Abstract
Exploiting the synchronized assessment of the neuromuscular implications, this paper proposes an embedded digital architecture for the assessment of the movements’ automatism and the reduction of pre-motor function capability. The study can enable a forward recognition of the Parkinson’s disease (PD) progression stages, which are characterized by muscular disorders. The architecture, implemented on Altera Cyclone V FPGA, classifies in real-time these physiological disorders during the walk. The system operates on 8 surface EMG (limbs) and 7 EEG (motor-cortex). The signals, synchronously acquired and processed, undergo to a features extraction (FE) in the time-frequency domains. The features are time-continuously processed (in chronological order) from an innovative on-going Support Vector Machine (SVM) classifier. The SVM identifies and categorizes the patient pathology severity. Experimental results from 4 subjects affected by mild (n = 2) and heavy PD (n = 2) show an accuracy 93.97% ± 2.1% in PD stages recognition.
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De Venuto, D., Annese, V.F., Mezzina, G.: Remote neuro-cognitive impairment sensing based on P300 spatio-temporal monitoring. IEEE Sensors J. 16(23), 8348–8356 (2016). https://doi.org/10.1109/jsen.2016.2606553
Rovini, E., et al.: How wearable sensors can support Parkinson’s disease diagnosis and treatment: a systematic review. Front. Neurosci. 11, 555 (2017)
Kostikis, N., et al.: A Smartphone based tool for assessing Parkinsonian hand tremor. J. Biomed. Heal Informat. 19, 1835–1842 (2015)
Braybrook, M., et al.: An ambulatory tremor score for Parkinson’s disease. J. Parkinsons. Dis. 6, 723–731 (2016)
Ruonala, V., et al.: EMG signal morphology and kinematic parameters in essential tremor and Parkinson’s disease patients. J. Electromyogr. Kinesiol. 24, 300–306 (2014)
Salarian, A., et al.: ITUG, a sensitive and reliable measure of mobility. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 303–310 (2010)
Perumal, S.V., Sankar, R.: Gait and tremor assessment for patients with Parkinson’s disease using wearable sensors. ICT Expr. 2, 168–174 (2016)
De Venuto, D., Annese, V.F., Mezzina, G., Defazio, G.: FPGA-based embedded cyber-physical platform to assess gait and postural stability in Parkinson’s disease. IEEE Trans. Compon. Packag. Manuf. Technology. https://doi.org/10.1109/tcpmt.2018.2810103
De Tommaso, M., Vecchio, E., Ricci, K., Montemurno, A., De Venuto, D., Annese, V.F.: Combined EEG/EMG evaluation during a novel dual task paradigm for gait analysis. In: Proceedings–2015 6th IEEE International Workshop on Advances in Sensors and Interfaces, IWASI 2015, art. no. 7184949, pp. 181–186. https://doi.org/10.1109/iwasi.2015.7184949 (2015)
Hoehn, M.M., Yahr, M.D.: Parkinsonism: onset, progression, and mortality. Neurology 50(2), 318–318 (1998)
De Venuto, D., Stikvoort, E., Tio Castro, D., Ponomarev, Y.: Ultra low-power 12-bit SAR ADC for RFID applications. In: 2010 Design, Automation & Test in Europe Conference & Exhibition, pp. 1071–1075. Dresden. https://doi.org/10.1109/date.2010.5456968 (2010)
De Venuto, D., Annese, V.F., Ruta, M., Di Sciascio, E., Sangiovanni Vincentelli, A.L.: Designing a cyber-physical system for fall prevention by cortico-muscular coupling detection. In: IEEE Design and Test, vol. 33(3), pp. 66–76, art. no. 7273831. https://doi.org/10.1109/mdat.2015.2480707 (2016)
Annese, V.F., Crepaldi, M., Demarchi, D., De Venuto, D.: A digital processor architecture for combined EEG/EMG falling risk prediction. In: 2016 Design, Automation & Test in Europe Conference & Exhibition, pp. 714–719. Dresden (2016)
Gunn, Steve R.: Support vector machines for classification and regression. ISIS Tech. Rep. 14(1), 5–16 (1998)
De Venuto, D., Torre, M.D., Boero, C., Carrara, S., De Micheli, G.: A novel multi-working electrode potentiostat for electrochemical detection of metabolites. 2010 IEEE Sensors 1572–1577. Kona, HI (2010). https://doi.org/10.1109/icsens.2010.5690297
Carrara, S., Torre, M.D., Cavallini, A., De Venuto, D., De Micheli, G.: Multiplexing pH and temperature in a molecular biosensor. In: 2010 Biomedical Circuits and Systems Conference (BioCAS), pp. 146–149. Paphos. https://doi.org/10.1109/biocas.2010.5709592 (2010)
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De Venuto, D., Mezzina, G. (2019). Neuromuscular Disorders Assessment by FPGA-Based SVM Classification of Synchronized EEG/EMG. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2018. Lecture Notes in Electrical Engineering, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-030-11973-7_5
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DOI: https://doi.org/10.1007/978-3-030-11973-7_5
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