MER signal processing for detection of surgical targets: a preliminary contribution.
DOI:
https://doi.org/10.47924/neurotarget202114Keywords:
MER, signal processing, Parkinson´s diseaseAbstract
In this paper, signals from microelectrode recording (MER) are described through mathematical features in order to discriminate the subthalamic nucleus from the other cerebral areas. The proposed method, based on MER signals, would be useful as a support for medical decisions during functional surgeries of patients with Parkinson's disease. In this work ten math’s features were analyzed: waveform length, RMS, variance, mean absolute value (MAV), mean frequency, median frequency, entropy, bandwidth, zero crossing and Willison amplitude in MER signals from subthalamic nucleus, thalamus, substantia nigra and uncertain area of two patients. The results show an acceptable discrimination between subthalamic nucleus and other areas of basal ganglia, using at least one feature of the signal.
Metrics
References
McNames J. Microelectrode Signal Analysis Techniques for Improved Localization, Microelectrode Recording in Movement Disorder Surgery. Thieme. 2004.
Bakstein E. Deep Brain Recordings in Parkinson´s Disease: Processing, Analysis and Fusion with Anatomical Models. Doctoral Thesis. Faculty of Electrical Engineering. Czech Technical University in Prague. 2016.
Burchiel KJ, Zvi I. Microelectrode recording in movement disorder surgery. Signal Processing and Pattern Recognition in Microelectrode Recordings. Thieme. 2004.
Goodman RR. Surgery for Parkinson’s Disease. Springer. 2019.
Favre J, Taha JM, Baumann T, Burchiel KJ. Computer analysis of the tonic, phasic, and kinesthetic activity of pallidal discharges in Parkinson patients. Surg Neurol. 1999; 51:665-673.
Sterio D, Beric A, Dogali M, Fazzini E, Alfaro G, Devinsky O. Neurophysiological properties of pallidal neurons in Parkinson’s Disease. Ann Neurol. 1994; 35:586-591.
Hurtado JM, Gray CM, Tamas LB, Sigvardt KA. Dynamics of tremor-related oscillations in the human globus pallidus: A single case study. Proc Natl Acad Sci US. 1999; 96:1674-1679.
Slavin KV, Holsapple J. Microelectrode Techniques: Equipment, Components, and Systems. In: Zvi I, Burchiel KJ. Microelectrode recording in movement disorder surgery. Thieme; 2004. 14 – 27. 9- Vargas Cardona HD, Álvarez MA, Orozco Gutiérrez Á. Representación óptima de señales MER aplicada a la identificación de estructuras cerebrales durante la estimulación cerebral profunda. Revista Tecnura. 2015; 19(45): 15-27.
Nazmi M, Rahman SI, Yamamoto S, Ahmad M, Malarvili S, Zamzuri H. Assesment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions. MDPI. 2017; 7(10).
Togneri R, Nordholm S. Spectral Entropy as speech features for speech recognition. ResearchGate; 2005.
Schiaffino L. STN area detection using K-NN classifiers for MER recordings in Parkinson patients during neurostimulator implant surgery. SABI; 2015. Journal of Physics: Conference Series 705 (2016) 012050. doi:10.1088/1742-6596/705/1/012050
Wan KR. A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease. Clinical Neurophysiology. Elsevier; 2018. 14- Ganesh RN. Applications, Challenges, and Advancements in Electromyography Signal Processing, University of Technology Sydney, Australia. SCOPUS; 2014. doi: 10.4018/978-1-4666-6090-8
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Sofía Jasón, Ricardo Berjano, Natalia López
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
