Carlos Pérez-López1, Albert Samà2, Daniel Rodríguez-Martín2, Juan Manuel Moreno-Aróstegui2, Joan Cabestany2, Angels Bayes3, Berta Mestre3, Sheila Alcaine3, Paola Quispe3, Gearóid Ó Laighin4, Dean Sweeney4, Leo R Quinlan5, Timothy J Counihan6, Patrick Browne6, Roberta Annicchiarico7, Alberto Costa8, Hadas Lewy9, Alejandro Rodríguez-Molinero4. 1. Centro de Estudios para la Dependencia y la vida Autónoma (CETpD), Universitat Politècnica de Catalunya (UPC), Rambla de l'Exposició, 59, 08800 Vilanova i la Geltrú, Barcelona, Spain. Electronic address: carlos.perez-lopez@upc.edu. 2. Centro de Estudios para la Dependencia y la vida Autónoma (CETpD), Universitat Politècnica de Catalunya (UPC), Rambla de l'Exposició, 59, 08800 Vilanova i la Geltrú, Barcelona, Spain. 3. UParkinson, Passeig Bonanova 26, Barcelona 08022, Spain. 4. Electrical & Electronic Engineering, School of Engineering & Informatics National University Galway (NUIG), University Rd, Galway, Ireland. 5. Physiology, School of Medicine National University Galway (NUIG), University Rd, Galway, Ireland. 6. School of Medicine, National University Galway (NUIG), University Rd, Galway, Ireland. 7. Fondazione Santa Lucia, Via Ardeatina, 306, Rome 00142, Italy. 8. Fondazione Santa Lucia, Via Ardeatina, 306, Rome 00142, Italy; Niccolò Cusano University, via Don Carlo Gnocchi, 3, Rome 00166, Italy. 9. Maccabi Healthcare Services, Hamered Street 27, Tel-Aviv 68125, Israel.
Abstract
BACKGROUND: After several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more informed decisions in patient's care. OBJECTIVE: To design and validate an algorithm able to be embedded into a system that PD patients could wear during their activities of daily living with the purpose of registering the occurrence of dyskinesia in real conditions. MATERIALS AND METHODS: Data from an accelerometer positioned in the waist are collected at the patient's home and are annotated by experienced clinicians. Data collection is divided into two parts: a main database gathered from 92 patients used to partially train and to evaluate the algorithms based on a leave-one-out approach and, on the other hand, a second database from 10 patients which have been used to also train a part of the detection algorithm. RESULTS: Results show that, depending on the severity and location of dyskinesia, specificities and sensitivities higher than 90% are achieved using a leave-one-out methodology. Although mild dyskinesias presented on the limbs are detected with 95% specificity and 39% sensitivity, the most important types of dyskinesia (any strong dyskinesia and trunk mild dyskinesia) are assessed with 95% specificity and 93% sensitivity. CONCLUSION: The presented algorithmic method and wearable device have been successfully validated in monitoring the occurrence of strong dyskinesias and mild trunk dyskinesias during activities of daily living.
BACKGROUND: After several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more informed decisions in patient's care. OBJECTIVE: To design and validate an algorithm able to be embedded into a system that PDpatients could wear during their activities of daily living with the purpose of registering the occurrence of dyskinesia in real conditions. MATERIALS AND METHODS: Data from an accelerometer positioned in the waist are collected at the patient's home and are annotated by experienced clinicians. Data collection is divided into two parts: a main database gathered from 92 patients used to partially train and to evaluate the algorithms based on a leave-one-out approach and, on the other hand, a second database from 10 patients which have been used to also train a part of the detection algorithm. RESULTS: Results show that, depending on the severity and location of dyskinesia, specificities and sensitivities higher than 90% are achieved using a leave-one-out methodology. Although mild dyskinesias presented on the limbs are detected with 95% specificity and 39% sensitivity, the most important types of dyskinesia (any strong dyskinesia and trunk mild dyskinesia) are assessed with 95% specificity and 93% sensitivity. CONCLUSION: The presented algorithmic method and wearable device have been successfully validated in monitoring the occurrence of strong dyskinesias and mild trunk dyskinesias during activities of daily living.
Authors: E Ray Dorsey; Alistair M Glidden; Melissa R Holloway; Gretchen L Birbeck; Lee H Schwamm Journal: Nat Rev Neurol Date: 2018-04-06 Impact factor: 42.937
Authors: Eni Halilaj; Apoorva Rajagopal; Madalina Fiterau; Jennifer L Hicks; Trevor J Hastie; Scott L Delp Journal: J Biomech Date: 2018-09-13 Impact factor: 2.712
Authors: Alejandro Rodríguez-Molinero; Jorge Hernández-Vara; Antonio Miñarro; Carlos Pérez-López; Àngels Bayes-Rusiñol; Juan Carlos Martínez-Castrillo; David A Pérez-Martínez Journal: BMJ Open Date: 2021-07-19 Impact factor: 2.692
Authors: Carlos Pérez-López; Albert Samà; Daniel Rodríguez-Martín; Andreu Català; Joan Cabestany; Juan Manuel Moreno-Arostegui; Eva de Mingo; Alejandro Rodríguez-Molinero Journal: Sensors (Basel) Date: 2016-12-15 Impact factor: 3.576
Authors: Daniel Rodríguez-Martín; Carlos Pérez-López; Albert Samà; Andreu Català; Joan Manuel Moreno Arostegui; Joan Cabestany; Berta Mestre; Sheila Alcaine; Anna Prats; María de la Cruz Crespo; Àngels Bayés Journal: Sensors (Basel) Date: 2017-04-11 Impact factor: 3.576