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Improved method for analyzing electrical data obtained from EEG for better diagnosis of brain related disorders

  • 1174: Futuristic Trends and Innovations in Multimedia Systems Using Big Data, IoT and Cloud Technologies (FTIMS)
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Abstract

Interpretation of data obtained from electroencephalogram (EEG) has been commonly used for studying the condition of the brain and to diagnose any abnormalities. However, it is a common occurrence that different characteristic waves from EEG may overlap each other which may cause inaccuracies leading to wrong interpretation and hence incorrect diagnosis. In this paper, we present a modified approach to analyze the EEG signals differently. Firstly, the data is normalized, and then divided into three different ranges of equal intervals. This is done for each characteristic wave of EEG data. We have applied the proposed approach to brainwave datasets taken from Kaggle and IEEE dataport. The proposed method helps in estimating the current condition of the brain with higher accuracy, due to quantified contribution of different waves. The presented approach is expected to eliminate any errors, which may exist presently in the diagnosis of brain-related diseases and disorders. The discussed approach presented in this paper is dependent on data analysis, and it does not depends on the way of conducting the EEG tests. Hence, it is extendable to other disorders of the human body. The proposed approach finds many applications to improve the accuracy of brain related disorders by repudiating data overlap by 0.01% improvements.

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References

  1. Abásolo D, Hornero R, Escudero J, Espino P (2008) A study on the possible usefulness of detrended fluctuation analysis of the electroencephalogram background activity in Alzheimer’s disease. IEEE Trans Biomed Eng 55(9):2171–2179

    Article  Google Scholar 

  2. Abhang PA, Gawali BW, Mehrotra SC (2016) Technological basics of EEG recording and operation of apparatus. Introduction to EEG-and Speech-Based Emotion Recognition, 19–50

  3. Accardo AP, Pensiero S, Perissutti P (2004). Saccadic parameters for early identification of neuronopathic Gaucher’s disease. In The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Vol. 1, pp. 699–702). IEEE.

  4. Akbari H, Sadiq MT, Rehman AU (2021) Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain. Health Inf Sci Syst 9:9. https://doi.org/10.1007/s13755-021-00139-7

    Article  Google Scholar 

  5. Alturki FA; AlSharabi K; Abdurraqeeb AM; Aljalal M (2020) EEG Signal Analysis for Diagnosing Neurological Disorders Using Discrete Wavelet Transform and Intelligent Techniques Sensors 20, no. 9: 2505. https://doi.org/10.3390/s20092505.

  6. Awanti R, Bhyri C, Vanjerkhede K. Brain disorder analysis using EEG. International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE), ISSN: 0976–1353 Volume 23 Issue 6 –OCTOBER 2016 (SPECIAL ISSUE), 54–57.

  7. Bauer S, May C, Dionysiou D, Stamatakos G, Buchler P, Reyes M (2011) Multiscale modeling for image analysis of brain tumor studies. IEEE Trans Biomed Eng 59(1):25–29

    Article  Google Scholar 

  8. Baumann SB, Wozny DR, Kelly SK, Meno FM (1997) The electrical conductivity of human cerebrospinal fluid at body temperature. IEEE Trans Biomed Eng 44(3):220–223

    Article  Google Scholar 

  9. Bhandari A, Bansal A, Singh A, Sinha N (2017) Transport of liposome encapsulated drugs in voxelized computational model of human brain tumors. IEEE Trans Nanobiosci 16(7):634–644

    Article  Google Scholar 

  10. Chen P, Liu Q, Wei L, Zhao B, Jia Y, Lv H, Fei X (2019) Automatically structuring on Chinese ultrasound report of cerebrovascular diseases via natural language processing. IEEE Access 7:89043–89050

    Article  Google Scholar 

  11. Conte R, Tonacci A, Sansone F, Scudellari MC, Pala AP, Grande A, ... & Giorgolo F (2019) NeuroExam: a tool for neurological examination in neuromuscular diseases. In 2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT) (pp. 5–10). IEEE

  12. Dai Y, Tang Z, Wang Y (2019) Data driven intelligent diagnostics for Parkinson’s disease. IEEE Access 7:106941–106950

    Article  Google Scholar 

  13. Damhorst GL, Murtagh M, Rodriguez WR, Bashir R (2015) Microfluidics and nanotechnology for detection of global infectious diseases. Proc IEEE 103(2):150–160

    Article  Google Scholar 

  14. De Leener B, Cohen-Adad J, Kadoury S (2015) Automatic segmentation of the spinal cord and spinal canal coupled with vertebral labeling. IEEE Trans Med Imaging 34(8):1705–1718

    Article  Google Scholar 

  15. Deivasigamani S, Senthilpari C, Yong WH (2021) Machine learning method based detection and diagnosis for epilepsy in EEG signal. J Ambient Intell Human Comput 12:4215–4221. https://doi.org/10.1007/s12652-020-01816-3

    Article  Google Scholar 

  16. Demirhan A (2017) Random forests based recognition of the clinical labels using brain MRI scans. In 2017 3rd International Conference on Frontiers of Signal Processing (ICFSP) (pp. 156–159). IEEE

  17. Dubey AK (2021) Optimized hybrid learning for multi disease prediction enabled by lion with butterfly optimization algorithm. Sādhanā 46:63. https://doi.org/10.1007/s12046-021-01574-8

    Article  MathSciNet  Google Scholar 

  18. Dubey AK, Kolhe ML, Singh VK (2020) Human computer interactive future framework: automation of human interaction and interfaces. Intl J Comput Vision Robot 10(5):426–448

    Article  Google Scholar 

  19. Durongbhan P, Zhao Y, Chen L, Zis P, De Marco M, Unwin ZC, … Blackburn DJ (2019) A dementia classification framework using frequency and time-frequency features based on EEG signals. IEEE Trans Neural Syst Rehab Eng 27(5):826–835

    Article  Google Scholar 

  20. Facchin S, Miled MA, Sawan M (2015) In-channel constriction valve for cerebrospinal fluid sampling. IEEE Trans Magn 51(3):1–4

    Article  Google Scholar 

  21. Gumaei A, Hassan MM, Hassan MR, Alelaiwi A, Fortino G (2019) A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access 7:36266–36273

    Article  Google Scholar 

  22. Hawley LL, Rector NA, DaSilva A, Laposa JM, Richter MA (2021) Technology supported mindfulness for obsessive compulsive disorder: Self-reported mindfulness and EEG correlates of mind wandering, Behaviour Research and Therapy, vol. 136, 103757, ISSN 0005-7967, https://doi.org/10.1016/j.brat.2020.103757.

  23. Hu PJH, Zeng D, Chen H, Larson C, Chang W, Tseng C, Ma J (2007) System for infectious disease information sharing and analysis: design and evaluation. IEEE Trans Inf Technol Biomed 11(4):483–492

    Article  Google Scholar 

  24. Islam A, Reza SM, Iftekharuddin KM (2013) Multifractal texture estimation for detection and segmentation of brain tumors. IEEE Trans Biomed Eng 60(11):3204–3215

    Article  Google Scholar 

  25. Jing-Shan H, Yang L, Bin-Qiang C, Chuang L, Bin Y (2020) “An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks, Frontiers in Neuroscience, Vol 14, Page: 808, ISSN: 1662-453X, doi: https://doi.org/10.3389/fnins.2020.00808.

  26. Kakouri AC, Christodoulou CC, Zachariou M, Oulas A, Minadakis G, Demetriou CA, Spyrou GM (2018) Revealing clusters of connected pathways through multisource data integration in Huntington’s disease and spastic Ataxia. IEEE J Biomed Health Inf 23(1):26–37

    Article  Google Scholar 

  27. Kang SJ, Rouhollahi A, Farmanzad F (2010) Numerical investigation of mechanical interaction of cerebrospinal fluid and brain tissue. In 2010 17th Iranian Conference of Biomedical Engineering (ICBME) (pp. 1–4). IEEE

  28. Khoo MC, Oliveira FM, Cheng L (2012) Understanding the metabolic syndrome: a modeling perspective. IEEE Rev Biomed Eng 6:143–155

    Article  Google Scholar 

  29. Kropotov JD (2009) chapter 4 - frontal midline Theta rhythm, editor(s): Juri D. Kropotov, Quantitative EEG, Event-Related Potentials and Neurotherapy, Academic Press, Pages 77–95, ISBN: 9780123745125, https://doi.org/10.1016/B978-0-12-374512-5.00004-9.

  30. Ladino LD, Téllez-Zenteno JF (2019) Epilepsy and obesity: a complex interaction. In The comorbidities of epilepsy (pp. 131–158). Academic Press

  31. Lee M, Kim JW, Jang B (2018) DOVE: an infectious disease outbreak statistics visualization system. IEEE Access 6:47206–47216

    Article  Google Scholar 

  32. Linninger AA, Xenos M, Zhu DC, Somayaji MR, Kondapalli S, Penn RD (2007) Cerebrospinal fluid flow in the normal and hydrocephalic human brain. IEEE Trans Biomed Eng 54(2):291–302

    Article  Google Scholar 

  33. Marmarelis VZ, Shin DC, Orme M, Zhang R (2013) Time-varying modeling of cerebral hemodynamics. IEEE Trans Biomed Eng 61(3):694–704

    Article  Google Scholar 

  34. Michel M, Fiebich BL, Kuzior H, Meixensberger S, Berger B, Maier S, Nickel K, Runge K, Denzel D, Pankratz B, Schiele MA, Domschke K, Van Elst LT, Endres D (2021) Increased GFAP concentrations in the cerebrospinal fluid of patients with unipolar depression. Transl Psychiatry 11:308

    Article  Google Scholar 

  35. Padilla P, López M, Górriz JM, Ramirez J, Salas-Gonzalez D, Alvarez I (2011) NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease. IEEE Trans Med Imaging 31(2):207–216

    Article  Google Scholar 

  36. Parveen R, Tazi S, Dubey AK (2017) Medical image defects investigation through reliability computing. In: Bhatia S., Mishra K., Tiwari S., Singh V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-10-3770-2_60.

  37. Pham TD (2017) Texture classification and visualization of time series of gait dynamics in patients with neuro-degenerative diseases. IEEE Trans Neural Syst Rehab Eng 26(1):188–196

    Article  Google Scholar 

  38. Prabhakar SK, Rajaguru H, Lee S (2020) A framework for schizophrenia EEG signal classification with nature inspired optimization algorithms. IEEE Access 8:39875–39897. https://doi.org/10.1109/ACCESS.2020.2975848

    Article  Google Scholar 

  39. Ramzan M, Dawn S (2019) Temporal measures for analysis of emotional states from human electroencephalography signals. In 2019 Twelfth International Conference on Contemporary Computing (IC3) (pp. 1–6). IEEE.

  40. Rashid A, Tahir S, Choudhury A (2014) Detection of brain tumor in EEG signals using independent component analysis. Intl J Appl Math Electron Comput 3(2):78–82

    Article  Google Scholar 

  41. Rollin G, Lages J, Shepelyansky DL (2019) World influence of infectious diseases from Wikipedia network analysis. IEEE Access 7:26073–26087

    Article  Google Scholar 

  42. Siuly S, Kabir E, Wang H, and Zhang Y, “Exploring Sampling in the Detection of Multicategory EEG Signals”, Computational and Mathematical Methods in Medicine, Hindawi, Volume 2015 |Article ID 576437 | https://doi.org/10.1155/2015/576437

  43. Syeda, F., Kumbhare, D., Baron, M. S., & Hadimani, R. L. (2019). Modeling of transcranial magnetic stimulation versus Pallidal deep brain stimulation for Parkinson’s disease. IEEE Trans Magn

  44. Tor HT, Ooi CP, Lim-Ashworth NSJ, Wei JKE, Jahmunah V, Oh SL, Rajendra Acharya U, Fung DSS (2021) Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals, Computer Methods and Programs in Biomedicine, Volume 200, 105941, ISSN 0169-2607, https://doi.org/10.1016/j.cmpb.2021.105941.

  45. Vaidya M, Flint RD, Wang PT, Barry A, Li Y, Ghassemi M, ... & Gallick S (2019) Hemicraniectomy in traumatic brain injury: a noninvasive platform to investigate high gamma activity for brain machine interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering

  46. VOGTLE, L.K. (2009) Pain in adults with cerebral palsy: impact and solutions. Dev Med Child Neurol 51:113–121. https://doi.org/10.1111/j.1469-8749.2009.03423.x

    Article  Google Scholar 

  47. Youn CH, Shim EB, Lim S, Cho YM, Hong HK, Choi YS, … Lee HK (2010) A cooperative metabolic syndrome estimation with high precision sensing unit. IEEE Trans Biomed Eng 58(3):809–813

    Article  Google Scholar 

  48. Zacharaki EI, Hogea CS, Biros G, Davatzikos C (2008) A comparative study of biomechanical simulators in deformable registration of brain tumor images. IEEE Trans Biomed Eng 55(3):1233–1236

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of CSE, ABES Engineering College, Ghaziabad, Uttar Pradesh, 201009, India

    Anil Kumar Dubey & Mala Saraswat

  2. Department of ECE, ABES Engineering College, Ghaziabad, Uttar Pradesh, 201009, India

    Raman Kapoor

  3. Department of ASH, ITS Engineering College, Greater Noida, Gautam Buddha Nagar, India

    Shaweta Khanna

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  1. Anil Kumar Dubey
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  2. Mala Saraswat
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  3. Raman Kapoor
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  4. Shaweta Khanna
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Correspondence to Anil Kumar Dubey or Shaweta Khanna.

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Dubey, A.K., Saraswat, M., Kapoor, R. et al. Improved method for analyzing electrical data obtained from EEG for better diagnosis of brain related disorders. Multimed Tools Appl 81, 35223–35244 (2022). https://doi.org/10.1007/s11042-021-11826-8

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  • DOI: https://doi.org/10.1007/s11042-021-11826-8

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