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Hosny M, Zhu M, Gao W, Elshenhab AM. STN localization using local field potentials based on wavelet packet features and stacking ensemble learning. J Neurosci Methods 2024; 407:110156. [PMID: 38703796 DOI: 10.1016/j.jneumeth.2024.110156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 02/20/2024] [Accepted: 04/27/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND DBS entails the insertion of an electrode into the patient brain, enabling Subthalamic nucleus (STN) stimulation. Accurate delineation of STN borders is a critical but time-consuming task, traditionally reliant on the neurosurgeon experience in deciphering the intricacies of microelectrode recording (MER). While clinical outcomes of MER have been satisfactory, they involve certain risks to patient safety. Recently, there has been a growing interest in exploring the potential of local field potentials (LFP) due to their correlation with the STN motor territory. METHOD A novel STN detection system, integrating LFP and wavelet packet transform (WPT) with stacking ensemble learning, is developed. Initial steps involve the inclusion of soft thresholding to increase robustness to LFP variability. Subsequently, non-linear WPT features are extracted. Finally, a unique ensemble model, comprising a dual-layer structure, is developed for STN localization. We harnessed the capabilities of support vector machine, Decision tree and k-Nearest Neighbor in conjunction with long short-term memory (LSTM) network. LSTM is pivotal for assigning adequate weights to every base model. RESULTS Results reveal that the proposed model achieved a remarkable accuracy and F1-score of 89.49% and 91.63%. COMPARISON WITH EXISTING METHODS Ensemble model demonstrated superior performance when compared to standalone base models and existing meta techniques. CONCLUSION This framework is envisioned to enhance the efficiency of DBS surgery and reduce the reliance on clinician experience for precise STN detection. This achievement is strategically significant to serve as an invaluable tool for refining the electrode trajectory, potentially replacing the current methodology based on MER.
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Affiliation(s)
- Mohamed Hosny
- Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha, Egypt.
| | - Minwei Zhu
- First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Wenpeng Gao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Ahmed M Elshenhab
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
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Saini R, Tiwari AK, Nath A, Singh P, Maurya SP, Shah MA. Covering assisted intuitionistic fuzzy bi-selection technique for data reduction and its applications. Sci Rep 2024; 14:13568. [PMID: 38866851 DOI: 10.1038/s41598-024-62099-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.
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Affiliation(s)
- Rajat Saini
- Department of Mathematics, School of Basic Sciences, Central University of Haryana, Mahendergarh, 123031, India
| | - Anoop Kumar Tiwari
- Department of Computer Science and Information Technology, Central University of Haryana, Mahendergarh, 123031, India.
| | - Abhigyan Nath
- Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, 492001, India
| | - Phool Singh
- Department of Mathematics (SoET), Central University of Haryana, Mahendergarh, 123031, India
| | - S P Maurya
- Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi, 221005, India
| | - Mohd Asif Shah
- Department of Economics, Kebri Dehar University, 250, Kebri Dehar, Somali, Ethiopia.
- Division of Research and Development, Lovely Professional University, Phagwara, Punjab, 144001, India.
- Department of Economics, Kardan University, Parwan e Du, Kabul, 1001, Afghanistan.
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Hosny M, Zhu M, Gao W, Fu Y. A novel deep learning model for STN localization from LFPs in Parkinson’s disease. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Abstract
This paper proposes a feature selection model based on a multilayer genetic algorithm (GA) to select the features of a high stock dividend (HSD) and eliminate the relatively redundant features in the optimal solution by using layer-by-layer information transfer and two-dimensionality reduction methods. Combining the ensemble model and time-series split cross-validation (TSCV) indicator as the fitness function solves the problem of selecting the fitness function for each layer. The symmetry character of the model is fully utilized in the two-dimensionality reduction processes, according to the change in data dimensions and the unbalanced characteristics of the HSD, setting the corresponding TSCV indicators. We built seven ensemble prediction models for actual stock trading data for comparison experiments. The results show that the feature selection model based on multilayer GA can effectively eliminate the relatively redundant features after dimensionality reduction and significantly improve the balancing accuracy, precision and AUC performance of the seven ensemble learning models. Finally, adversarial validation is used to analyze the differences in the balanced accuracy of the training and test sets caused by the inconsistent distribution of the data sets.
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Rao AT, Lu CW, Askari A, Malaga KA, Chou KL, Patil PG. Clinically-derived oscillatory biomarker predicts optimal subthalamic stimulation for Parkinson's disease. J Neural Eng 2022; 19. [PMID: 35272281 DOI: 10.1088/1741-2552/ac5c8c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/10/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Choosing the optimal electrode trajectory, stimulation location, and stimulation amplitude in subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson's disease (PD) remains a time-consuming empirical effort. In this retrospective study, we derive a data-driven electrophysiological biomarker that predicts clinical DBS location and parameters, and we consolidate this information into a quantitative score that may facilitate an objective approach to STN DBS surgery and programming. APPROACH Random-forest feature selection was applied to a dataset of 1046 microelectrode recordings sites across 20 DBS implant trajectories to identify features of oscillatory activity that predict clinically programmed volumes of tissue activation (VTA). A cross-validated classifier was used to retrospectively predict VTA regions from these features. Spatial convolution of probabilistic classifier outputs along MER trajectories produced a biomarker score that reflects the probability of localization within a clinically optimized VTA. MAIN RESULTS Biomarker scores peaked within the VTA region and were significantly correlated with percent improvement in postoperative motor symptoms (MDS-UPRDS Part III, R = 0.61, p = 0.004). Notably, the length of STN, a common criterion for trajectory selection, did not show similar correlation (R = -0.31, p = 0.18). These findings suggest that biomarker-based trajectory selection and programming may improve motor outcomes by 9 ± 3 percentage points (p = 0.047) in this dataset. SIGNIFICANCE A clinically defined electrophysiological biomarker not only predicts VTA size and location but also correlates well with motor outcomes. Use of this biomarker for trajectory selection and initial stimulation may potentially simplify STN DBS surgery and programming.
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Affiliation(s)
- Akshay T Rao
- Biomedical Engineering, University of Michigan, 1500 East Medical Center Dr., SPC 5338, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| | - Charles W Lu
- Biomedical Engineering, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| | - Asra Askari
- Biomedical Engineering, University of Michigan, 1500 E Medical Center Drive, SPC 5338, Ann Arbor, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| | - Karlo A Malaga
- Biomedical Engineering, Bucknell University, 316 Academic East Building, Lewisburg, Pennsylvania, 17837, UNITED STATES
| | - Kelvin L Chou
- Neurosurgery, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, Michigan, 48109-5338, UNITED STATES
| | - Parag G Patil
- Neurosurgery, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, Michigan, 48109-5338, UNITED STATES
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Lin CC, Kang JR, Liang YL, Kuo CC. Simultaneous feature and instance selection in big noisy data using memetic variable neighborhood search. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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A novel deep recurrent convolutional neural network for subthalamic nucleus localization using local field potential signals. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.09.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Detection of subthalamic nucleus using novel higher-order spectra features in microelectrode recordings signals. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.04.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Coelli S, Levi V, Del Vecchio Del Vecchio J, Mailland E, Rinaldo S, Eleopra R, Bianchi AM. An intra-operative feature-based classification of microelectrode recordings to support the subthalamic nucleus functional identification during deep brain stimulation surgery. J Neural Eng 2020; 18. [PMID: 33202390 DOI: 10.1088/1741-2552/abcb15] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 11/17/2020] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The Subthalamic Nucleus (STN) is the most selected target for the placement of the Deep Brain Stimulation (DBS) electrode to treat Parkinson's disease. Its identification is a delicate and challenging task which is based on the interpretation of the STN functional activity acquired through microelectrode recordings (MER). Aim of this work is to explore the potentiality of a set of twenty-five features to build a classification model for the discrimination of MER signals belonging to the STN. APPROACH We explored the use of different sets of spike-dependent and spike-independent features in combination with an Ensemble Trees classification (ET) algorithm on a dataset composed of thirteen patients receiving bilateral DBS. We compared results from six subsets of features and two dataset conditions (with and without standardization) using performance metrics on a leave-one-patient-out validation schema. MAIN RESULTS We obtained statistically better results (i.e., higher accuracy p-value = 0.003) on the raw dataset than on the standardized one, where the selection of seven features using a minimum redundancy maximum relevance (MRMR) algorithm provided a mean accuracy of 94.1%, comparable with the use of the full set of features. In the same conditions, the spike-dependent features provided the lowest accuracy (86.8%), while a power density-based index was shown to be a good indicator of STN activity (92.3%). SIGNIFICANCE Results suggest that a small and simple set of features can be used for an efficient classification of microelectrode recordings to implement an intraoperative support for clinical decision during deep brain stimulation surgery.
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Affiliation(s)
- Stefania Coelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Lombardia, ITALY
| | - Vincenzo Levi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Lombardia, ITALY
| | | | - Enrico Mailland
- Neurology Unit, Dipartimento di Area Medica Internistica, ASST Santi Paolo e Carlo, Milano, Lombardia, ITALY
| | - Sara Rinaldo
- Movement Disorder Unit, Department of Clinical Neurosciences, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Lombardia, ITALY
| | - Roberto Eleopra
- Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Lombardia, ITALY
| | - Anna Maria Bianchi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Lombardia, ITALY
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Coelli S, Levi V, Del Vecchio Del Vecchio J, Mailland E, Rinaldo S, Eleopra R, Bianchi AM. Characterization of Microelectrode Recordings for the Subthalamic Nucleus identification in Parkinson's disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3485-3488. [PMID: 33018754 DOI: 10.1109/embc44109.2020.9175299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's disease, when the pharmacological approach has no more effect. DBS efficacy strongly depends on the accurate localization of the STN and the adequate positioning of the stimulation electrode during DBS stereotactic surgery. During this procedure, the analysis of microelectrode recordings (MER) is fundamental to assess the correct localization. Therefore, in this work, we explore different signal feature types for the characterization of the MER signals associated to STN from NON-STN structures. We extracted a set of spike-dependent (action potential domain) and spike-independent features in the time and frequency domain to evaluate their usefulness in distinguishing the STN from other structures. We discuss the results from a physiological and methodological point of view, showing the superiority of features having a direct electrophysiological interpretation.Clinical Relevance- The identification of a simple, clinically interpretable, and powerful set of features for the STN localization would support the clinical positioning of the DBS electrode, improving the treatment outcome.
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