Wavelet-Based Method for Parkinson's Detection from Voice Signal
DOI:
https://doi.org/10.32792/jeps.v13i2.302الملخص
Parkinson's disease (PD) is a brain disorder that causes speech and communication problems. The speech
issues are often described as slow speech and difficulty with articulation because Jaw muscles don’t
move with enough strength. Mainly, clinical experts make a voice assessment to analyze voice signals, to
detect PD. In this paper, we designed an intelligent model based on discrete wavelet transform (DWT) for
the detection of Parkinson's. Firstly, the voice signals are passed through DWT. The suggested DWTbased
model is then used to extract a collection of statistical features from voice data. The extracted
features are evaluated, and the significant ones are chosen. The features that have been chosen are fed
into a least squares support vector machine (LS-SVM), as well as another four classifiers K-nearest
neighbor (KNN), bagged tree, SVM (support vector machine), and k means for the comparison. The
proposed model is evaluated using a publicly available dataset named UCI machine learning repository.
Several tests were carried out, and the findings revealed that the proposed framework can classify voice
signals with a100% accuracy in the LS-SVM technique
المراجع
[ 1] Poorjam, Amir Hossein, et al. "Automatic quality control and enhancement for voicebased
remote Parkinson’s disease detection." Speech Communication 127 (2021):
[ 2] Naranjo, Lizbeth, et al. "A two-stage variable selection and classification approach for
Parkinson’s disease detection by using voice recording replications." Computer methods and
programs in biomedicine 142 (2017): 147-156.
[ 3] Haq, Amin Ul, et al. "Feature selection based on L1-norm support vector machine and effective
recognition system for Parkinson’s disease using voice recordings." IEEE access 7 (2019): 37718-
[ 4] Lahmiri, Salim, and Amir Shmuel. "Detection of Parkinson’s disease based on voice patterns
ranking and optimized support vector machine." Biomedical Signal Processing and Control 49
(2019): 427-433.
[ 5] Mostafa, Salama A., et al. "Examining multiple feature evaluation and classification methods
for improving the diagnosis of Parkinson’s disease." Cognitive Systems Research 54 (2019): 90-
[ 6] Sabeena, B., S. Sivakumari, and P. Amudha. "A technical survey on various machine learning
approaches for Parkinson’s disease classification." Materials Today: Proceedings (2020).
[ 7] Tai, Yu Chen, et al. "A voice analysis approach for recognizing Parkinson’s disease patterns."
IFAC-PapersOnLine 54.15 (2021): 382-387.
[ 8] Laetitia, et al. "Voice characteristics from isolated rapid eye movement sleep behavior disorder
to early Parkinson's disease." Parkinsonism & Related Disorders 95 (2022): 86-91
[ 9] Ali, Liaqat, et al. "Automated detection of Parkinson’s disease based on multiple types of
sustained phonations using linear discriminant analysis and genetically optimized neural network."
IEEE journal of translational engineering in health and medicine 7 (2019): 1-10.
[ 10] Gunduz, Hakan. "Deep learning-based Parkinson’s disease classification using vocal
feature sets." IEEE Access 7 (2019): 115540-115551.
[ 11] Xu, Zhi-Jing, et al. "Parkinson’s disease detection based on spectrogram-deep
convolutional generative adversarial network sample augmentation." IEEE Access 8 (2020):
-20690.
[ 12] Karaman, Onur, et al. "Robust automated Parkinson disease detection based on voice
signals with transfer learning." Expert Systems with Applications 178 (2021): 115013.
[ 13] Shalin, Gaurav, et al. "Prediction and detection of freezing of gait in Parkinson’s disease
from plantar pressure data using long short-term memory neural-networks." Journal of
neuroengineering and rehabilitation 18.1 (2021): 1-15.
[ 14] Sakar, Betul Erdogdu, et al. "Collection and analysis of a Parkinson speech dataset with
multiple types of sound recordings." IEEE Journal of Biomedical and Health Informatics 17.4
(2013): 828-834.
[ 15] Wrobel, Krzysztof. "Diagnosing Parkinson’s disease by means of ensemble classification
of patients’ voice samples." Procedia Computer Science 192 (2021): 3905-3914.
[ 16] Vidya, B., and P. Sasikumar. "Wearable multi-sensor data fusion approach for human
activity recognition using machine learning algorithms." Sensors and Actuators A: Physical 341
(2022): 113557.
[ 17] Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: A review.
IEEE Transactions on pattern analysis and machine intelligence, 22(1), 4-37.
[ 18] Hoq, Muntasir, Mohammed Nazim Uddin, and Seung-Bo Park. "Vocal featureextraction-based artificial intelligent model for Parkinson’s disease detection." Diagnostics 11.6
(2021): 1076.
[ 19] Goberman, Alexander M. "Correlation between acoustic speech characteristics and nonspeech
motor performance in Parkinson disease." Medical science monitor 11.3 (2005): CR109-
CR116.
[ 20] Kannathal, N., et al. "Entropies for detection of epilepsy in EEG." Computer methods
and programs in biomedicine 80.3 (2005): 187-194.
[ 21] Li, Yan, and Peng Paul Wen. "Clustering technique-based least square support vector
machine for EEG signal classification." Computer methods and programs in biomedicine 104.3
(2011): 358-372.
[ 22] Kunhimangalam, Reeda, Sujith Ovallath, and Paul K. Joseph. "A novel fuzzy expert
system for the identification of severity of carpal tunnel syndrome." BioMed research international
(2013).
[ 23] Meghraoui, Djamila, et al. "A novel pre-processing technique in pathologic voice
detection: Application to Parkinson’s disease phonation." Biomedical Signal Processing and
Control 68 (2021): 102604.
[ 24]
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الحقوق الفكرية (c) 2023 Journal of Education for Pure Science- University of Thi-Qar
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