TY - GEN
T1 - Analysis and Classification of Tremors in a Parkinson's Disease Simulator Using Machine Learning
AU - Toque, Erick
AU - Vila, Sebastian
AU - Gutierrez-Flores, Cesar
AU - Silva-Salas, Rosa M.
AU - Abarca, Victoria E.
AU - Elias, Dante A.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Hand tremor is a symptom typically observed in patients with Parkinson's disease (PD). However, repetitive hand movements in healthy patients or non-PD conditions can also be confused with this symptom. In that sense, this work develops a systematic analysis for differentiating the types of tremors in reference using Machine Learning techniques and a tremor simulator mechanism equipped with inertial sensors that will provide the necessary dataset for such analysis. According to scientific literature, this mechanism is based on frequency analysis with a principal component of about 5 Hz. The results show that the best classification model is the random forest with favorable metrics such as an accuracy of 98.66% and an F1 score of 98.66 %. This will allow a classification of the nature of tremors for subsequent application in diagnosing PD, reducing complexity in the clinical analysis through data collection with inertial sensors and applying an optimized algorithm. In addition, it means a step forward in automating clinical procedures to benefit patients with this type of disease with symptomatological particularities' such as hand tremors.
AB - Hand tremor is a symptom typically observed in patients with Parkinson's disease (PD). However, repetitive hand movements in healthy patients or non-PD conditions can also be confused with this symptom. In that sense, this work develops a systematic analysis for differentiating the types of tremors in reference using Machine Learning techniques and a tremor simulator mechanism equipped with inertial sensors that will provide the necessary dataset for such analysis. According to scientific literature, this mechanism is based on frequency analysis with a principal component of about 5 Hz. The results show that the best classification model is the random forest with favorable metrics such as an accuracy of 98.66% and an F1 score of 98.66 %. This will allow a classification of the nature of tremors for subsequent application in diagnosing PD, reducing complexity in the clinical analysis through data collection with inertial sensors and applying an optimized algorithm. In addition, it means a step forward in automating clinical procedures to benefit patients with this type of disease with symptomatological particularities' such as hand tremors.
KW - classification
KW - hand
KW - machine learning
KW - Parkinson
KW - tremor
UR - http://www.scopus.com/inward/record.url?scp=85211941221&partnerID=8YFLogxK
U2 - 10.1109/ANDESCON61840.2024.10755804
DO - 10.1109/ANDESCON61840.2024.10755804
M3 - Conference contribution
AN - SCOPUS:85211941221
T3 - IEEE Andescon, ANDESCON 2024 - Proceedings
BT - IEEE Andescon, ANDESCON 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Andescon, ANDESCON 2024
Y2 - 11 September 2024 through 13 September 2024
ER -