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Cisotto G, Capuzzo M, Guglielmi AV, Zanella A. Feature stability and setup minimization for EEG-EMG-enabled monitoring systems. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2022; 2022:103. [PMID: 36320592 PMCID: PMC9612609 DOI: 10.1186/s13634-022-00939-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Delivering health care at home emerged as a key advancement to reduce healthcare costs and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training applications, wearable and portable devices can be employed for movement recognition and monitoring of the associated brain signals. This is one of the contexts where it is essential to minimize the monitoring setup and the amount of data to collect, process, and share. In this paper, we address this challenge for a monitoring system that includes high-dimensional EEG and EMG data for the classification of a specific type of hand movement. We fuse EEG and EMG into the magnitude squared coherence (MSC) signal, from which we extracted features using different algorithms (one from the authors) to solve binary classification problems. Finally, we propose a mapping-and-aggregation strategy to increase the interpretability of the machine learning results. The proposed approach provides very low mis-classification errors ( < 0.1 ), with very few and stable MSC features ( < 10 % of the initial set of available features). Furthermore, we identified a common pattern across algorithms and classification problems, i.e., the activation of the centro-parietal brain areas and arm's muscles in 8-80 Hz frequency band, in line with previous literature. Thus, this study represents a step forward to the minimization of a reliable EEG-EMG setup to enable gesture recognition.
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Affiliation(s)
- Giulia Cisotto
- Department of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy
- Inter-University Consortium for Telecommunications (CNIT), Padova, Italy
- Department of Informatics, Systems and Communications, University of Milano-Bicocca, Viale Sarca, 336, 20126 Milano, Italy
| | - Martina Capuzzo
- Department of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy
- Human Inspired Technologies Research Center, University of Padova, Via Luzzatti, 4, 35121 Padova, Italy
| | - Anna Valeria Guglielmi
- Department of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy
| | - Andrea Zanella
- Department of Information Engineering, University of Padova, Via Gradenigo, 6, 35121 Padova, Italy
- Inter-University Consortium for Telecommunications (CNIT), Padova, Italy
- Human Inspired Technologies Research Center, University of Padova, Via Luzzatti, 4, 35121 Padova, Italy
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Bi XA, Hu X, Xie Y, Wu H. A novel CERNNE approach for predicting Parkinson's Disease-associated genes and brain regions based on multimodal imaging genetics data. Med Image Anal 2020; 67:101830. [PMID: 33096519 DOI: 10.1016/j.media.2020.101830] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 07/24/2020] [Accepted: 09/01/2020] [Indexed: 12/13/2022]
Abstract
The detection and pathogenic factors analysis of Parkinson's disease (PD) has a practical significance for its diagnosis and treatment. However, the traditional research paradigms are commonly based on single neural imaging data, which is easy to ignore the complementarity between multimodal imaging genetics data. The existing researches also pay little attention to the comprehensive framework of patient detection and pathogenic factors analysis for PD. Based on functional magnetic resonance imaging (fMRI) data and single nucleotide polymorphism (SNP) data, a novel brain disease multimodal data analysis model is proposed in this paper. Firstly, according to the complementarity between the two types of data, the classical correlation analysis method is used to construct the fusion feature of subjects. Secondly, based on the artificial neural network, the fusion feature analysis tool named clustering evolutionary random neural network ensemble (CERNNE) is designed. This method integrates multiple neural networks constructed randomly, and uses clustering evolution strategy to optimize the ensemble learner by adaptive selective integration, selecting the discriminative features for PD analysis and ensuring the generalization performance of the ensemble model. By combining with data fusion scheme, the CERNNE is applied to forming a multi-task analysis framework, recognizing PD patients and predicting PD-associated brain regions and genes. In the multimodal data experiment, the proposed framework shows better classification performance and pathogenic factors predicting ability, which provides a new perspective for the diagnosis of PD.
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Affiliation(s)
- Xia-An Bi
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081, China; College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.
| | - Xi Hu
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081, China; College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
| | - Yiming Xie
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081, China; College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
| | - Hao Wu
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081, China; College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
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Galdi P, Fratello M, Trojsi F, Russo A, Tedeschi G, Tagliaferri R, Esposito F. Stochastic Rank Aggregation for the Identification of Functional Neuromarkers. Neuroinformatics 2019; 17:479-496. [PMID: 30604083 DOI: 10.1007/s12021-018-9412-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The main challenge in analysing functional magnetic resonance imaging (fMRI) data from extended samples of subject (N > 100) is to extract as much relevant information as possible from big amounts of noisy data. When studying neurodegenerative diseases with resting-state fMRI, one of the objectives is to determine regions with abnormal background activity with respect to a healthy brain and this is often attained with comparative statistical models applied to single voxels or brain parcels within one or several functional networks. In this work, we propose a novel approach based on clustering and stochastic rank aggregation to identify parcels that exhibit a coherent behaviour in groups of subjects affected by the same disorder and apply it to default-mode network independent component maps from resting-state fMRI data sets. Brain voxels are partitioned into parcels through k-means clustering, then solutions are enhanced by means of consensus techniques. For each subject, clusters are ranked according to their median value and a stochastic rank aggregation method, TopKLists, is applied to combine the individual rankings within each class of subjects. For comparison, the same approach was tested on an anatomical parcellation. We found parcels for which the rankings were different among control subjects and subjects affected by Parkinson's disease and amyotrophic lateral sclerosis and found evidence in literature for the relevance of top ranked regions in default-mode brain activity. The proposed framework represents a valid method for the identification of functional neuromarkers from resting-state fMRI data, and it might therefore constitute a step forward in the development of fully automated data-driven techniques to support early diagnoses of neurodegenerative diseases.
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Affiliation(s)
- Paola Galdi
- NeuRoNe Lab, Department of Management and Innovation Systems, University of Salerno, Fisciano, Salerno, Italy
| | - Michele Fratello
- Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Francesca Trojsi
- Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Antonio Russo
- Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Gioacchino Tedeschi
- Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Roberto Tagliaferri
- NeuRoNe Lab, Department of Management and Innovation Systems, University of Salerno, Fisciano, Salerno, Italy
| | - Fabrizio Esposito
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via S. Allende, 84081, Baronissi, Salerno, Italy.
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