A NEW COMPUTATIONAL APPROACH FRAMEWORK FOR THE DIAGNOSIS OF ALZHEIMER'S DISEASE

Year 2023, Volume: 8 Issue: 1, 19 - 39, 07.07.2023

Zülfikar Aslan

Abstract

Alzheimer's disease (AD) represents a significant neurological disorder with a wide prevalence worldwide, characterized by cognitive and behavioral deficits resulting from brain degeneration. Despite extensive research efforts, a cure for AD has yet to be found. However, detecting the disease at its early stages can aid in slowing down its progression. However, accurate diagnosis of AD involves costly and arduous testing procedures that necessitate the evaluation of an experienced specialist. To address this, a new computer-aided diagnosis (CAD) system with high performance has been proposed to automatically diagnose AD using EEG signals. The MSPCA method was used for preprocessing to eliminate existing noise, followed by the application of the TQWT signal decomposition technique to EEG data. The Hjorth parameters, derived from the recorded data, were obtained as distinctive attributes for subsequent analysis, and the resulting features were tested using various classification algorithms. The obtained features were evaluated using different statistical techniques to determine their classification performance in distinguishing AD patients from healthy individuals. The results revealed that the k-nearest neighbor (KNN) classifier yielded the highest classification performance of 99.78%±0.004. The methodological framework under investigation, which draws on the Tunable Q-factor Wavelet Transform (TQWT), was evaluated through the application of diverse signal separation methodologies. The results indicate that the proposed approach outperformed other techniques in AD diagnosis. As such, the present study puts forth a Computer-Aided Diagnosis (CAD) framework that holds promise in augmenting the proficiency of experts in the diagnosis of Alzheimer's disease.

Keywords

TQWT, EEG, Alzheimer Diagnosis, Hjorth Parameter

References

• [1] Bairagi, V. 2018. EEG signal analysis for early diagnosis of Alzheimer disease using spectral and wavelet based features. International Journal of Information Technology, 10(3), 403-412. https://doi.org/10.1007/S41870-018-0165-5.
• [2] Trambaiolli, L. R., Spolaôr, N., Lorena, A. C., Anghinah, R., & Sato, J. R. 2017. Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease. Clinical Neurophysiology, 128(10), 2058-2067. https://doi.org/10.1016/J.CLINPH.2017.06.251.
• [3] Blank, R. H., & Blank, R. H. 2019. Alzheimer’s disease and other dementias: An introduction. Social & Public Policy of Alzheimer's Disease in the United States, 1-26. https://doi.org/10.1007/978-981-13-0656-3_1.
• [4] Kulkarni, N. N., & Bairagi, V. K. 2017. Extracting salient features for EEG-based diagnosis of Alzheimer's disease using support vector machine classifier. IETE Journal of Research, 63(1), 11-22. https://doi.org/10.1080/03772063.2016.1241164.
• [5] Ruiz-Gómez, S. J., Gómez, C., Poza, J., Gutiérrez-Tobal, G. C., Tola-Arribas, M. A., Cano, M., & Hornero, R. 2018. Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment. Entropy, 20(1), 35. https://doi.org/10.3390/e20010035.
• [6] Amezquita-Sanchez, J. P., Mammone, N., Morabito, F. C., Marino, S., & Adeli, H. 2019. A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals. Journal of neuroscience methods, 322, 88-95. https://doi.org/10.1016/j.jneumeth.2019.04.013
• [7] Kulkarni, N. 2018. Use of complexity based features in diagnosis of mild Alzheimer disease using EEG signals. International Journal of Information Technology, 10(1), 59-64. https://doi.org/10.1007/s41870-017-0057-0
• [8] Tzimourta, K. D., Giannakeas, N., Tzallas, A. T., Astrakas, L. G., Afrantou, T., Ioannidis, P., ... & Tsipouras, M. G. 2019. EEG window length evaluation for the detection of Alzheimer’s disease over different brain regions. Brain sciences, 9(4), 81. https://doi.org/10.3390/brainsci9040081
• [9] Safi, M. S., & Safi, S. M. M. 2021. Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters. Biomedical Signal Processing and Control, 65, 102338. https://doi.org/10.1016/j.bspc.2020.102338
• [10] Liu, J., Zhang, C., Zhu, Y., Ristaniemi, T., Parviainen, T., & Cong, F. 2020. Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition. Computer methods and programs in biomedicine, 184, 105120. https://doi.org/10.1016/j.cmpb.2019.105120
• [11] Zeng, W., Yuan, J., Yuan, C., Wang, Q., Liu, F., & Wang, Y. 2021. A novel technique for the detection of myocardial dysfunction using ECG signals based on hybrid signal processing and neural networks. Soft Computing, 25, 4571-4595. https://doi.org/10.1007/s00500-020-05465-8
• [12] Murugappan, M., Alshuaib, W., Bourisly, A. K., Khare, S. K., Sruthi, S., & Bajaj, V. 2020. Tunable Q wavelet transform based emotion classification in Parkinson’s disease using Electroencephalography. Plos one, 15(11), e0242014. https://doi.org/10.1371/journal.pone.0242014
• [13] Patidar, S., Pachori, R. B., Upadhyay, A., & Acharya, U. R. 2017. An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism. Applied Soft Computing, 50, 71-78. https://doi.org/10.1016/j.asoc.2016.11.002
• [14] Patidar, S., & Panigrahi, T. 2017. Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals. Biomedical Signal Processing and Control, 34, 74-80. https://doi.org/10.1016/j.bspc.2017.01.001
• [15] Patidar, S., Pachori, R. B., & Acharya, U. R. 2015. Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals. Knowledge-based systems, 82, 1-10. https://doi.org/10.1016/j.knosys.2015.02.011
• [16] Hassan, A. R., Siuly, S., & Zhang, Y. 2016. Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Computer methods and programs in biomedicine, 137, 247-259. https://doi.org/10.1016/j.cmpb.2016.09.008
• [17] Taran, S., & Bajaj, V. 2019. Motor imagery tasks-based EEG signals classification using tunable-Q wavelet transform. Neural Computing and Applications, 31, 6925-6932. https://doi.org/10.1007/s00521-018-3531-0
• [18] Bajaj, V., Taran, S., Khare, S. K., & Sengur, A. 2020. Feature extraction method for classification of alertness and drowsiness states EEG signals. Applied Acoustics, 163, 107224. https://doi.org/10.1016/j.apacoust.2020.107224
• [19] Safi, M. S., & Safi, S. M. M. 2021. Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters. Biomedical Signal Processing and Control, 65, 102338. https://doi.org/10.1016/j.bspc.2020.102338
• [20] Aslan, Z. 2021. Migraine detection from EEG signals using tunable Q-factor wavelet transform and ensemble learning techniques. Physical and Engineering Sciences in Medicine, 44(4), 1201-1212. https://doi.org/10.1007/s13246-021-01055-6
• [21] Leite, J. P. R., & Moreno, R. L. 2018. Heartbeat classification with low computational cost using Hjorth parameters. IET Signal Processing, 12(4), 431-438. https://doi.org/10.1049/iet-spr.2017.0296
• [22] Rizal, A., Hidayat, R., & Nugroho, H. A. 2019. Comparison of multi-distance signal level difference Hjorth descriptor and its variations for lung sound classifications. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 7(2), 345-356. http://dx.doi.org/10.52549/ijeei.v7i2.771
• [23] Rizal, A., Hidayat, R., & Nugroho, H. A. 2015. Determining lung sound characterization using Hjorth descriptor. In 2015 International conference on control, electronics, renewable energy and communications (ICCEREC). 54-57. https://doi.org/10.1109/ICCEREC.2015.7337053
• [24] Chow, J. C., Ouyang, C. S., Chiang, C. T., Yang, R. C., Wu, R. C., Wu, H. C., & Lin, L. C. 2019. Novel method using Hjorth mobility analysis for diagnosing attention-deficit hyperactivity disorder in girls. Brain and Development, 41(4), 334-340. https://doi.org/10.1016/j.braindev.2018.11.006
• [25] Cecchin, T., Ranta, R., Koessler, L., Caspary, O., Vespignani, H., & Maillard, L. 2010. Seizure lateralization in scalp EEG using Hjorth parameters. Clinical neurophysiology, 121(3), 290-300. https://doi.org/10.1016/j.clinph.2009.10.033
• [26] Vidaurre, C., Krämer, N., Blankertz, B., & Schlögl, A. 2009. Time domain parameters as a feature for EEG-based brain–computer interfaces. Neural Networks, 22(9), 1313-1319. https://doi.org/10.1016/j.neunet.2009.07.020
• [27] Pineda, A. M., Ramos, F. M., Betting, L. E., & Campanharo, A. S. 2020. Quantile graphs for EEG-based diagnosis of Alzheimer’s disease. Plos one, 15(6). https://doi.org/10.1371/journal.pone.0231169
• [28] Liu, K. H., & Huang, D. S. 2008. Cancer classification using rotation forest. Computers in biology and medicine, 38(5), 601-610. https://doi.org/10.1016/j.compbiomed.2008.02.007
• [29] Patidar, S., & Pachori, R. B. 2014. Classification of cardiac sound signals using constrained tunable-Q wavelet transform. Expert Systems with Applications, 41(16), 7161-7170. https://doi.org/10.1016/j.eswa.2014.05.052
• [30] Selesnick, I. W. 2011. Wavelet transform with tunable Q-factor. IEEE transactions on signal processing, 59(8), 3560-3575. https://doi.org/10.1109/TSP.2011.2143711
• [31] He, W., Zi, Y., Chen, B., Wu, F., & He, Z. 2015. Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform. Mechanical Systems and Signal Processing, 54, 457-480. https://doi.org/10.1016/j.ymssp.2014.09.007
• [32] Hjorth, B. 1970. EEG analysis based on time domain properties. Electroencephalography and clinical neurophysiology, 29(3), 306-310. https://doi.org/10.1016/0013-4694(70)90143-4
• [33] Kim, T. K. 2015. T test as a parametric statistic. Korean journal of anesthesiology, 68(6), 540-546. https://doi.org/10.4097/kjae.2015.68.6.540
• [34] Cuzick, J. 1985. A Wilcoxon‐type test for trend. Statistics in medicine, 4(1), 87-90. https://doi.org/10.1002/sim.4780040112
• [35] Massey Jr, F. J. 1951. The Kolmogorov-Smirnov test for goodness of fit. Journal of the American statistical Association, 46(253), 68-78. https://doi.org/10.1080/01621459.1951.10500769
• [36] World Health Organization. 2012. Dementia: a public health priority. World Health Organization.
• [37] Jeong, J. 2004. EEG dynamics in patients with Alzheimer's disease. Clinical neurophysiology, 115(7), 1490-1505. https://doi.org/10.1016/j.clinph.2004.01.001
• [38] Alberdi, A., Aztiria, A., & Basarab, A. 2016. On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey. Artificial intelligence in medicine, 71, 1-29. https://doi.org/10.1016/j.artmed.2016.06.003
• [39] Cassani, R., Estarellas, M., San-Martin, R., Fraga, F. J., & Falk, T. H. 2018. Systematic review on resting-state EEG for Alzheimer's disease diagnosis and progression assessment. Disease markers. https://doi.org/10.1155/2018/5174815
• [40] Dauwels, J., Vialatte, F., & Cichocki, A. 2010. Diagnosis of Alzheimer's disease from EEG signals: where are we standing?. Current Alzheimer Research, 7(6), 487-505. https://doi.org/10.2174/156720510792231720
• [41] Oltu, B., Akşahin, M. F., & Kibaroğlu, S. (2021). A novel electroencephalography based approach for Alzheimer’s disease and mild cognitive impairment detection. Biomedical Signal Processing and Control, 63, 102223. https://doi.org/10.1016/j.bspc.2020.102223