2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 2 , PP: 315-327, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
K. Satyanarayana Murthy 1 * , Suribabu Korada 2
Doi: https://doi.org/10.54216/FPA.190223
In medical diagnosis and prognosis, symptoms provided by patients play a critical role in identifying diseases. Machine learning offers a powerful approach to analyzing and predicting illnesses based on these symptoms. In particular, classification algorithms are widely used to analyze input data and predict disease outcomes. A key factor in effective classification is the selection of relevant attributes, which directly affects the accuracy of the prediction. This research emphasizes the importance of proper feature extraction techniques in the context of disease prediction using biomedical signal analysis. Effective analysis requires both the extraction of critical features and the elimination of irrelevant data. The aim of this study is to explore existing approaches to disease prediction based on biomedical signal analysis. We focus on feature extraction from pre-processed data, which aids in distinguishing between different biomedical signals recorded by medical devices. Our objective is to identify biomedical cues that differentiate various health conditions. Examples of such signals include electroencephalogram (EEG), electrocardiogram (ECG), and electrogastrogram (EGG). Understanding how these signals differ between healthy and diseased states is crucial for accurate disease prediction. This research investigates diseases such as heart disease, kidney failure, and lung infections, considering how variations in biomedical signals can be used to predict the likelihood of severe illness. We continue to seek advancements in predicting and mitigating future health risks
Machine Learning , Signal Processing , Electroencephalogram (EEG)
[1] H. Yadav and S. Maini, "Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 82, pp. 47003-47047, 2023.
[2] A. Cataldo, S. Criscuolo, E. De Benedetto, A. Masciullo, M. Pesola, and R. Schiavoni, "Uncovering the correlation between COVID-19 and neurodegenerative processes: Toward a new approach based on EEG entropic analysis," Bioengineering, vol. 10, no. 4, p. 435, 2023.
[3] O. E. Korkmaz, O. Aydemir, E. A. Oral, and I. Y. Ozbek, "Investigating the effect of COVID-19 infection on P300 based BCI application performance," Traitement du Signal, vol. 38, no. 6, pp. 1767-1773, 2021.
[4] G. B. Tantillo et al., "Electroencephalography at the height of a pandemic: EEG findings in patients with COVID-19," Clin. Neurophysiol., vol. 137, pp. 102-112, May 2022.
[5] Y. Yao et al., "Study on brain damage patterns of COVID-19 patients based on EEG signals," Front. Hum. Neurosci., vol. 17, p. 1280362, 2023.
[6] L. A. Corazza et al., "Electroencephalographic findings among inpatients with COVID-19 in a tertiary hospital from a middle-income country," Arq. Neuropsiquiatr., vol. 79, no. 4, pp. 315-320, 2021.
[7] I. Sáez-Landete et al., "Retrospective analysis of EEG in patients with COVID-19: EEG recording in acute and follow-up phases," Clin. EEG Neurosci., vol. 53, no. 3, pp. 215-228, May 2022.
[8] Y. Yang, T. Yu, and J. Yang, "Clinical manifestations and EEG findings in children infected with COVID-19 and exhibiting neurological symptoms," BMC Pediatr., vol. 24, p. 49, 2024.
[9] B. Gogia et al., "EEG characteristics in COVID-19 survivors and non-survivors with seizures and encephalopathy," Cureus, vol. 13, no. 10, p. e18476, 2021.
[10] O. Karadas, B. Ozturk, and A. R. Sonkaya, "EEG changes in intensive care patients diagnosed with COVID-19: A prospective clinical study," Neurol. Sci., vol. 43, pp. 2277–2283, 2022.
[11] A. R. Antony and Z. Haneef, "Systematic review of EEG findings in 617 patients diagnosed with COVID-19," Seizure: Eur. J. Epilepsy, vol. 83, pp. 234–241, 2020.
[12] L. Marinelli et al., "The value of EEG attenuation in the prediction of outcome in COVID-19 patients," Neurol. Sci., vol. 43, pp. 6159–6166, 2022.
[13] C. Tsai, S. E. Wilson, and C. Rubinos, "SARS-CoV-2 infection and seizures: The perfect storm," J. Integr. Neurosci., vol. 21, no. 4, p. 115, 2022.
[14] W. Guan et al., "Clinical characteristics of coronavirus disease 2019 in China," N. Engl. J. Med., vol. 382, pp. 1708-1720, 2020.
[15] L. Mao et al., "Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China," JAMA Neurol., 2020.
[16] A. Varatharaj et al., "Neurological and neuropsychiatric complications of COVID-19 in 153 patients: A UK-wide surveillance study," Lancet Psychiatry, 2020.
[17] M. U. Ahmed et al., "Neurological manifestations of COVID-19 (SARS-CoV-2): A review," Front. Neurol., vol. 11, p. 518, 2020.
[18] M. A. Ellul et al., "Neurological associations of COVID-19," Lancet Neurol., vol. 4422, pp. 2-3, 2020.
[19] A. S. Galanopoulou et al., "EEG findings in acutely ill patients investigated for SARS-CoV-2/COVID-19: A small case series preliminary report," Epilepsia Open, vol. 5, pp. 314–324, 2020.
[20] E. Pasini et al., "EEG findings in COVID-19 related encephalopathy," Clin. Neurophysiol., vol. 131, pp. 2265–2277, 2020.
[21] J. Pastor, L. Vega-Zelaya, and E. Martín Abad, "Specific EEG encephalopathy pattern in SARS-CoV-2 patients," J. Clin. Med., 2020.
[22] L. J. Hirsch et al., "American clinical neurophysiology society’s standardized critical care EEG terminology: 2012 version," J. Clin. Neurophysiol., vol. 30, pp. 1–27, 2013.
[23] J. Lau, J. P. A. Ioannidis, and C. H. Schmid, "Quantitative synthesis in systematic reviews," Ann. Intern. Med., vol. 127, pp. 820–826, 1997.
[24] N. Ayub et al., "Clinical electroencephalography findings and considerations in hospitalized patients with coronavirus SARS-CoV-2," MedRxiv, 2020.
[25] S. Louis et al., "Continuous electroencephalography characteristics and acute symptomatic seizures in COVID-19 patients," Clin. Neurophysiol., vol. 131, pp. 2651–2661, 2020.
[26] L. J. W. Canham et al., "Electroencephalographic (EEG) features of encephalopathy in the setting of COVID-19: A case series," Clin. Neurophysiol. Pract., 2020.
[27] J. Pellinen et al., "Continuous EEG findings in patients with COVID-19 infection admitted to a New York academic hospital system," Epilepsia, 2020.
[28] M. S. Pilato et al., "EEG findings in coronavirus disease," J. Clin. Neurophysiol. Off. Publ. Am. Electroencephalogr. Soc., 2020.
[29] C. Delorme et al., "COVID-19-related encephalopathy: A case series with brain FDG-PET/CT findings," Eur. J. Neurol., 2020.
[30] M. Hepburn et al., "Acute symptomatic seizures in critically ill patients with COVID-19: Is there an association?," Neurocrit. Care, 2020.
[31] S. Mohammadi, F. Moosaie, and M. H. Aarabi, "Understanding the immunologic characteristics of neurologic manifestations of SARS-CoV-2 and potential immunological mechanisms," Mol. Neurobiol., vol. 57, 2020.
[32] G. Assenza et al., "Electroencephalography at the time of COVID-19 pandemic in Italy," Neurol. Sci., vol. 41, pp. 1999–2004, 2020.
[33] A. Emami et al., "Seizure in patients with COVID-19," Neurol. Sci., vol. 41, pp. 3057–3061, 2020.
[34] N. M. El-Kafrawy, D. Hegazy, and M. F. Tolba, "Features extraction and classification of EEG signals using empirical mode decomposition and support vector machine," in Adv. Mach. Learn. Technol. Appl., Springer, 2014, pp. 189–198.
[35] T. Liu and D. Yang, "A three-branch 3D convolutional neural network for EEG-based different hand movement stages classification," Sci. Rep., vol. 11, p. 10758, 2021.
[36] R. B. Vallabhaneni et al., "Deep learning algorithms in EEG signal decoding application: A review," IEEE Access, vol. 9, pp. 125778–125786, 2021.
