Automated detection of subthalamic nucleus in deep brain stimulation surgery for Parkinson's disease using microelectrode recordings and wavelet packet features.
J Neurosci Methods
; 343: 108826, 2020 09 01.
Article
em En
| MEDLINE
| ID: mdl-32622981
ABSTRACT
BACKGROUND:
Deep brain stimulation (DBS) to the subthalamic nucleus (STN) is an effective neurosurgery that overcomes the motor system alternations of patients with advanced Parkinson's disease. The most challenging aspect of DBS surgery is the accurate identification of STN and its borders. In general, it is performed manually by a neurophysiologist using the microelectrode recordings (MERs). This process is subjective, and tedious and further, interpretation of MERs is difficult because of its inherent nonstationary variations. NEWMETHODS:
In this work, the wavelet-packet based features are proposed to automatically localize the STN and its subcortical structures using microelectrode recorded signals during DBS surgery. The study analyses 2904 MERs of 26 PD patients who underwent DBS implantation. The low and high order statistical parameters are extracted from the wavelet packet coefficients of MERs and used in the classifications, namely, non-STN vs. STN, pre-STN vs. STN and STN vs. post-STN.RESULTS:
Most of the features are significantly different in STN and its subcortical regions, namely, pre-STN and post-STN. The proposed features achieve an average accuracy of 85 % in non-STN vs. STN, 87.2 % in pre-STN vs. STN and 77.7 % in STN vs. post-STN. The accuracy is improved by around 10 % in non-STN vs. STN and STN vs. post-STN when the transition error is 1 mm. COMPARISON WITH EXISTINGMETHODS:
The proposed features are found to be better than the wavelet features.CONCLUSIONS:
The proposed approach could be a potential useful adjunct for the real-time rapid intraoperative identification of STN and its anatomical borders.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Doença de Parkinson
/
Núcleo Subtalâmico
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Estimulação Encefálica Profunda
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article