TY - GEN
T1 - PD-Net
T2 - 36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023
AU - Khan, Mustaqeem
AU - Khan, Ufaq
AU - Othmani, Alice
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Parkinson's disease is a neurodegenerative disorder that affects movement and muscle control and is caused by the loss of dopamine-producing neurons in the brain. The main symptoms of Parkinson's disease (PD) include tremors, rigidity, slowness of movement, imbalances, and linguistic impairment. One of the most pronounced clinical indicators is a change in the patient's voice, which can be used to assist in the diagnosis and evaluation of PD. An innovative method based on speech signals is proposed in this study to automatically identify PD by a sophisticated learning strategy to extract features via a parallel convolution-based network with an attention mechanism to preferentially focused on relevant PD cues. The proposed method utilized raw speech and i-vector as input tensors. We evaluated the method by different metrics including accuracy 98%, precision 0.99, recall 0.96, and f1-score 0.97 which shows the model's robustness.
AB - Parkinson's disease is a neurodegenerative disorder that affects movement and muscle control and is caused by the loss of dopamine-producing neurons in the brain. The main symptoms of Parkinson's disease (PD) include tremors, rigidity, slowness of movement, imbalances, and linguistic impairment. One of the most pronounced clinical indicators is a change in the patient's voice, which can be used to assist in the diagnosis and evaluation of PD. An innovative method based on speech signals is proposed in this study to automatically identify PD by a sophisticated learning strategy to extract features via a parallel convolution-based network with an attention mechanism to preferentially focused on relevant PD cues. The proposed method utilized raw speech and i-vector as input tensors. We evaluated the method by different metrics including accuracy 98%, precision 0.99, recall 0.96, and f1-score 0.97 which shows the model's robustness.
KW - Attention mechanism
KW - Deep learning
KW - I-vector
KW - Parallel CNN
KW - Parkinson's disease
KW - Speech signals
UR - http://www.scopus.com/inward/record.url?scp=85166475954&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85166475954&partnerID=8YFLogxK
U2 - 10.1109/CBMS58004.2023.00248
DO - 10.1109/CBMS58004.2023.00248
M3 - Conference contribution
AN - SCOPUS:85166475954
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 382
EP - 385
BT - Proceedings - 2023 IEEE 36th International Symposium on Computer-Based Medical Systems, CBMS 2023
A2 - Sicilia, Rosa
A2 - Kane, Bridget
A2 - Almeida, Joao Rafael
A2 - Spiliopoulou, Myra
A2 - Andrades, Jose Alberto Benitez
A2 - Placidi, Giuseppe
A2 - Gonzalez, Alejandro Rodriguez
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 June 2023 through 24 June 2023
ER -