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2025 | OriginalPaper | Buchkapitel

Mental Health Assessment Using EEG Sensor and Machine Learning

verfasst von : Man Singh, Chetan Vyas, Bireshwar Dass Mazumdar

Erschienen in: Innovative Computing and Communications

Verlag: Springer Nature Singapore

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Abstract

Heart failure stands as a significant public health issue with the high mortality and morbidity rates. Timely anticipation and detection of heart failure play a vital role in facilitating prompt intervention and improved prognoses. Recently, machine learning methodologies have emerged as auspicious tools. Machine learning approaches have emerged as promising tools for predicting and diagnosing heart failure in prognosticating and identifying heart failure cases. This survey paper presents an extensive overview of the extant body of literature concerning machine learning cantered strategies for the early prognosis and diagnosis of heart failure. We scout into exploration of diverse ML techniques that are frequently employed in heart failure prognosis and diagnosis, including artificial neutral network, random forest logistic regression, support vector machine models and deep learning. Furthermore, an examination of the datasets and evaluation metrics used to calculate the efficiency of machine learning models in prognosis and diagnosing heart failure is presented. Our investigation culminates in a synthesis of the principle discoveries compiled from the reviewed literature. We undertake a comparative analysis of performance exhibited by distinct ML technologies, while also addressing the obstacles and constraint inherent in that the utilization of machine learning for heart failure prognosis and diagnosis. By offering this survey article, we furnish valuable asset of researchers and medical practitioners who possess a wasted interest in elevating machine learning techniques for the early prognosis and diagnosis of heart failure.

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Literatur
1.
Zurück zum Zitat Ahsan, M. M., & Siddique, Z. (2021). Machine learning-based heart disease diagnosis: A systematic literature review. Artificial Intelligence in Medicine, 128. Ahsan, M. M., & Siddique, Z. (2021). Machine learning-based heart disease diagnosis: A systematic literature review. Artificial Intelligence in Medicine, 128.
2.
Zurück zum Zitat Obasi, T., & Shafiq, M. O. (2019). Towards comparing and using machine learning techniques for detecting and predicting heart attack and diseases (pp. 2393–2402). Obasi, T., & Shafiq, M. O. (2019). Towards comparing and using machine learning techniques for detecting and predicting heart attack and diseases (pp. 2393–2402).
3.
Zurück zum Zitat Chicco, D., & Jurman, G. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC medical informatics and decision making. Chicco, D., & Jurman, G. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC medical informatics and decision making.
4.
Zurück zum Zitat Ma, L. Y., Chen, W. W., Gao, R. L., Liu, L. S., Zhu, M. L., Wang, Y. J., Wu, Z.S., Li, H.J., Gu, D.F., Yang, Y.J., Zheng, Z., & Hu, S. S. (2018). China cardiovascular diseases report. PMCID. Ma, L. Y., Chen, W. W., Gao, R. L., Liu, L. S., Zhu, M. L., Wang, Y. J., Wu, Z.S., Li, H.J., Gu, D.F., Yang, Y.J., Zheng, Z., & Hu, S. S. (2018). China cardiovascular diseases report. PMCID.
5.
Zurück zum Zitat Rajagopalan, S., Al-Kindi, S. G., & Brook, R. D. (2018). Air pollution and cardiovascular disease. Journal of the American College of Cardiology, 23, 2054–2070. Rajagopalan, S., Al-Kindi, S. G., & Brook, R. D. (2018). Air pollution and cardiovascular disease. Journal of the American College of Cardiology, 23, 2054–2070.
6.
Zurück zum Zitat Subbalakshmi, G., Ramesh, K., & Rao, M. C. (2011). Decision support in heart disease prediction system using naive Bayes. IJCSE, 2, 170–176. Subbalakshmi, G., Ramesh, K., & Rao, M. C. (2011). Decision support in heart disease prediction system using naive Bayes. IJCSE, 2, 170–176.
8.
Zurück zum Zitat Ponikowski, P., Anker, S. D., AlHabib, K. F., Cowie, M. R., Force, T. L., Hu, S., Jaarsma, T., Krum, H., Rastogi, V., Rohde, L. E., Samal, U. C., & Filippatos, G. (2014). Heart failure: Preventing disease and death worldwide. ESC Heart Failure, 1. https://doi.org/10.1002/ehf2.12005 Ponikowski, P., Anker, S. D., AlHabib, K. F., Cowie, M. R., Force, T. L., Hu, S., Jaarsma, T., Krum, H., Rastogi, V., Rohde, L. E., Samal, U. C., & Filippatos, G. (2014). Heart failure: Preventing disease and death worldwide. ESC Heart Failure, 1. https://​doi.​org/​10.​1002/​ehf2.​12005
12.
13.
Zurück zum Zitat Al-Tashi, Q., Saad, M. B., Muneer, A., Qureshi, R., Mirjalili, S., Sheshadri, A., Le, X., Vokes, N. I., Zhang, J., & Wu, J. (2023). Models for the identification of prognostic and predictive cancer biomarkers: A systematic review. International Journal of Molecular Sciences, 24. https://doi.org/10.3390/ijms24097781 Al-Tashi, Q., Saad, M. B., Muneer, A., Qureshi, R., Mirjalili, S., Sheshadri, A., Le, X., Vokes, N. I., Zhang, J., & Wu, J. (2023). Models for the identification of prognostic and predictive cancer biomarkers: A systematic review. International Journal of Molecular Sciences, 24. https://​doi.​org/​10.​3390/​ijms24097781
16.
Zurück zum Zitat O’Kelly, A. C., Michos, E. D., Shufelt, C. L., Vermunt, J. V., Minissian, M. B., Quesada, O., Smith, G. N., Rich-Edwards, J. W., Garovic, V. D., El Khoudary, S. R., & Honigberg, M. C. (2022). Pregnancy and reproductive risk factors for cardiovascular disease in women. ESC Heart Failure, 130. https://doi.org/10.1161/CIRCRESAHA.121.319895 O’Kelly, A. C., Michos, E. D., Shufelt, C. L., Vermunt, J. V., Minissian, M. B., Quesada, O., Smith, G. N., Rich-Edwards, J. W., Garovic, V. D., El Khoudary, S. R., & Honigberg, M. C. (2022). Pregnancy and reproductive risk factors for cardiovascular disease in women. ESC Heart Failure, 130. https://​doi.​org/​10.​1161/​CIRCRESAHA.​121.​319895
17.
Zurück zum Zitat Jenča, D., Melenovský, V., Stehlik, J., Staněk, V., Kettner, J., Kautzner, J., Adámková, V., & Wohlfahrt, P. (2021). Heart failure after myocardial infarction: Incidence and predictors. ESC Heart Failure, 8. https://doi.org/10.1002/ehf2.13144 Jenča, D., Melenovský, V., Stehlik, J., Staněk, V., Kettner, J., Kautzner, J., Adámková, V., & Wohlfahrt, P. (2021). Heart failure after myocardial infarction: Incidence and predictors. ESC Heart Failure, 8. https://​doi.​org/​10.​1002/​ehf2.​13144
Metadaten
Titel
Mental Health Assessment Using EEG Sensor and Machine Learning
verfasst von
Man Singh
Chetan Vyas
Bireshwar Dass Mazumdar
Copyright-Jahr
2025
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-4152-6_13