Abstract
Technological advances in signal processing, electronics, embedded systems and neuroscience have allowed the design of devices that help physicians to better assess the evolution of neurological diseases. In this context, we are interested in the development of an intelligent system for the quantification of Parkinson’s disease (PD). In order to achieve this, the system contains two parts: a wireless sensor network and an embedded system. The wireless sensor network is used to measure motor defects of the patient; it is constituted of several nodes which communicate among themselves. These nodes are intelligent sensors; they contains accelerometers, EMG and blood pressure sensors to detect any malfunction of the patient’s motor activities. As regards to the embedded system, it allows analyzing the patient’s voice signal in order to extract a descriptor that characterizes PD. The network detects the patient’s posture and measures his or her tremors. The voice analysis system measures the degradation of the patient’s condition. The embedded system combines the three decisions using the Chair–Varshney rule. The data fusion between the sensor network and the embedded system, will quantify the disease to facilitate the diagnostic for the physician, while providing the ability to effectively assess the evolution of the patient’s health.
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References
Aziz, M. (2014). A new adaptive decentralized soft decision combining rule for distributed sensor systems with data fusion. Information Sciences, 25, 197–210.
Benba, A., Jilbab, A., & Hammouch, A. (2014). Voice analysis for detecting persons with Parkinson’s disease using MFCC and VQ. In The 2014 international conference on circuits, systems and signal processing, Saint Petersburg, Russia, September 23–25.
Benba, A., Jilbab, A., & Hammouch, A. (2014). Voice analysis for detecting persons with Parkinson’s disease using PLP and VQ. Journal of Theoretical and Applied Information Technology 70(3).
Benba, A., Jilbab, A., & Hammouch, A. (2014). Voiceprint analysis using perceptual linear prediction and support vector machines for detecting persons with Parkinson’s disease. In The 3rd international conference on health science and biomedical systems, Florence, Italy, November 22–24.
Benba, A., Jilbab, A., & Hammouch, A. (2015). Detecting patients with parkinson’s disease using Mel frequency cepstral coefficients and support vector machines. International Journal on Electrical Engineering and Informatics, 7(2), 297.
Benba, A., Jilbab, A., & Hammouch, A. (2016). Discriminating between patients with Parkinson’s and neurological diseases using Cepstral analysis. In IEEE transactions on neural systems and rehabilitation engineering (IEEE TNSRE). doi:10.1109/TNSRE.2013.2294749. No. 99, ISSN: 1534-4320.
Benba, A., Jilbab, A., & Hammouch, A. (2016b). Analysis of multiple types of voice recordings in Cepstral domain using MFCC for discriminating between patients with Parkinson’s disease and healthy people. International Journal of Speech Technology. doi:10.1007/s10772-016-9338-4.
Benba, A., Jilbab, A., & Hammouch, A. (2016c). Voice analysis for detecting patients with Parkinson’s disease using the hybridization of the best acoustic features. International Journal on Electrical Engineering and Informatics (IJEEI), 8(1), 1.
Benmalek, E., Elmhamdi, J., & Jilbab, A. (2015). UPDRS tracking using linear regression and neural network for Parkinson’s disease prediction. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 4(6), 189–193.
Chair, Z., & Varshney, P. K. (1986). Optimal data fusion in multiple sensor detection systems. IEEE trans. Aerospace Electron. Syst., 22, 98–101.
Dai, H., Zhang, P., & Lueth, T. C. (2015). Quantitative assessment of parkinsonian tremor based on an inertial measurement unit. Sensors, 15(10), 25055–25071.
El abbassi, M. A., Jilbab, A., & Bourouhou, A. (2016). Detection model based on multi-sensor data for early fire prevention. In 2nd international conference on electrical and information technologies ICEIT’2016.
Hermansky, H. (1990). Perceptual linear predictive (PLP) analysis of speech. The Journal of the Acoustical Society of America, 87(4), 1738–1752.
Khan, T., Westin, J., & Dougherty, M. (2014). Classification of speech intelligibility in Parkinson’s disease. Biocybernetics and Biomedical Engineering, 34(1), 35–45.
Sung, W.-T. (2010a). Multi-sensors data fusion system for wireless sensors networks of factory monitoring via BPN technology. Expert Systems with Applications, 37, 2124–2131.
Sung, W.-T. (2010b). Multi-sensors data fusion system for wireless sensors networks of factory monitoring via BPN technology. Expert Systems with Applications, 37, 2124–2131.
Zervas, E., Mpimpoudis, A., Anagnostopoulos, C., Sekkas, O., & Hadjiefthymiades, S. (2011). Multisensor data fusion for fire detection. Information Fusion, 12, 150–159.
Zhou, G., Zhu, Z., Chen, G., & Zhou, L. (2011a). Decision fusion rules based on multi-bit knowledge of local sensorsin wireless sensor networks. Information Fusion, 12, 187–193.
Zhou, G., Zhu, Z., Chen, G., & Zhou, L. (2011b). Decision fusion rules based on multi-bit knowledge of local sensors in wireless sensor networks. Information Fusion, 12, 187–193.
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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5).
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Jilbab, A., Benba, A. & Hammouch, A. Quantification system of Parkinson’s disease. Int J Speech Technol 20, 143–150 (2017). https://doi.org/10.1007/s10772-016-9394-9
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DOI: https://doi.org/10.1007/s10772-016-9394-9