Using Ensemble of Hand-Feature Engineering and Machine Learning Classifiers for Refining the Subthalamic Nucleus Location from Micro-Electrode Recordings in Parkinson’s Disease
Abstract
:1. Introduction
2. Materials and Methods
3. Feature Extraction
3.1. Spike-Dependent Features
3.2. Spike-Independent Features
3.3. Feature Reduction
- Step 2: Sort feature weights in descending order.
- Step 3: Select the most distinctive k features by using the highest positive weights according to the threshold values (greater and equal than 0.5).
4. Classification Procedure and Performance Evaluation
- Support Vector Machine (SVM). As already stated, the SVM classifier has shown a good achievement for the STN location based on MERs or LFP data within various scenarios. In this work, the configuration model was chosen to avoid the risk of overfitting. Accordingly, a Radial Basis Function (RBF) kernel function was used, with the kernel scale set to 1 and constraint penalty set to 1.
- K-Nearest Neighbors (KNNs). In this study, the malhabonic distance metric is used to compute the distance between the test feature and training features belonging to M classes (in this case, M = 2), and each data input will be assigned into a specific class based on the minimum computed distance between the test data and K (in this case, K = 5) nearest training data.
- Random Forest (RF). Random Forest is a set of decision trees constructed to train each subgroup out of the whole data training set at each decision tree and then deliver a single decision that will be aggregated with all decisions to output the predicted class index. In this work, after the optimization process, each classifier has been set to 32 trees with a leaf size equal to 4.
- Decision Tree (DT). In the decision tree classifier, the decision rule is made up of the features outcome learned by three compromised principal nodes: root node, decision node, and leaf node, which handle the extracted features, the branches’ decision outcome, and the predicted class (STN IN and STN OUT), respectively.
- Discriminant Analysis (DA). In this algorithm, the input features are separated by a discriminate linear plane engendered from the estimation of the covariance matrix rate [22]. The DA plan aims at minimizing the variance of the features that belong to the same class and maximizing the variance of features between classes (STN-IN and STN-OUT).
- Neural Network (NN). Among multiple variants, we used three families of neural networks: the Probabilistic Neural Network (PNN), Feed-forward Neural Network (FNN) and Back-propagation Neural Network (BNN). For all of them, they comprise at least three layers: the input layer, the hidden layer, and the output layer. For training, the Stochastic Gradient Descent (SGD) is used. The topology consists of a single hidden layer with 28 units, and the last layer is softmax of two classes (STN-IN and STN-OUT).
5. Results
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Spike-Dependent Features
Appendix A.1. Spike Firing Rate (SFR)
Appendix A.2. Inter-Spike Interval Sequences (ISI)
Appendix A.3. Standard Deviation of ISI (SISI)
Appendix A.4. Pause Ratio (PR)
Appendix A.5. Pause Index (PI)
Appendix A.6. Bursting Rate (BR)
Appendix A.7. Modified Burst Index (MBI)
Appendix B. Spike-Independent Features
Appendix B.1. Bispectrum (Bs)
Appendix B.2. Zero Crossing (Zc)
Appendix B.3. Curve Length (Cl)
Appendix B.4. Integrator (In)
Appendix B.5. Root Mean Square (RMS)
Appendix B.6. Kurtosis (Ku)
Appendix B.7. Cumulative Bispectrum (Cb)
Appendix B.8. Average Amplitude Change (AAc)
Appendix B.9. Short-Term Fourier Transform (STFT)
Appendix B.10. Teager Energy (Te)
Appendix B.11. Average First Amplitude Difference (AFa)
Appendix B.12. Second Amplitude Difference (SAd)
Appendix B.13. Standard Deviation (SD)
Appendix B.14. Skewness (Sk)
Appendix B.15. Slope Sign Change (Sc)
Appendix B.16. Variance (Va)
Appendix B.17. 1D-Local Binary Pattern (LBP1)
Appendix C. Scatter Plots for the Most Discriminant Features Used in the STN Area Detection
References
- DeMaagd, G.; Philip, A. Parkinson’s disease and its management: Part 1: Disease entity, risk factors, pathophysiology, clinical presentation, and diagnosis. Pharm. Ther. 2015, 40, 504. [Google Scholar]
- Delamarre, A.; Meissner, W.G. Epidemiology, environmental risk factors and genetics of Parkinson’s disease. La Presse Médicale 2017, 46, 175–181. [Google Scholar] [CrossRef] [PubMed]
- Dorsey, E.R.; Elbaz, A.; Nichols, E.; Abbasi, N.; Abd-Allah, F.; Abdelalim, A.; Adsuar, J.C.; Ansha, M.G.; Brayne, C.; Choi, J.Y.J.; et al. Global, regional, and national burden of Parkinson’s disease, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2018, 17, 939–953. [Google Scholar] [CrossRef] [PubMed]
- Groiss, S.; Wojtecki, L.; Südmeyer, M.; Schnitzler, A. Deep brain stimulation in Parkinson’s disease. Ther. Adv. Neurol. Disord. 2009, 2, 379–391. [Google Scholar] [CrossRef] [PubMed]
- Martinez-Martin, P.; Skorvanek, M.; Henriksen, T.; Lindvall, S.; Domingos, J.; Alobaidi, A.; Kandukuri, P.L.; Chaudhari, V.S.; Patel, A.B.; Parra, J.C.; et al. Impact of advanced Parkinson’s disease on caregivers: An international real-world study. J. Neurol. 2023, 270, 2162–2173. [Google Scholar] [CrossRef] [PubMed]
- Lozano, C.S.; Tam, J.; Lozano, A.M. The changing landscape of surgery for Parkinson’s Disease. Mov. Disord. 2018, 33, 36–47. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.; Zhu, G.; Liu, Y.; Liu, D.; Yuan, T.; Zhang, X.; Jiang, Y.; Du, T.; Zhang, J. Predict initial subthalamic nucleus stimulation outcome in Parkinson’s disease with brain morphology. CNS Neurosci. Ther. 2022, 28, 667–676. [Google Scholar] [CrossRef] [PubMed]
- Chen, S.Y.; Tsai, S.T.; Hung, H.Y.; Lin, S.H.; Pan, Y.H.; Lin, S.Z. Targeting the Subthalamic Nucleus for Deep Brain Stimulation—A Comparative Study Between Magnetic Resonance Images Alone and Fusion with Computed Tomographic Images. World Neurosurg. 2011, 75, 132–137. [Google Scholar] [CrossRef]
- Umemura, A.; Oka, Y.; Yamada, K.; Oyama, G.; Shimo, Y.; Hattori, N. Validity of Single Tract Microelectrode Recording in Subthalamic Nucleus Stimulation. Neurol. Med.-Chir. 2013, 53, 821–827. [Google Scholar] [CrossRef]
- Wu, B.; Xu, J.; Zhang, C.; Ling, Y.; Yang, C.; Xuan, R.; Wang, S.; Guo, Q.; Zeng, Z.; Jiang, N.; et al. The Effect of Surgical Positioning on Pneumocephalus in Subthalamic Nucleus Deep Brain Stimulation Surgery for Parkinson Disease. Neuromodul. Technol. Neural Interface 2022, 26, 1714–1723. [Google Scholar] [CrossRef]
- Vinke, R.S.; Geerlings, M.; Selvaraj, A.K.; Georgiev, D.; Bloem, B.R.; Esselink, R.A.; Bartels, R.H. The Role of Microelectrode Recording in Deep Brain Stimulation Surgery for Parkinson’s Disease: A Systematic Review and Meta-Analysis. J. Park. Dis. 2022, 12, 2059–2069. [Google Scholar] [CrossRef] [PubMed]
- Wan, K.R.; Maszczyk, T.; See, A.A.Q.; Dauwels, J.; King, N.K.K. A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease. Clin. Neurophysiol. 2019, 130, 145–154. [Google Scholar] [CrossRef] [PubMed]
- Tepper, Á.; Henrich, M.C.; Schiaffino, L.; Rosado Munoz, A.; Gutiérrez, A.; Guerrero Martinez, J. Selection of the optimal algorithm for real-time estimation of beta band power during DBS surgeries in patients with Parkinson’s disease. Comput. Intell. Neurosci. 2017, 2017, 1512504. [Google Scholar] [CrossRef] [PubMed]
- Karthick, P.; Wan, K.R.; Yuvaraj, R.; See, A.A.; King, N.K.K.; Dauwels, J. Detection of subthalamic nucleus using time-frequency features of microelectrode recordings and random forest classifier. In Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; pp. 4164–4167. [Google Scholar]
- Lourens, M.; Meijer, H.; Contarino, M.; Van den Munckhof, P.; Schuurman, P.; Van Gils, S.; Bour, L. Functional neuronal activity and connectivity within the subthalamic nucleus in Parkinson’s disease. Clin. Neurophysiol. 2013, 124, 967–981. [Google Scholar] [CrossRef] [PubMed]
- Novak, P.; Przybyszewski, A.W.; Barborica, A.; Ravin, P.; Margolin, L.; Pilitsis, J.G. Localization of the subthalamic nucleus in Parkinson disease using multiunit activity. J. Neurol. Sci. 2011, 310, 44–49. [Google Scholar] [CrossRef]
- Hosny, M.; Zhu, M.; Su, Y.; Gao, W.; Fu, Y. A novel deep recurrent convolutional neural network for subthalamic nucleus localization using local field potential signals. Biocybern. Biomed. Eng. 2021, 41, 1561–1574. [Google Scholar] [CrossRef]
- Rajpurohit, V.; Danish, S.F.; Hargreaves, E.L.; Wong, S. Optimizing computational feature sets for subthalamic nucleus localization in DBS surgery with feature selection. Clin. Neurophysiol. 2015, 126, 975–982. [Google Scholar] [CrossRef] [PubMed]
- Hosny, M.; Zhu, M.; Gao, W.; Fu, Y. Detection of subthalamic nucleus using novel higher-order spectra features in microelectrode recordings signals. Biocybern. Biomed. Eng. 2021, 41, 704–716. [Google Scholar] [CrossRef]
- Hosny, M.; Zhu, M.; Gao, W.; Fu, Y. Deep convolutional neural network for the automated detection of Subthalamic nucleus using MER signals. J. Neurosci. Methods 2021, 356, 109145. [Google Scholar] [CrossRef]
- Rosado-Muñoz, A.; Guerrero-Martínez, J.F.; Gutierrez Martín, A. Microelectrode register (MER) data from Deep Brain Stimulation (DBS) surgery in Parkinson’s disease patients. DATASET. Zenodo 2022. [Google Scholar] [CrossRef]
- Toosi, R.; Akhaee, M.A.; Dehaqani, M.R.A. An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions. Sci. Rep. 2021, 11, 13925. [Google Scholar] [CrossRef] [PubMed]
- Schwalger, T. The Interspike-Interval Statistics of Non-Renewal Neuron Models. Ph.D. Thesis, Humboldt-Universität zu Berlin, Berlin, Germany, 2013. [Google Scholar] [CrossRef]
- Toledo-Pérez, D.C.; Rodríguez-Reséndiz, J.; Gómez-Loenzo, R.A.; Jauregui-Correa, J. Support vector machine-based EMG signal classification techniques: A review. Appl. Sci. 2019, 9, 4402. [Google Scholar] [CrossRef]
- Yuvaraj, R.; Rajendra Acharya, U.; Hagiwara, Y. A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals. Neural Comput. Appl. 2018, 30, 1225–1235. [Google Scholar] [CrossRef]
- Caesarendra, W.; Tjahjowidodo, T. A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing. Machines 2017, 5, 21. [Google Scholar] [CrossRef]
- Yahyaei, R.; Özkurt, T.E. Mean curve length: An efficient feature for brainwave biometrics. Biomed. Signal Process. Control 2022, 76, 103664. [Google Scholar] [CrossRef]
- Sharma, L.; Dandapat, S.; Mahanta, A. Kurtosis-based noise estimation and multiscale energy to denoise ECG signal. Signal, Image Video Process. 2013, 7, 235–245. [Google Scholar] [CrossRef]
- Yuegang, W.; Shao, J.; Hongtao, X. Non-stationary Signals Processing Based on STFT. In Proceedings of the 2007 8th International Conference on Electronic Measurement and Instruments, Xi’an, China, 16–18 August 2007; pp. 3-301–3-304. [Google Scholar] [CrossRef]
- Choi, J.H.; Jung, H.K.; Kim, T. A new action potential detector using the MTEO and its effects on spike sorting systems at low signal-to-noise ratios. IEEE Trans. Biomed. Eng. 2006, 53, 738–746. [Google Scholar] [CrossRef] [PubMed]
- Attallah, B.; Serir, A.; Chahir, Y. Feature extraction in palmprint recognition using spiral of moment skewness and kurtosis algorithm. Pattern Anal. Appl. 2019, 22, 1197–1205. [Google Scholar] [CrossRef]
- Benouis, M.; Mostefai, L.; Costen, N.; Regouid, M. ECG based biometric identification using one-dimensional local difference pattern. Biomed. Signal Process. Control 2021, 64, 102226. [Google Scholar] [CrossRef]
- Houam, L.; Hafiane, A.; Boukrouche, A.; Lespessailles, E.; Jennane, R. One dimensional local binary pattern for bone texture characterization. Pattern Anal. Appl. 2014, 17, 179–193. [Google Scholar] [CrossRef]
- Yang, W.; Wang, K.; Zuo, W. Neighborhood component feature selection for high-dimensional data. J. Comput. 2012, 7, 161–168. [Google Scholar] [CrossRef]
- Paluszek, M.; Thomas, S.; Paluszek, M.; Thomas, S. MATLAB machine learning toolboxes. In Practical MATLAB Deep Learning: A Project-Based Approach; Apress: Berkeley, CA, USA, 2020; pp. 25–41. [Google Scholar]
- Chaovalitwongse, W.A.; Jeong, Y.S.; Jeong, M.K.; Danish, S.F.; Wong, S. Pattern recognition approaches for identifying subcortical targets during deep brain stimulation surgery. IEEE Intell. Syst. 2011, 26, 54–63. [Google Scholar] [CrossRef]
- Wong, S.; Baltuch, G.; Jaggi, J.; Danish, S. Functional localization and visualization of the subthalamic nucleus from microelectrode recordings acquired during DBS surgery with unsupervised machine learning. J. Neural Eng. 2009, 6, 026006. [Google Scholar] [CrossRef]
- Cagnan, H.; Dolan, K.; He, X.; Contarino, M.F.; Schuurman, R.; Van Den Munckhof, P.; Wadman, W.J.; Bour, L.; Martens, H.C. Automatic subthalamic nucleus detection from microelectrode recordings based on noise level and neuronal activity. J. Neural Eng. 2011, 8, 046006. [Google Scholar] [CrossRef] [PubMed]
- Guillén, P.; Martinez-de Pison, F.; Sanchez, R.; Argáez, M.; Velázquez, L. Characterization of subcortical structures during deep brain stimulation utilizing support vector machines. In Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August–3 September 2011; pp. 7949–7952. [Google Scholar]
- Padilla, J.; Vargas Cardona, H.; Arango, A.; Carmona, H.; Álvarez, M.; Guijarro, E.; Orozco, A. NEUROZONE: On-line recognition of brain structures in stereotactic surgery–Application to Parkinson’s disease surgery. In Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA, 28 August–1 September 2012. [Google Scholar]
- Schiaffino, L.; Muñoz, A.R.; Martínez, J.G.; Villora, J.F.; Gutiérrez, A.; Torres, I.M. STN area detection using K-NN classifiers for MER recordings in Parkinson patients during neurostimulator implant surgery. In Proceedings of the Journal of Physics: Conference Series; IOP Publishing: Bristol, UK, 2016; Volume 705, p. 012050. [Google Scholar]
- Schiaffino, L.; Rosado Muñoz, A.; Francés Villora, J.; Bataller, M.; Gutiérrez, A.; Martínez Torres, I.; Teruel-Martí, V.; Guerrero Martínez, J. Feature selection for KNN classifier to improve accurate detection of subthalamic nucleus during deep brain stimulation surgery in Parkinson’s patients. In VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, 26–28 October 2016; Torres, I., Bustamante, J., Sierra, D.A., Eds.; Springer: Singapore, 2017; pp. 441–444. [Google Scholar] [CrossRef]
- Valsky, D.; Marmor-Levin, O.; Deffains, M.; Eitan, R.; Blackwell, K.T.; Bergman, H.; Israel, Z. Stop! border ahead: A utomatic detection of subthalamic exit during deep brain stimulation surgery. Mov. Disord. 2017, 32, 70–79. [Google Scholar] [CrossRef] [PubMed]
- Bellino, G.M.; Schiaffino, L.; Battisti, M.; Guerrero, J.; Rosado-Muñoz, A. Optimization of the KNN supervised classification algorithm as a support tool for the implantation of deep brain stimulators in patients with Parkinson’s disease. Entropy 2019, 21, 346. [Google Scholar] [CrossRef]
- Khosravi, M.; Atashzar, S.F.; Gilmore, G.; Jog, M.S.; Patel, R.V. Unsupervised clustering of micro-electrophysiological signals for localization of subthalamic nucleus during DBS surgery. In Proceedings of the 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), San Francisco, CA, USA, 20–23 March 2019; pp. 17–20. [Google Scholar]
- Khosravi, M.; Atashzar, S.F.; Gilmore, G.; Jog, M.S.; Patel, R.V. Intraoperative localization of STN during DBS surgery using a data-driven model. IEEE J. Transl. Eng. Health Med. 2020, 8, 1–9. [Google Scholar] [CrossRef] [PubMed]
- Karthick, P.; Wan, K.R.; Qi, A.S.A.; Dauwels, J.; King, N.K.K. Automated detection of subthalamic nucleus in deep brain stimulation surgery for Parkinson’s disease using microelectrode recordings and wavelet packet features. J. Neurosci. Methods 2020, 343, 108826. [Google Scholar] [CrossRef]
- Coelli, S.; Levi, V.; Del Vecchio, J.D.V.; Mailland, E.; Rinaldo, S.; Eleopra, R.; Bianchi, A.M. An intra-operative feature-based classification of microelectrode recordings to support the subthalamic nucleus functional identification during deep brain stimulation surgery. J. Neural Eng. 2021, 18, 016003. [Google Scholar] [CrossRef]
- Vinke, R.S.; Selvaraj, A.K.; Geerlings, M.; Georgiev, D.; Sadikov, A.; Kubben, P.L.; Doorduin, J.; Praamstra, P.; Bloem, B.R.; Bartels, R.H.; et al. The Role of Microelectrode Recording and Stereotactic Computed Tomography in Verifying Lead Placement during Awake MRI-Guided Subthalamic Nucleus Deep Brain Stimulation for Parkinson’s Disease. J. Park. Dis. 2022, 12, 1269–1278. [Google Scholar] [CrossRef]
- Watts, J.; Khojandi, A.; Shylo, O.; Ramdhani, R.A. Machine learning’s application in deep brain stimulation for Parkinson’s disease: A review. Brain Sci. 2020, 10, 809. [Google Scholar] [CrossRef] [PubMed]
- Koirala, N.; Serrano, L.; Paschen, S.; Falk, D.; Anwar, A.R.; Kuravi, P.; Deuschl, G.; Groppa, S.; Muthuraman, M. Mapping of subthalamic nucleus using microelectrode recordings during deep brain stimulation. Sci. Rep. 2020, 10, 19241. [Google Scholar] [CrossRef] [PubMed]
- Martin, T.; Peralta, M.; Gilmore, G.; Sauleau, P.; Haegelen, C.; Jannin, P.; Baxter, J.S. Extending convolutional neural networks for localizing the subthalamic nucleus from micro-electrode recordings in Parkinson’s disease. Biomed. Signal Process. Control 2021, 67, 102529. [Google Scholar] [CrossRef]
Patient | STN-IN(L+R) | STN-OUT(L+R) | STN-IN(L+R) | STN-OUT(L+R) | Total |
---|---|---|---|---|---|
Number | #Steps | #Steps | Seconds | Seconds | Seconds |
1 | 20+NA | 4+NA | 959+NA | 134+NA | 1093 |
2 | 18+31 | 14+3 | 1354+1855 | 591+199 | 3999 |
3 | 13+27 | 27+12 | 626+993 | 923+434 | 2976 |
4 | 21+21 | 11+9 | 946+1114 | 629+421 | 3110 |
5 | 26+NA | 15+NA | 1200+NA | 497+NA | 1697 |
6 | 15+NA | 25+NA | 941+NA | 1528+NA | 2469 |
7 | 23+NA | 7+NA | 1632+NA | 731+NA | 2363 |
8 | 22+17 | 11+26 | 1229+1041 | 454+1141 | 3865 |
9 | 30+37 | 8+4 | 1248+1322 | 285+114 | 2969 |
10 | 32+32 | 15+13 | 1147+1852 | 921+557 | 4477 |
11 | 18+NA | 19+NA | 1024+NA | 939+NA | 1963 |
12 | 30+30 | 11+12 | 1415+995 | 380+416 | 3206 |
13 | 29+25 | 18+14 | 1429+1119 | 548+640 | 3736 |
14 | NA+24 | NA+11 | NA+1129 | NA+461 | 1590 |
TOTAL | 297+244 | 185+104 | 15,150+11,420 | 8560+4383 | 39,513 |
Features | Description |
---|---|
Spike Firing Rate (SFR) | Count of spikes per second. |
Inter Spike Interval (ISI) | Time intervals between consecutive spikes in the spike sequence [23]. |
Standard deviation of (SISI) | Standard deviation of inter-spike intervals . |
Pause Ratio (PR) | Ratio of cumulative time of greater than 50 ms to the cumulative time of ISI less than 50 ms. |
Pause Index (PI) | Ratio of the number of greater than 50 ms to the number of less than 50 ms. |
Bursting Rate (BR) | Number of bursts per second. |
Modified Burst Index (MBI) | Ratio of the number of less than 10 ms to the number of greater than 10 ms. |
Features | Description |
---|---|
Bispectrum (Bs) | The Fourier transform of the third-order cumulant spectrum [25]. |
Zero crossing (Zc) | Number of zero crossings in the temporal domain [24]. |
Curve length (Cl) | Sum of the difference of consecutive samples [27]. |
Integrator (In) | Sum of absolute value [24]. |
Root Mean Square (RMS) | Root mean square [24]. |
Kurtosis (Ku) | Peakiness coefficient variation [28]. |
Cumulative bispectrum (Cb) | Cumulative relationship between the higher-order moments [25]. |
Average Amplitude Change (AAc) | Average value of all amplitudes [24]. |
Short-Term Fourier Transform (STFT) | Spectrum energy from each MER segment [29]. |
Teager Energy (Te) | Energy rate induced from the second-order differential derivative of the amplitude and frequency variation [30]. |
Average First Amplitude difference (AFa) | Average of first amplitude difference [24]. |
Second Amplitude difference (SAd) | Average of second amplitude difference [24]. |
Standard deviation (SD) | Standard deviation [24]. |
Skewness (Sk) | Measure to quantify the presence of assymmetry in the shape of the distribution [31]. |
Slope Sign change (SSc) | Number of times that the slope of the MER curve changes its sign in the segment [24]. |
Variance (Va) | The signal’s averaged power [24]. |
1D-Local Binary Pattern (LBP1) | Having the central segment as threshold, it is compared with its neighborhood to generate binary code [32,33]. |
ML Classifier | KNNs | SVM | RF | DT | DA | PNN |
---|---|---|---|---|---|---|
The 11 features selected | 99.9 | 100.0 | 99.9 | 99.8 | 100.0 | 100.0 |
All 24 features selected | 99.8 | 99.9 | 99.5 | 99.9 | 99.8 | 100.0 |
Patient | Precision | Recall | Kappa | F1-Score | Accuracy |
---|---|---|---|---|---|
P1 | 100 | 100 | 86.8 | 100 | 100 |
P2 | 100 | 100 | 87.0 | 100 | 100 |
P3 | 86.9 | 95.1 | 87.7 | 90.1 | 96.3 |
P4 | 100 | 99.9 | 84.9 | 99.9 | 100 |
P5 | 96.6 | 99.6 | 87.9 | 98.0 | 99.5 |
P6 | 99.8 | 99.8 | 84.4 | 99.8 | 99.9 |
P7 | 99.8 | 99.8 | 84.8 | 99.8 | 99.9 |
P8 | 99.8 | 99.9 | 88.4 | 99.3 | 99.6 |
P9 | 98.8 | 99.9 | 88.4 | 99.3 | 99.6 |
P10 | 99.6 | 99.5 | 81.2 | 99.5 | 99.6 |
P11 | 99.8 | 99.8 | 80.8 | 99.8 | 99.8 |
P12 | 99.7 | 100 | 86.1 | 99.8 | 99.9 |
P13 | 99.7 | 100 | 86.1 | 99.8 | 99.9 |
P14 | 99.5 | 99.9 | 87.0 | 99.6 | 99.7 |
Authors | # Patients | # Features | Classifier | Training | Accuracy (%) |
---|---|---|---|---|---|
Wong et al. [37] | 27 | 13 SD and SIN | Fuzzy clustering | Hold-out | 89.6 |
Cangan et al. [38] | 48 | 3 SD and SIN | Unsupervised | NA | 88.0 |
Chaovalitwongse et al. [36] | 17 | 3 SD and SIN | Bayesian, KNNs, and KNNs-DTW | LOOCV | 89.6 |
Guillen et al. [39] | 4 | 6 SIN | SVM | 10-fold CV | 99.4 |
Vargas Cardona et al. [40] | 4 | 3 SD and SIN | Bayesian and KNNs | NA | 85.0 |
Rajpurohit et al. [18] | 26 | 13 SD and SIN | Logistic regression | 10-fold CV | 84.0 |
Schiaffino et al. [41] | 8 | 15 SD and SIN | KNNs and Fuzzy KNNs | LOOCV | 72.0 |
Schiaffino et al. [42] | 15 | 16 SD and SIN | KNNs | 10-fold CV | 86.1 |
Valsky et al. [43] | 81 | 2 SIN | SVM and HMM | 10-fold CV | 94.0 |
Bellino et al. [44] | 14 | 18 SD and SIN | KNNs | LOOCV | 94.4 |
Karthick et al. [14] | 26 | 1 SIN | Random Forest | LOOCV | 83.0 |
Khosravi et al. [45] | 50 | 1 SIN | K-means clustering and SOM | k-CV | 80.0 |
Khosravi et al. [46] | 100 | 11 SD and SIN | Deep Neural network | 10-fold CV | 92.0 |
Karthick. et al. [47] | 26 | 1 SIN | Random Forest, SVM, and KNNs | LOOCV | 94.0 |
S Coelli et al. [48] | 13 | 26 SD and SIN | Decision trees | LOOCV | 94.1 |
Hosny et al. [19] | 21 | 12 SD and SIN | Bagging, KNNs, SVM, Decision Tree, and AdaBoost | LOOCV | 94.8 |
Our work | 14 | 11 SD and SIN | SVM, KNNs, RF, DT, DA, and NN | LOOCV | 99.8 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Benouis, M.; Rosado-Muñoz, A. Using Ensemble of Hand-Feature Engineering and Machine Learning Classifiers for Refining the Subthalamic Nucleus Location from Micro-Electrode Recordings in Parkinson’s Disease. Appl. Sci. 2024, 14, 5157. https://doi.org/10.3390/app14125157
Benouis M, Rosado-Muñoz A. Using Ensemble of Hand-Feature Engineering and Machine Learning Classifiers for Refining the Subthalamic Nucleus Location from Micro-Electrode Recordings in Parkinson’s Disease. Applied Sciences. 2024; 14(12):5157. https://doi.org/10.3390/app14125157
Chicago/Turabian StyleBenouis, Mohamed, and Alfredo Rosado-Muñoz. 2024. "Using Ensemble of Hand-Feature Engineering and Machine Learning Classifiers for Refining the Subthalamic Nucleus Location from Micro-Electrode Recordings in Parkinson’s Disease" Applied Sciences 14, no. 12: 5157. https://doi.org/10.3390/app14125157
APA StyleBenouis, M., & Rosado-Muñoz, A. (2024). Using Ensemble of Hand-Feature Engineering and Machine Learning Classifiers for Refining the Subthalamic Nucleus Location from Micro-Electrode Recordings in Parkinson’s Disease. Applied Sciences, 14(12), 5157. https://doi.org/10.3390/app14125157