تشخیص خودکار وغیرتهاجمی سکته‌مغزی با استفاده از یک مدل جدید زمانی-فرکانسی سیگنال فشار کف پا

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران

2 دانشیار، دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران

چکیده

وقوع بیماری سکته‌مغزی به دلیل انحطاط ناگهانی سلول‌های مغزی است که این مساله ناشی از کمبود اکسیژن-رسانی به سلول‌ها در اثر انسداد عروقی و یا پارگی آن‌ها و قطع جریان خون است که می‌تواند منجر به اختلال در راه رفتن شود. در حال حاضر، تصویربرداری‌های مغزی شامل تصویربرداری رزونانس مغناطیسی، توموگرافی کامپیوتری و آنژیوگرافی مغزی ابزارهای اصلی تشخیص سکته‌مغزی هستند که نمی توانند یک تشخیص اقتصادی و غیرتهاجمی را تامین کنند. در این مطالعه با هدف ارائه روش‌ تشخیصی خودکار، غیرتهاجمی و کم هزینه برای سکته مغزی ایسکمیک از تحلیل کامپیوتری سیگنال فشار کف پا استفاده شده است. روش پیشنهادی مبتنی بر استخراج ویژگی‌های جدید زمانی-فرکانسی سیگنال فشار کف پا به کمک تجزیه موجک عامل Q قابل‌تنظیم، انتخاب ویژگی ReliefF و طبقه‌بندی ماشین بردار پشتیبان، K-نزدیک‌ترین همسایگی و جنگل تصادفی می‌باشد. ویژگی اصلی روش پیشنهادی قابلیت استخراج اجزای نوسانی و اطلاعات گذرای سیگنال غیرایستای فشار کف پا به کمک یک روش جدید زمانی-فرکانسی و امکان انطباق با خصوصیات متغیر با زمان آن می‌باشد. جهت بررسی صحت تشخیصی روش‌ پیشنهادی از مجموعه داده‌های سیگنال فشار بیماران مبتلا به سکته مغزی ایسکمیک در حین راه رفتن استفاده شده است که شامل 46 فرد سالم و 36 بیمارمی‌باشد. نتایج بدست آمده قابلیت تشخیصی بالای روش پیشنهادی را با تعداد 35 ویژگی ساده آماری با میانگین صحت 77/99% نشان داده‌اند. روش پیشنهادی قادر به ارائه مصالحه مناسب بین صحت تشخیصی بالا و هزینه محاسباتی پایین با استفاده از ویژگی‌های ساده آماری کف پا می‌باشد که برای کاربردهای عملی تشخیصی مناسب به نظر می‌رسد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Automatic and non-invasive method for Ischemic stroke detection using a novel time-frequency model of plantar pressure signals

نویسندگان [English]

  • Zahra Atrachali 1
  • Peyvand Ghaderyan 2
1 Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.
2 Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.
چکیده [English]

The occurrence of stroke is due to the sudden degeneration of brain cells, which is caused by the lack of oxygen supply to the cells due to vascular blockage or their rupture and the interruption of blood flow, which can lead to gait impairement. The brain imaging techniques including magnetic resonance imaging, computed tomography and cerebral angiography are the main tools for stroke detection, which may not provide a cost-effective and non-invasive diagnosis. Tthis study aims to propose an automatic, non-invasive and low-cost method for Ischemic stroke detection based on computer-aided analysis of the plantar pressure signals. The proposed method is based on new time-frequency plantar feature extraction based on Tunable Q-factor Wavelet Transform, informative feature selection based on ReliefF and classification based on Support Vector Machine, K-Nearest Neighborhood and Random Forest techniques. The main property of this method is the ability to extract fluctuating components and transient information of non-stationary plantar pressure signal using a new time-frequency method and the possibility of adapting to its time-varying characteristics. In order to evaluate the detection performance, the foot pressure signals of 36 patients afflicted with stroke and 46 healthy controls recorded during walking has been used. The obtained results have shown the high diagnostic capability of the proposed method with an average accuracy rate of 99.77% using 35 simple statistical features. The proposed method is able to provide a trade-off between high diagnostic accuracy and low computational cost using simple statistical plantar features, which seems suitable for practical diagnostic applications.

کلیدواژه‌ها [English]

  • Tunable Q-factor Wavelet Transform
  • Random Forest
  • ReliefF Feature Selection
  • Machine Learning

مراجع

[1]     R. S. da Silva et al., "Psychometric properties of wearable technologies to assess post-stroke gait parameters: a systematic review," Gait & Posture, 2024.
[2]     S. Chen et al., "MSA-YOLOv5: Multi-scale attention-based YOLOv5 for automatic detection of acute ischemic stroke from multi-modality MRI images," Computers in Biology and Medicine, vol. 165, p. 107471, 2023.
[3]     D. Joshi, A. Khajuria, and P. Joshi, "An automatic non-invasive method for Parkinson's disease classification," Computer methods and programs in biomedicine, vol. 145, pp. 135-145, 2017.
[4]     J. M. Melvin, The effects of heel height, shoe volume and upper stiffness on shoe comfort and plantar pressure. University of Salford (United Kingdom), 2014.
[5]     P. Ghaderyan and G. Fathi, "Inter-limb time-varying singular value: a new gait feature for Parkinson’s disease detection and stage classification," Measurement, vol. 177, p. 109249, 2021.
[6]     V. Novak et al., "Cerebral flow velocities during daily activities depend on blood pressure in patients with chronic ischemic infarctions," Stroke, 2010.
[7]     E. C. Lee et al., "Utility of exosomes in ischemic and hemorrhagic stroke diagnosis and treatment," International Journal of Molecular Sciences, vol. 23, no. 15, p. 8367, 2022.
[8]     Y. Zhang et al., "Detection of acute ischemic stroke and backtracking stroke onset time via machine learning analysis of metabolomics," Biomedicine & Pharmacotherapy, vol. 155, p. 113641, 2022.
[9]     G. Das and P. Kumar, "Potential key genes for predicting risk of stroke occurrence: A computational approach," Neuroscience Informatics, vol. 2, no. 2, p. 100068, 2022.
[10]   Y.-H. Wang et al., "Lumbrokinase regulates endoplasmic reticulum stress to improve neurological deficits in ischemic stroke," Neuropharmacology, vol. 221, p. 109277, 2022.
[11]   S. J. Park, I. Hussain, S. Hong, D. Kim, H. Park, and H. C. M. Benjamin, "Real-time gait monitoring system for consumer stroke prediction service," in 2020 IEEE International conference on consumer electronics (ICCE), IEEE, pp. 1-4, 2020.
[12]   S. S. Bidabadi, I. Murray, G. Y. F. Lee, S. Morris, and T. Tan, "Classification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms," Gait & posture, vol. 71, pp. 234-240, 2019.
[13]   S. M. G. Beyrami and P. Ghaderyan, "A robust, cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis," Measurement, vol. 156, p. 107579, 2020.
[14]   K. Echigoya, K. Okada, M. Wakasa, A. Saito, M. Kimoto, and A. Suto, "Changes to foot pressure pattern in post-stroke individuals who have started to walk independently during the convalescent phase," Gait & Posture, vol. 90, pp. 307-312, 2021.
[15]   C. Beyaert, R. Vasa, and G. E. Frykberg, "Gait post-stroke: Pathophysiology and rehabilitation strategies," Neurophysiologie Clinique/Clinical Neurophysiology, vol. 45, no. 4-5, pp. 335-355, 2015.
[16]   M. Jacquelin Perry, "Gait analysis: normal and pathological function," New Jersey: SLACK, 2010.
[17]   A. Sant’Anna and N. Wickström, "A symbol-based approach to gait analysis from acceleration signals: Identification and detection of gait events and a new measure of gait symmetry," IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 5, pp. 1180-1187, 2010.
[18]   S. Krishnan and Y. Athavale, "Trends in biomedical signal feature extraction," Biomedical Signal Processing and Control, vol. 43, pp. 41-63, 2018.
[19]   W. Wang, K. Li, N. Wei, C. Yin, and S. Yue, "Evaluation of postural instability in stroke patient during quiet standing," in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp. 2522-2525, 2017.
[20]   F. Valentini, B. Granger, D. Hennebelle, N. Eythrib, and G. Robain, "Repeatability and variability of baropodometric and spatio-temporal gait parameters–results in healthy subjects and in stroke patients," Neurophysiologie Clinique/Clinical Neurophysiology, vol. 41, no. 4, pp. 181-189, 2011.
[21]   K. Hirata et al., "Adaptive changes in foot placement for split-belt treadmill walking in individuals with stroke," Journal of Electromyography and Kinesiology, vol. 48, pp. 112-120, 2019.
[22]   P. Lopez-Meyer, G. D. Fulk, and E. S. Sazonov, "Automatic detection of temporal gait parameters in poststroke individuals," IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 4, pp. 594-601, 2011.
[23]   K. van Kammen, A. M. Boonstra, L. H. van der Woude, H. A. Reinders-Messelink, and R. den Otter, "Differences in muscle activity and temporal step parameters between Lokomat guided walking and treadmill walking in post-stroke hemiparetic patients and healthy walkers," Journal of neuroengineering and rehabilitation, vol. 14, pp. 1-11, 2017.
[24]   M. Munoz-Organero, J. Parker, L. Powell, and S. Mawson, "Assessing walking strategies using insole pressure sensors for stroke survivors," Sensors, vol. 16, no. 10, p. 1631, 2016.
[25]   K. J. Nolan, M. Yarossi, and P. Mclaughlin, "Changes in center of pressure displacement with the use of a foot drop stimulator in individuals with stroke," Clinical biomechanics, vol. 30, no. 7, pp. 755-761, 2015.
[26]   M. Muñoz-Organero, J. Parker, L. Powell, R. Davies, and S. Mawson, "Sensor optimization in smart insoles for post-stroke gait asymmetries using total variation and L 1 distances," IEEE Sensors Journal, vol. 17, no. 10, pp. 3142-3151, 2017.
[27]   C. Le Bocq, M. Rousseaux, N. Buisset, W. Daveluy, S. Blond, and E. Allart, "Effects of tibial nerve neurotomy on posture and gait in stroke patients: a focus on patient-perceived benefits in daily life," Journal of the Neurological Sciences, vol. 366, pp. 158-163, 2016.
[28]   A. Mannini, D. Trojaniello, A. Cereatti, and A. M. Sabatini, "A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington’s disease patients," Sensors, vol. 16, no. 1, p. 134, 2016.
[29]   E. Bergamini, M. Iosa, V. Belluscio, G. Morone, M. Tramontano, and G. Vannozzi, "Multi-sensor assessment of dynamic balance during gait in patients with subacute stroke," Journal of biomechanics, vol. 61, pp. 208-215, 2017.
[30]   M. Saljuqi and P. Ghaderyan, "A novel method based on matching pursuit decomposition of gait signals for Parkinson’s disease, Amyotrophic lateral sclerosis and Huntington’s disease detection," Neuroscience Letters, vol. 761, p. 136107, 2021.
[31]   R. Polikar, "The wavelet tutorial," ed, 1996.
[32]   H. S. Pal, A. Kumar, A. Vishwakarma, and M. K. Ahirwal, "Electrocardiogram signal compression using tunable-Q wavelet transform and meta-heuristic optimization techniques," Biomedical Signal Processing and Control, vol. 78, p. 103932, 2022.
[33]   S. C. KS, A. Mishra, V. Shirhatti, and S. Ray, "Comparison of matching pursuit algorithm with other signal processing techniques for computation of the time-frequency power spectrum of brain signals," Journal of Neuroscience, vol. 36, no. 12, pp. 3399-3408, 2016.
[34]   I. Selesnick, "Wavelet transform with tunable Q-factor IEEE transactions on signal processing. 2011 Aug; 8 (59) pp: 3560-75. doi: 10.1109," TSP, 2011.
[35]   S. Taran, V. Bajaj, G. Sinha, and K. Polat, "Detection of sleep apnea events using electroencephalogram signals," Applied Acoustics, vol. 181, p. 108137, 2021.
[36]   M. Baygin, "An accurate automated schizophrenia detection using TQWT and statistical moment based feature extraction," Biomedical Signal Processing and Control, vol. 68, p. 102777, 2021.
[37]   M. Li, S. Tian, L. Sun, and X. Chen, "Gait analysis for post-stroke hemiparetic patient by multi-features fusion method," Sensors, vol. 19, no. 7, p. 1737, 2019.
[38]   D. Yoo, Y. Son, D.-H. Kim, K.-H. Seo, and B.-C. Lee, "Technology-assisted ankle rehabilitation improves balance and gait performance in stroke survivors: a randomized controlled study with 1-month follow-up," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 12, pp. 2315-2323, 2018.
[39]   J. C. Dean, A. E. Embry, K. H. Stimpson, L. A. Perry, and S. A. Kautz, "Effects of hip abduction and adduction accuracy on post-stroke gait," Clinical Biomechanics, vol. 44, pp. 14-20, 2017.
[40]   P. Ghaderyan and S. M. G. Beyrami, "Neurodegenerative diseases detection using distance metrics and sparse coding: A new perspective on gait symmetric features," Computers in Biology and Medicine, vol. 120, p. 103736, 2020.
[41]   M.-G. Tan, J.-H. Ho, H.-T. Goh, H. K. Ng, L. A. Latif, and M. Mazlan, "A new fractal-based kinetic index to characterize gait deficits with application in stroke survivor functional mobility assessment," Biomedical Signal Processing and Control, vol. 52, pp. 403-413, 2019.
[42]   P. Ghaderyan and A. Abbasi, "A novel cepstral-based technique for automatic cognitive load estimation," Biomedical Signal Processing and Control, vol. 39, pp. 396-404, 2018.
[43]   M. Pourezzat and H. Danandeh Hesar, "Development of a New Adaptive Method Based on Empirical Fourier Decomposition for the Diagnosis of Obstructive Sleep Apnea Using Electrocardiogram Signal Analysis," TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, vol. 53, no. 3, pp. 159-170, 2023.
[44]   R. J. Urbanowicz, M. Meeker, W. La Cava, R. S. Olson, and J. H. Moore, "Relief-based feature selection: Introduction and review," Journal of biomedical informatics, vol. 85, pp. 189-203, 2018.
[45]   M. Wang et al., "Research on abnormal gait recognition algorithms for stroke patients based on array pressure sensing system," in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), IEEE, pp. 1560-1563, 2019.
[46]   S. Osowski, K. Siwek, and T. Markiewicz, "MLP and SVM networks-a comparative study," in Proceedings of the 6th Nordic Signal Processing Symposium, NORSIG 2004., 2004: IEEE, pp. 37-40, 2004.
[47]   S. Ray, "An analysis of computational complexity and accuracy of two supervised machine learning algorithms—K-nearest neighbor and support vector machine," in Data Management, Analytics and Innovation: Proceedings of ICDMAI 2020, Volume 1, Springer, pp. 335-347, 2021.
[48]   V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, "An assessment of the effectiveness of a random forest classifier for land-cover classification," ISPRS journal of photogrammetry and remote sensing, vol. 67, pp. 93-104, 2012.
[49]   A. L. Goldberger et al., "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals," circulation, vol. 101, no. 23, pp. e215-e220, 2000.
[50]   V. Novak et al., "Cerebral flow velocities during daily activities depend on blood pressure in patients with chronic ischemic infarctions," Stroke, vol. 41, no. 1, pp. 61-66, 2010.
[51]   V. Bajaj, S. Taran, S. K. Khare, and A. Sengur, "Feature extraction method for classification of alertness and drowsiness states EEG signals," Applied Acoustics, vol. 163, p. 107224, 2020.
[52]   A. R. Hassan, S. Siuly, and Y. Zhang, "Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating," Computer methods and programs in biomedicine, vol. 137, pp. 247-259, 2016.
[53]   G. Kaushik, P. Gaur, R. R. Sharma, and R. B. Pachori, "EEG signal based seizure detection focused on Hjorth parameters from tunable-Q wavelet sub-bands," Biomedical Signal Processing and Control, vol. 76, p. 103645, 2022.
[54]   I. W. Selesnick, "Wavelet transform with tunable Q-factor," IEEE transactions on signal processing, vol. 59, no. 8, pp. 3560-3575, 2011.
[55]   A. Sharma, S. Patidar, A. Upadhyay, and U. R. Acharya, "Accurate tunable-Q wavelet transform based method for QRS complex detection," Computers & Electrical Engineering, vol. 75, pp. 101-111, 2019.
[56]   G. Manikandan and S. Abirami, "An efficient feature selection framework based on information theory for high dimensional data," Applied Soft Computing, vol. 111, p. 107729, 2021.
[57]   S. Kilicarslan, K. Adem, and M. Celik, "Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network," Medical hypotheses, vol. 137, p. 109577, 2020.
[58]   K. Kira and L. A. Rendell, "A practical approach to feature selection," in Machine learning proceedings, Elsevier, 1992, pp. 249-256, 1992.
[59]   B. Zhang, Y. Li, and Z. Chai, "A novel random multi-subspace based ReliefF for feature selection," Knowledge-Based Systems, vol. 252, p. 109400, 2022.
[60]   M. Robnik-Šikonja and I. Kononenko, "An adaptation of Relief for attribute estimation in regression," in Machine learning: Proceedings of the fourteenth international conference (ICML’97), vol. 5: Citeseer, pp. 296-304, 1997.
[61]   R. Altilio, A. Rossetti, Q. Fang, X. Gu, and M. Panella, "A comparison of machine learning classifiers for smartphone-based gait analysis," Medical & Biological Engineering & Computing, vol. 59, pp. 535-546, 2021.
[62]   P. Patil, K. S. Kumar, N. Gaud, and V. B. Semwal, "Clinical human gait classification: extreme learning machine approach," in 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT), IEEE, pp. 1-6, 2019.
[63]   N. Biswas, K. M. M. Uddin, S. T. Rikta, and S. K. Dey, "A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach," Healthcare Analytics, vol. 2, p. 100116, 2022.
[64]         J. P. Amezquita-Sanchez and H. Adeli, "A new music-empirical wavelet transform methodology for time–frequency 
[1]     R. S. da Silva et al., "Psychometric properties of wearable technologies to assess post-stroke gait parameters: a systematic review," Gait & Posture, 2024.
[2]     S. Chen et al., "MSA-YOLOv5: Multi-scale attention-based YOLOv5 for automatic detection of acute ischemic stroke from multi-modality MRI images," Computers in Biology and Medicine, vol. 165, p. 107471, 2023.
[3]     D. Joshi, A. Khajuria, and P. Joshi, "An automatic non-invasive method for Parkinson's disease classification," Computer methods and programs in biomedicine, vol. 145, pp. 135-145, 2017.
[4]     J. M. Melvin, The effects of heel height, shoe volume and upper stiffness on shoe comfort and plantar pressure. University of Salford (United Kingdom), 2014.
[5]     P. Ghaderyan and G. Fathi, "Inter-limb time-varying singular value: a new gait feature for Parkinson’s disease detection and stage classification," Measurement, vol. 177, p. 109249, 2021.
[6]     V. Novak et al., "Cerebral flow velocities during daily activities depend on blood pressure in patients with chronic ischemic infarctions," Stroke, 2010.
[7]     E. C. Lee et al., "Utility of exosomes in ischemic and hemorrhagic stroke diagnosis and treatment," International Journal of Molecular Sciences, vol. 23, no. 15, p. 8367, 2022.
[8]     Y. Zhang et al., "Detection of acute ischemic stroke and backtracking stroke onset time via machine learning analysis of metabolomics," Biomedicine & Pharmacotherapy, vol. 155, p. 113641, 2022.
[9]     G. Das and P. Kumar, "Potential key genes for predicting risk of stroke occurrence: A computational approach," Neuroscience Informatics, vol. 2, no. 2, p. 100068, 2022.
[10]   Y.-H. Wang et al., "Lumbrokinase regulates endoplasmic reticulum stress to improve neurological deficits in ischemic stroke," Neuropharmacology, vol. 221, p. 109277, 2022.
[11]   S. J. Park, I. Hussain, S. Hong, D. Kim, H. Park, and H. C. M. Benjamin, "Real-time gait monitoring system for consumer stroke prediction service," in 2020 IEEE International conference on consumer electronics (ICCE), IEEE, pp. 1-4, 2020.
[12]   S. S. Bidabadi, I. Murray, G. Y. F. Lee, S. Morris, and T. Tan, "Classification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms," Gait & posture, vol. 71, pp. 234-240, 2019.
[13]   S. M. G. Beyrami and P. Ghaderyan, "A robust, cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis," Measurement, vol. 156, p. 107579, 2020.
[14]   K. Echigoya, K. Okada, M. Wakasa, A. Saito, M. Kimoto, and A. Suto, "Changes to foot pressure pattern in post-stroke individuals who have started to walk independently during the convalescent phase," Gait & Posture, vol. 90, pp. 307-312, 2021.
[15]   C. Beyaert, R. Vasa, and G. E. Frykberg, "Gait post-stroke: Pathophysiology and rehabilitation strategies," Neurophysiologie Clinique/Clinical Neurophysiology, vol. 45, no. 4-5, pp. 335-355, 2015.
[16]   M. Jacquelin Perry, "Gait analysis: normal and pathological function," New Jersey: SLACK, 2010.
[17]   A. Sant’Anna and N. Wickström, "A symbol-based approach to gait analysis from acceleration signals: Identification and detection of gait events and a new measure of gait symmetry," IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 5, pp. 1180-1187, 2010.
[18]   S. Krishnan and Y. Athavale, "Trends in biomedical signal feature extraction," Biomedical Signal Processing and Control, vol. 43, pp. 41-63, 2018.
[19]   W. Wang, K. Li, N. Wei, C. Yin, and S. Yue, "Evaluation of postural instability in stroke patient during quiet standing," in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp. 2522-2525, 2017.
[20]   F. Valentini, B. Granger, D. Hennebelle, N. Eythrib, and G. Robain, "Repeatability and variability of baropodometric and spatio-temporal gait parameters–results in healthy subjects and in stroke patients," Neurophysiologie Clinique/Clinical Neurophysiology, vol. 41, no. 4, pp. 181-189, 2011.
[21]   K. Hirata et al., "Adaptive changes in foot placement for split-belt treadmill walking in individuals with stroke," Journal of Electromyography and Kinesiology, vol. 48, pp. 112-120, 2019.
[22]   P. Lopez-Meyer, G. D. Fulk, and E. S. Sazonov, "Automatic detection of temporal gait parameters in poststroke individuals," IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 4, pp. 594-601, 2011.
[23]   K. van Kammen, A. M. Boonstra, L. H. van der Woude, H. A. Reinders-Messelink, and R. den Otter, "Differences in muscle activity and temporal step parameters between Lokomat guided walking and treadmill walking in post-stroke hemiparetic patients and healthy walkers," Journal of neuroengineering and rehabilitation, vol. 14, pp. 1-11, 2017.
[24]   M. Munoz-Organero, J. Parker, L. Powell, and S. Mawson, "Assessing walking strategies using insole pressure sensors for stroke survivors," Sensors, vol. 16, no. 10, p. 1631, 2016.
[25]   K. J. Nolan, M. Yarossi, and P. Mclaughlin, "Changes in center of pressure displacement with the use of a foot drop stimulator in individuals with stroke," Clinical biomechanics, vol. 30, no. 7, pp. 755-761, 2015.
[26]   M. Muñoz-Organero, J. Parker, L. Powell, R. Davies, and S. Mawson, "Sensor optimization in smart insoles for post-stroke gait asymmetries using total variation and L 1 distances," IEEE Sensors Journal, vol. 17, no. 10, pp. 3142-3151, 2017.
[27]   C. Le Bocq, M. Rousseaux, N. Buisset, W. Daveluy, S. Blond, and E. Allart, "Effects of tibial nerve neurotomy on posture and gait in stroke patients: a focus on patient-perceived benefits in daily life," Journal of the Neurological Sciences, vol. 366, pp. 158-163, 2016.
[28]   A. Mannini, D. Trojaniello, A. Cereatti, and A. M. Sabatini, "A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington’s disease patients," Sensors, vol. 16, no. 1, p. 134, 2016.
[29]   E. Bergamini, M. Iosa, V. Belluscio, G. Morone, M. Tramontano, and G. Vannozzi, "Multi-sensor assessment of dynamic balance during gait in patients with subacute stroke," Journal of biomechanics, vol. 61, pp. 208-215, 2017.
[30]   M. Saljuqi and P. Ghaderyan, "A novel method based on matching pursuit decomposition of gait signals for Parkinson’s disease, Amyotrophic lateral sclerosis and Huntington’s disease detection," Neuroscience Letters, vol. 761, p. 136107, 2021.
[31]   R. Polikar, "The wavelet tutorial," ed, 1996.
[32]   H. S. Pal, A. Kumar, A. Vishwakarma, and M. K. Ahirwal, "Electrocardiogram signal compression using tunable-Q wavelet transform and meta-heuristic optimization techniques," Biomedical Signal Processing and Control, vol. 78, p. 103932, 2022.
[33]   S. C. KS, A. Mishra, V. Shirhatti, and S. Ray, "Comparison of matching pursuit algorithm with other signal processing techniques for computation of the time-frequency power spectrum of brain signals," Journal of Neuroscience, vol. 36, no. 12, pp. 3399-3408, 2016.
[34]   I. Selesnick, "Wavelet transform with tunable Q-factor IEEE transactions on signal processing. 2011 Aug; 8 (59) pp: 3560-75. doi: 10.1109," TSP, 2011.
[35]   S. Taran, V. Bajaj, G. Sinha, and K. Polat, "Detection of sleep apnea events using electroencephalogram signals," Applied Acoustics, vol. 181, p. 108137, 2021.
[36]   M. Baygin, "An accurate automated schizophrenia detection using TQWT and statistical moment based feature extraction," Biomedical Signal Processing and Control, vol. 68, p. 102777, 2021.
[37]   M. Li, S. Tian, L. Sun, and X. Chen, "Gait analysis for post-stroke hemiparetic patient by multi-features fusion method," Sensors, vol. 19, no. 7, p. 1737, 2019.
[38]   D. Yoo, Y. Son, D.-H. Kim, K.-H. Seo, and B.-C. Lee, "Technology-assisted ankle rehabilitation improves balance and gait performance in stroke survivors: a randomized controlled study with 1-month follow-up," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 12, pp. 2315-2323, 2018.
[39]   J. C. Dean, A. E. Embry, K. H. Stimpson, L. A. Perry, and S. A. Kautz, "Effects of hip abduction and adduction accuracy on post-stroke gait," Clinical Biomechanics, vol. 44, pp. 14-20, 2017.
[40]   P. Ghaderyan and S. M. G. Beyrami, "Neurodegenerative diseases detection using distance metrics and sparse coding: A new perspective on gait symmetric features," Computers in Biology and Medicine, vol. 120, p. 103736, 2020.
[41]   M.-G. Tan, J.-H. Ho, H.-T. Goh, H. K. Ng, L. A. Latif, and M. Mazlan, "A new fractal-based kinetic index to characterize gait deficits with application in stroke survivor functional mobility assessment," Biomedical Signal Processing and Control, vol. 52, pp. 403-413, 2019.
[42]   P. Ghaderyan and A. Abbasi, "A novel cepstral-based technique for automatic cognitive load estimation," Biomedical Signal Processing and Control, vol. 39, pp. 396-404, 2018.
[43]   M. Pourezzat and H. Danandeh Hesar, "Development of a New Adaptive Method Based on Empirical Fourier Decomposition for the Diagnosis of Obstructive Sleep Apnea Using Electrocardiogram Signal Analysis," TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, vol. 53, no. 3, pp. 159-170, 2023.
[44]   R. J. Urbanowicz, M. Meeker, W. La Cava, R. S. Olson, and J. H. Moore, "Relief-based feature selection: Introduction and review," Journal of biomedical informatics, vol. 85, pp. 189-203, 2018.
[45]   M. Wang et al., "Research on abnormal gait recognition algorithms for stroke patients based on array pressure sensing system," in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), IEEE, pp. 1560-1563, 2019.
[46]   S. Osowski, K. Siwek, and T. Markiewicz, "MLP and SVM networks-a comparative study," in Proceedings of the 6th Nordic Signal Processing Symposium, NORSIG 2004., 2004: IEEE, pp. 37-40, 2004.
[47]   S. Ray, "An analysis of computational complexity and accuracy of two supervised machine learning algorithms—K-nearest neighbor and support vector machine," in Data Management, Analytics and Innovation: Proceedings of ICDMAI 2020, Volume 1, Springer, pp. 335-347, 2021.
[48]   V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, "An assessment of the effectiveness of a random forest classifier for land-cover classification," ISPRS journal of photogrammetry and remote sensing, vol. 67, pp. 93-104, 2012.
[49]   A. L. Goldberger et al., "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals," circulation, vol. 101, no. 23, pp. e215-e220, 2000.
[50]   V. Novak et al., "Cerebral flow velocities during daily activities depend on blood pressure in patients with chronic ischemic infarctions," Stroke, vol. 41, no. 1, pp. 61-66, 2010.
[51]   V. Bajaj, S. Taran, S. K. Khare, and A. Sengur, "Feature extraction method for classification of alertness and drowsiness states EEG signals," Applied Acoustics, vol. 163, p. 107224, 2020.
[52]   A. R. Hassan, S. Siuly, and Y. Zhang, "Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating," Computer methods and programs in biomedicine, vol. 137, pp. 247-259, 2016.
[53]   G. Kaushik, P. Gaur, R. R. Sharma, and R. B. Pachori, "EEG signal based seizure detection focused on Hjorth parameters from tunable-Q wavelet sub-bands," Biomedical Signal Processing and Control, vol. 76, p. 103645, 2022.
[54]   I. W. Selesnick, "Wavelet transform with tunable Q-factor," IEEE transactions on signal processing, vol. 59, no. 8, pp. 3560-3575, 2011.
[55]   A. Sharma, S. Patidar, A. Upadhyay, and U. R. Acharya, "Accurate tunable-Q wavelet transform based method for QRS complex detection," Computers & Electrical Engineering, vol. 75, pp. 101-111, 2019.
[56]   G. Manikandan and S. Abirami, "An efficient feature selection framework based on information theory for high dimensional data," Applied Soft Computing, vol. 111, p. 107729, 2021.
[57]   S. Kilicarslan, K. Adem, and M. Celik, "Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network," Medical hypotheses, vol. 137, p. 109577, 2020.
[58]   K. Kira and L. A. Rendell, "A practical approach to feature selection," in Machine learning proceedings, Elsevier, 1992, pp. 249-256, 1992.
[59]   B. Zhang, Y. Li, and Z. Chai, "A novel random multi-subspace based ReliefF for feature selection," Knowledge-Based Systems, vol. 252, p. 109400, 2022.
[60]   M. Robnik-Šikonja and I. Kononenko, "An adaptation of Relief for attribute estimation in regression," in Machine learning: Proceedings of the fourteenth international conference (ICML’97), vol. 5: Citeseer, pp. 296-304, 1997.
[61]   R. Altilio, A. Rossetti, Q. Fang, X. Gu, and M. Panella, "A comparison of machine learning classifiers for smartphone-based gait analysis," Medical & Biological Engineering & Computing, vol. 59, pp. 535-546, 2021.
[62]   P. Patil, K. S. Kumar, N. Gaud, and V. B. Semwal, "Clinical human gait classification: extreme learning machine approach," in 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT), IEEE, pp. 1-6, 2019.
[63]   N. Biswas, K. M. M. Uddin, S. T. Rikta, and S. K. Dey, "A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach," Healthcare Analytics, vol. 2, p. 100116, 2022.
[64]   J. P. Amezquita-Sanchez and H. Adeli, "A new music-empirical wavelet transform methodology for time–frequency analysis of noisy nonlinear and non-stationary signals," Digital Signal Processing, vol. 45, pp. 55-68, 2015.
[65]   J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, "A comprehensive survey on support vector machine classification: Applications, challenges and trends," Neurocomputing, vol. 408, pp. 189-215, 2020.
[66]   Y. Narayan, "SEMG signal classification using KNN classifier with FD and TFD features," Materials Today: Proceedings, vol. 37, pp. 3219-3225, 2021.
[67]   J. Zhou et al., "Predicting Stroke and Mortality in Mitral Regurgitation: A Machine Learning Approach," Current Problems in Cardiology, p. 101464, 2022.
[68]   A. Subudhi, M. Dash, and S. Sabut, "Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier," Biocybernetics and Biomedical Engineering, vol. 40, no. 1, pp. 277-289, 2020.
[69]   G. Cattani, "Combining data envelopment analysis and Random Forest for selecting optimal locations of solar PV plants," Energy and AI, vol. 11, p. 100222, 2023.
[70]   C. Donos, M. Dümpelmann, and A. Schulze-Bonhage, "Early seizure detection algorithm based on intracranial EEG and random forest classification," International journal of neural systems, vol. 25, no. 05, p. 1550023, 2015.

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