Nowadays objective and efficient assessment of Parkinson Disease (PD) with machine learning techniques is a major focus for clinical management. This work presents a novel approach for classification of patients with PD (PwPD) and healthy controls (HC) using Bidirectional Long Short-Term Neural Network (BLSTM). In this paper, the SensHand and the SensFoot inertial wearable sensors for upper and lower limbs motion analysis were used to acquire motion data in thirteen tasks derived from the MDS-UPDRS III. Sixty-four PwPD and fifty HC were involved in this study. One hundred ninety extracted spatiotemporal and frequency parameters were applied as a single input against each subject to develop a recurrent BLSTM to discriminate the two groups. The maximum achieved accuracy was 82.4%, with the sensitivity of 92.3% and specificity of 76.2%. The obtained results suggest that the use of the extracted parameters for the development of the BLSTM contributed significantly to the classification of PwPD and HC.

Biomechanical parameters assessment for the classification of Parkinson Disease using Bidirectional Long Short-Term Memory / Butt A.H.; Cavallo F.; Maremmani C.; Rovini E.. - ELETTRONICO. - 2020-:(2020), pp. 5761-5764. ((Intervento presentato al convegno 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 tenutosi a can nel 2020 [10.1109/EMBC44109.2020.9176051].

Biomechanical parameters assessment for the classification of Parkinson Disease using Bidirectional Long Short-Term Memory

Cavallo F.
;
Rovini E.
2020

Abstract

Nowadays objective and efficient assessment of Parkinson Disease (PD) with machine learning techniques is a major focus for clinical management. This work presents a novel approach for classification of patients with PD (PwPD) and healthy controls (HC) using Bidirectional Long Short-Term Neural Network (BLSTM). In this paper, the SensHand and the SensFoot inertial wearable sensors for upper and lower limbs motion analysis were used to acquire motion data in thirteen tasks derived from the MDS-UPDRS III. Sixty-four PwPD and fifty HC were involved in this study. One hundred ninety extracted spatiotemporal and frequency parameters were applied as a single input against each subject to develop a recurrent BLSTM to discriminate the two groups. The maximum achieved accuracy was 82.4%, with the sensitivity of 92.3% and specificity of 76.2%. The obtained results suggest that the use of the extracted parameters for the development of the BLSTM contributed significantly to the classification of PwPD and HC.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
can
2020
Butt A.H.; Cavallo F.; Maremmani C.; Rovini E.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1213599
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