and Joint Surgery, 102(24), e141.
[25]	Lee, D., et al. (2024). EMG-based hand gesture classifier robust to daily variation: Recursive domain adversarial neural network with data synthesis. Biomedical Signal Processing and Control, 88, 105600–105600.
[26]	Lees, K. R., Bluhmki, E., von Kummer, R., Brott, T. G., et al. (2019). Time to treatment with intravenous alteplase and outcome in stroke: an updated pooled analysis of ECASS, ATLANTIS, NINDS, and EPITHET trials. Lancet, 375(9727), 1695-1703.
[27]	Li, Y., Ding, Y., Liu, S., & Chen, W. (2018). Study on the early rehabilitation training method of cerebral infarction patients based on surface electromyography. Journal of Healthcare Engineering, 2018, 1–8.
[28]	Liu, Y., & Zhang, Y. (2019). CNN-based feature extraction and classification of stroke-related EMG patterns. In 2019 IEEE International Conference on Progress in Informatics and Computing (PIC) (pp. 1–6). IEEE.
[29]	Ma, S., Xiao, B., Hong, R., Addissie, B., Drikas, Z., Antonsen, T.,... & Anlage, S. (2019). Classification and prediction of wave chaotic systems with machine learning techniques. arXiv preprint arXiv: 1908.04716.
[30]	Merletti, R., & Parker, P. A. (2004). Electromyography: physiology, engineering, and noninvasive applications. IEEE Press.
[31]	Paoletti, M., Demichelis, F., Guan, Y., Gerhardt, J., Zhang, X., Balan, S.,... & Rubin, M. A. (2020). Bioinformatic analysis of prostate cancer progression with and without definitive radiotherapy. British Journal of Cancer, 122(4), 489-495.
[32]	Powers, W. J., Rabinstein, A. A., Ackerson, T., Adeoye, O. M., Bambakidis, N. C., Becker, K.,... & Jauch, E. C. (2018). Guidelines for the Early Management of Patients With Acute Ischemic Stroke: 2019 Update to the 2018 Guidelines for the Early Management of Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke, 50(12), e344-e418.
[33]	Putro, N. A. S., et al. (2024). Estimating finger joint angles by surface EMG signal using feature extraction and transformer-based deep learning model. Biomedical Signal Processing and Control, 87, 105447–105447.
[34]	Qu, S., Lv, J., Zhang, Y., & Yu, J. (2017). Application of EMG-biofeedback in recovery training of limb motor function in patients with stroke. Journal of Physical Therapy Science, 29(8), 1426-1430.
[35]	Raghavendra, U., et al. (2018). Application of deep learning techniques for detection of stroke using EMG signals. International Journal of Stroke.
[36]	Simão, M., et al. (2019). EMG-based online classification of gestures with recurrent neural networks. Pattern Recognition Letters, 128, 45–51.
[37]	Smith, C. J., Bray, B. D., Hoffman, A., Meisel, A., Heuschmann, P. U., Wolfe, C. D., & Tyrrell, P. J. (2019). Can a Novel Clinical Risk Score Improve Stroke Risk Prediction in Transient Ischemic Attack Patients? Stroke, 50(6), 1460-1466.
[38]	Tang, X., Zhu, J., & Yu, W. (2011). Effects of L-carnitine against oxidative stress in human HepG2 cells through regulating mitochondrial function. Biomedical and Environmental Sciences, 24(6), 661–669.
[39]	Van't Hof, A. W., Ten Berg, J., Heestermans, T., Dill, T., Funck, R. C., van Werkum, W.,... & Suryapranata, H. (2020). Prehospital initiation of tirofiban in patients with ST-elevation myocardial infarction undergoing primary angioplasty (On-TIME 2): a multicentre, double-blind, randomised controlled trial. Lancet, 392(10150), 31-40.
[40]	Wang, H., Cao, Y., Chao, H., & Gu, X. (2020). Development of a finger rehabilitation training system based on surface EMG signals and virtual reality technology. Journal of Medical Engineering & Technology, 44(6), 376–384.
[41]	Xiong, W., & Guo, J. (2020). A new model for stroke detection using EMG signals. Journal of Medical Systems, 44(8), 1–10.
[42]	Yu, F., Wen, S., & Wei, H. (2020). Early stroke detection based on recurrent neural networks. In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 67–70). IEEE.

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APA Style

Chile-Agada, B., Ochei, L. C., Egbono, F. (2024). Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges. International Journal of Intelligent Information Systems, 13(2), 29-42. https://doi.org/10.11648/j.ijiis.20241302.12

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ACS Style

Chile-Agada, B.; Ochei, L. C.; Egbono, F. Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges. Int. J. Intell. Inf. Syst. 2024, 13(2), 29-42. doi: 10.11648/j.ijiis.20241302.12

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AMA Style

Chile-Agada B, Ochei LC, Egbono F. Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges. Int J Intell Inf Syst. 2024;13(2):29-42. doi: 10.11648/j.ijiis.20241302.12

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@article{10.11648/j.ijiis.20241302.12,
  author = {Bob Chile-Agada and Laud Charles Ochei and Fubara Egbono},
  title = {Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges
},
  journal = {International Journal of Intelligent Information Systems},
  volume = {13},
  number = {2},
  pages = {29-42},
  doi = {10.11648/j.ijiis.20241302.12},
  url = {https://doi.org/10.11648/j.ijiis.20241302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20241302.12},
  abstract = {This paper provides a thorough analysis of the use of electromyography (EMG) data in early stroke diagnosis and detection. Stroke continues to be a major global cause of disability and death, which emphasises the critical need for an accurate diagnosis made quickly to improve patient outcomes. Early detection is still difficult to achieve, even with improvements in medical imaging and testing technologies. By detecting minute variations in muscle activity linked to stroke symptoms, EMG data analysis offers a viable method for early stroke identification. The review delves into the diverse methodologies and strategies utilised to leverage EMG data for the purpose of stroke diagnosis, encompassing the application of deep learning models and machine learning algorithms. The paper proposes a structured framework for classifying approaches for early stroke detection and diagnosis using EMG data, providing a systematic way to categorize and compare different methodologies. The paper concludes by highlighting the revolutionary potential of EMG-based techniques in improving the diagnosis of strokes earlier and urging more study to address current issues and make clinical application easier.
},
 year = {2024}
}

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TY  - JOUR
T1  - Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges

AU  - Bob Chile-Agada
AU  - Laud Charles Ochei
AU  - Fubara Egbono
Y1  - 2024/04/17
PY  - 2024
N1  - https://doi.org/10.11648/j.ijiis.20241302.12
DO  - 10.11648/j.ijiis.20241302.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 29
EP  - 42
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20241302.12
AB  - This paper provides a thorough analysis of the use of electromyography (EMG) data in early stroke diagnosis and detection. Stroke continues to be a major global cause of disability and death, which emphasises the critical need for an accurate diagnosis made quickly to improve patient outcomes. Early detection is still difficult to achieve, even with improvements in medical imaging and testing technologies. By detecting minute variations in muscle activity linked to stroke symptoms, EMG data analysis offers a viable method for early stroke identification. The review delves into the diverse methodologies and strategies utilised to leverage EMG data for the purpose of stroke diagnosis, encompassing the application of deep learning models and machine learning algorithms. The paper proposes a structured framework for classifying approaches for early stroke detection and diagnosis using EMG data, providing a systematic way to categorize and compare different methodologies. The paper concludes by highlighting the revolutionary potential of EMG-based techniques in improving the diagnosis of strokes earlier and urging more study to address current issues and make clinical application easier.

VL  - 13
IS  - 2
ER  -

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Author Information

Bob Chile-Agada

Department of Computer Science, University of Port Harcourt, Port Harcourt, Nigeria

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Laud Charles Ochei

Department of Computer Science, University of Port Harcourt, Port Harcourt, Nigeria

Contact Email
Fubara Egbono

Department of Computer Science, University of Port Harcourt, Port Harcourt, Nigeria

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Plain Text BibTeX RIS

APA Style

Chile-Agada, B., Ochei, L. C., Egbono, F. (2024). Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges. International Journal of Intelligent Information Systems, 13(2), 29-42. https://doi.org/10.11648/j.ijiis.20241302.12

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ACS Style

Chile-Agada, B.; Ochei, L. C.; Egbono, F. Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges. Int. J. Intell. Inf. Syst. 2024, 13(2), 29-42. doi: 10.11648/j.ijiis.20241302.12

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AMA Style

Chile-Agada B, Ochei LC, Egbono F. Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges. Int J Intell Inf Syst. 2024;13(2):29-42. doi: 10.11648/j.ijiis.20241302.12

Copy | Download

@article{10.11648/j.ijiis.20241302.12,
  author = {Bob Chile-Agada and Laud Charles Ochei and Fubara Egbono},
  title = {Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges
},
  journal = {International Journal of Intelligent Information Systems},
  volume = {13},
  number = {2},
  pages = {29-42},
  doi = {10.11648/j.ijiis.20241302.12},
  url = {https://doi.org/10.11648/j.ijiis.20241302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20241302.12},
  abstract = {This paper provides a thorough analysis of the use of electromyography (EMG) data in early stroke diagnosis and detection. Stroke continues to be a major global cause of disability and death, which emphasises the critical need for an accurate diagnosis made quickly to improve patient outcomes. Early detection is still difficult to achieve, even with improvements in medical imaging and testing technologies. By detecting minute variations in muscle activity linked to stroke symptoms, EMG data analysis offers a viable method for early stroke identification. The review delves into the diverse methodologies and strategies utilised to leverage EMG data for the purpose of stroke diagnosis, encompassing the application of deep learning models and machine learning algorithms. The paper proposes a structured framework for classifying approaches for early stroke detection and diagnosis using EMG data, providing a systematic way to categorize and compare different methodologies. The paper concludes by highlighting the revolutionary potential of EMG-based techniques in improving the diagnosis of strokes earlier and urging more study to address current issues and make clinical application easier.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Evaluation of Approaches for Early Stroke Detection and Diagnosis Using EMG Data: Features, Techniques, and Challenges

AU  - Bob Chile-Agada
AU  - Laud Charles Ochei
AU  - Fubara Egbono
Y1  - 2024/04/17
PY  - 2024
N1  - https://doi.org/10.11648/j.ijiis.20241302.12
DO  - 10.11648/j.ijiis.20241302.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 29
EP  - 42
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20241302.12
AB  - This paper provides a thorough analysis of the use of electromyography (EMG) data in early stroke diagnosis and detection. Stroke continues to be a major global cause of disability and death, which emphasises the critical need for an accurate diagnosis made quickly to improve patient outcomes. Early detection is still difficult to achieve, even with improvements in medical imaging and testing technologies. By detecting minute variations in muscle activity linked to stroke symptoms, EMG data analysis offers a viable method for early stroke identification. The review delves into the diverse methodologies and strategies utilised to leverage EMG data for the purpose of stroke diagnosis, encompassing the application of deep learning models and machine learning algorithms. The paper proposes a structured framework for classifying approaches for early stroke detection and diagnosis using EMG data, providing a systematic way to categorize and compare different methodologies. The paper concludes by highlighting the revolutionary potential of EMG-based techniques in improving the diagnosis of strokes earlier and urging more study to address current issues and make clinical application easier.

VL  - 13
IS  - 2
ER  -

Copy | Download