1
|
Chen Z, Liu Y, Zhang Y, Li Q. Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer's disease diagnosis. Med Image Anal 2023; 84:102698. [PMID: 36462372 DOI: 10.1016/j.media.2022.102698] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 10/18/2022] [Accepted: 11/17/2022] [Indexed: 11/23/2022]
Abstract
Recent studies have shown that multimodal neuroimaging data provide complementary information of the brain and latent space-based methods have achieved promising results in fusing multimodal data for Alzheimer's disease (AD) diagnosis. However, most existing methods treat all features equally and adopt nonorthogonal projections to learn the latent space, which cannot retain enough discriminative information in the latent space. Besides, they usually preserve the relationships among subjects in the latent space based on the similarity graph constructed on original features for performance boosting. However, the noises and redundant features significantly corrupt the graph. To address these limitations, we propose an Orthogonal Latent space learning with Feature weighting and Graph learning (OLFG) model for multimodal AD diagnosis. Specifically, we map multiple modalities into a common latent space by orthogonal constrained projection to capture the discriminative information for AD diagnosis. Then, a feature weighting matrix is utilized to sort the importance of features in AD diagnosis adaptively. Besides, we devise a regularization term with learned graph to preserve the local structure of the data in the latent space and integrate the graph construction into the learning processing for accurately encoding the relationships among samples. Instead of constructing a similarity graph for each modality, we learn a joint graph for multiple modalities to capture the correlations among modalities. Finally, the representations in the latent space are projected into the target space to perform AD diagnosis. An alternating optimization algorithm with proved convergence is developed to solve the optimization objective. Extensive experimental results show the effectiveness of the proposed method.
Collapse
Affiliation(s)
- Zhi Chen
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yongguo Liu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Yun Zhang
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qiaoqin Li
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| |
Collapse
|
2
|
Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103293] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
3
|
Ahmadi-Dastgerdi N, Hosseini-Nejad H, Amiri H, Shoeibi A, Gorriz JM. A Vector Quantization-Based Spike Compression Approach Dedicated to Multichannel Neural Recording Microsystems. Int J Neural Syst 2021; 32:2250001. [PMID: 34931938 DOI: 10.1142/s0129065722500010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Implantable high-density multichannel neural recording microsystems provide simultaneous recording of brain activities. Wireless transmission of the entire recorded data causes high bandwidth usage, which is not tolerable for implantable applications. As a result, a hardware-friendly compression module is required to reduce the amount of data before it is transmitted. This paper presents a novel compression approach that utilizes a spike extractor and a vector quantization (VQ)-based spike compressor. In this approach, extracted spikes are vector quantized using an unsupervised learning process providing a high spike compression ratio (CR) of 10-80. A combination of extracting and compressing neural spikes results in a significant data reduction as well as preserving the spike waveshapes. The compression performance of the proposed approach was evaluated under variant conditions. We also developed new architectures such that the hardware blocks of our approach can be implemented more efficiently. The compression module was implemented in a 180-nm standard CMOS process achieving a SNDR of 14.49[Formula: see text]dB and a classification accuracy (CA) of 99.62% at a CR of 20, while consuming 4[Formula: see text][Formula: see text]W power and 0.16[Formula: see text]mm2 chip area per channel.
Collapse
Affiliation(s)
| | | | - Hadi Amiri
- School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran
| | - Afshin Shoeibi
- Faculty of Electrical Engineering, FPGA Research Lab K. N. Toosi, University of Technology, Tehran, Iran
| | - Juan Manuel Gorriz
- Department of Signal Processing Networking and Communications, University of Granada, Granada, Spain.,Department of Psychiatry, University of Cambridge, Cambridge, UK
| |
Collapse
|
4
|
Martinez-Murcia FJ, Ortiz A, Ramírez J, Górriz JM, Cruz R. Deep residual transfer learning for automatic diagnosis and grading of diabetic retinopathy. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.04.148] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
|
5
|
Ramírez J, Górriz JM, Ortiz A, Cole JH, Dyrba M. Editorial: Deep Learning in Aging Neuroscience. Front Neuroinform 2020; 14:573974. [PMID: 33209104 PMCID: PMC7649163 DOI: 10.3389/fninf.2020.573974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 09/16/2020] [Indexed: 11/30/2022] Open
Affiliation(s)
- Javier Ramírez
- Department Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Juan M Górriz
- Department Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Andrés Ortiz
- Department of Communications Engineering, University of Málaga, Málaga, Spain
| | - James H Cole
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom.,Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| |
Collapse
|
6
|
Alterations of Brain Networks in Alzheimer's Disease and Mild Cognitive Impairment: A Resting State fMRI Study Based on a Population-specific Brain Template. Neuroscience 2020; 452:192-207. [PMID: 33197505 DOI: 10.1016/j.neuroscience.2020.10.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 10/18/2020] [Accepted: 10/21/2020] [Indexed: 12/11/2022]
Abstract
This study aimed to investigate the alterations in brain networks in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) based on a population-specific brain template. Previous studies on AD brain networks using graph theory rarely adopted brain templates specific for certain ethnicities. In this study, patients were divided into 3 groups: AD (n = 24), MCI (n = 27), and healthy controls (HCs, n = 33), and all of the subjects are Chinese. Functional brain networks were constructed for each group based on a Chinese brain template using resting-state functional magnetic resonance imaging (rs-fMRI) data; several graph metrics were calculated. Graph metrics with significant differences after false discovery rate (FDR) correction were analyzed with respect to correlations with four neuropsychological test scores: Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Activities of Daily Living (ADL), and Clinical Dementia Rating (CDR), which assessed the subjects' cognitive functions and ability to engage in ADL. Graph metrics including assortativity coefficient, nodal degree centrality, nodal clustering coefficient, nodal efficiency, and nodal local efficiency of the frontal gyrus and cerebellum were significantly altered in AD and MCI compared with HC. Several graph metrics were significantly correlated with cognitive function and the ability to engage in daily activities. The findings suggest that altered graph metrics in the frontal gyrus may reflect brain plasticity, and that patients with MCI may have unique graph metric alterations in the cerebellum. Future graph analysis studies on functional brain networks in AD and MCI based on population-specific brain atlases for particular ethnicities may prove valuable.
Collapse
|
7
|
Ruiz E, Ramírez J, Górriz JM, Casillas J. Alzheimer's Disease Computer-Aided Diagnosis: Histogram-Based Analysis of Regional MRI Volumes for Feature Selection and Classification. J Alzheimers Dis 2019; 65:819-842. [PMID: 29966190 DOI: 10.3233/jad-170514] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
This paper proposes a novel fully automatic computer-aided diagnosis (CAD) system for the early detection of Alzheimer's disease (AD) based on supervised machine learning methods. The novelty of the approach, which is based on histogram analysis, is twofold: 1) a feature extraction process that aims to detect differences in brain regions of interest (ROIs) relevant for the recognition of subjects with AD and 2) an original greedy algorithm that predicts the severity of the effects of AD on these regions. This algorithm takes account of the progressive nature of AD that affects the brain structure with different levels of severity, i.e., the loss of gray matter in AD is found first in memory-related areas of the brain such as the hippocampus. Moreover, the proposed feature extraction process generates a reduced set of attributes which allows the use of general-purpose classification machine learning algorithms. In particular, the proposed feature extraction approach assesses the ROI image separability between classes in order to identify the ones with greater discriminant power. These regions will have the highest influence in the classification decision at the final stage. Several experiments were carried out on segmented magnetic resonance images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in order to show the benefits of the overall method. The proposed CAD system achieved competitive classification results in a highly efficient and straightforward way.
Collapse
|
8
|
A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals. J Neurosci Methods 2019; 322:88-95. [DOI: 10.1016/j.jneumeth.2019.04.013] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/27/2019] [Accepted: 04/27/2019] [Indexed: 11/20/2022]
|
9
|
Grollemund V, Pradat PF, Querin G, Delbot F, Le Chat G, Pradat-Peyre JF, Bede P. Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions. Front Neurosci 2019; 13:135. [PMID: 30872992 PMCID: PMC6403867 DOI: 10.3389/fnins.2019.00135] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 02/06/2019] [Indexed: 12/23/2022] Open
Abstract
Background: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic factors. Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers. Disease heterogeneity, late-stage recruitment into pharmaceutical trials, and inclusion of phenotypically admixed patient cohorts are some of the key barriers to successful clinical trials. Machine Learning (ML) models and large international data sets offer unprecedented opportunities to appraise candidate diagnostic, monitoring, and prognostic markers. Accurate patient stratification into well-defined prognostic categories is another aspiration of emerging classification and staging systems. Methods: The objective of this paper is the comprehensive, systematic, and critical review of ML initiatives in ALS to date and their potential in research, clinical, and pharmacological applications. The focus of this review is to provide a dual, clinical-mathematical perspective on recent advances and future directions of the field. Another objective of the paper is the frank discussion of the pitfalls and drawbacks of specific models, highlighting the shortcomings of existing studies and to provide methodological recommendations for future study designs. Results: Despite considerable sample size limitations, ML techniques have already been successfully applied to ALS data sets and a number of promising diagnosis models have been proposed. Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. These models also offer patient stratification opportunities for future clinical trials. Despite the enormous potential of ML in ALS research, statistical assumptions are often violated, the choice of specific statistical models is seldom justified, and the constraints of ML models are rarely enunciated. Conclusions: From a mathematical perspective, the main barrier to the development of validated diagnostic, prognostic, and monitoring indicators stem from limited sample sizes. The combination of multiple clinical, biofluid, and imaging biomarkers is likely to increase the accuracy of mathematical modeling and contribute to optimized clinical trial designs.
Collapse
Affiliation(s)
- Vincent Grollemund
- Laboratoire d'Informatique de Paris 6, Sorbonne University, Paris, France
- FRS Consulting, Paris, France
| | - Pierre-François Pradat
- Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Sorbonne Université, Paris, France
- APHP, Département de Neurologie, Hôpital Pitié-Salpêtrière, Centre Référent SLA, Paris, France
- Northern Ireland Center for Stratified Medecine, Biomedical Sciences Research Institute Ulster University, C-TRIC, Altnagelvin Hospital, Londonderry, United Kingdom
| | - Giorgia Querin
- Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Sorbonne Université, Paris, France
- APHP, Département de Neurologie, Hôpital Pitié-Salpêtrière, Centre Référent SLA, Paris, France
| | - François Delbot
- Laboratoire d'Informatique de Paris 6, Sorbonne University, Paris, France
- Département de Mathématiques et Informatique, Paris Nanterre University, Nanterre, France
| | | | - Jean-François Pradat-Peyre
- Laboratoire d'Informatique de Paris 6, Sorbonne University, Paris, France
- Département de Mathématiques et Informatique, Paris Nanterre University, Nanterre, France
- Modal'X, Paris Nanterre University, Nanterre, France
| | - Peter Bede
- Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Sorbonne Université, Paris, France
- APHP, Département de Neurologie, Hôpital Pitié-Salpêtrière, Centre Référent SLA, Paris, France
- Computational Neuroimaging Group, Trinity College, Dublin, Ireland
| |
Collapse
|
10
|
Collazos-Huertas D, Cárdenas-Peña D, Castellanos-Dominguez G. Instance-Based Representation Using Multiple Kernel Learning for Predicting Conversion to Alzheimer Disease. Int J Neural Syst 2019; 29:1850042. [DOI: 10.1142/s0129065718500429] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The early detection of Alzheimer’s disease and quantification of its progression poses multiple difficulties for machine learning algorithms. Two of the most relevant issues are related to missing data and results interpretability. To deal with both issues, we introduce a methodology to predict conversion of mild cognitive impairment patients to Alzheimer’s from structural brain MRI volumes. First, we use morphological measures of each brain structure to build an instance-based feature mapping that copes with missed follow-up visits. Then, the extracted multiple feature mappings are combined into a single representation through the convex combination of reproducing kernels. The weighting parameters per structure are tuned based on the maximization of the centered-kernel alignment criterion. We evaluate the proposed methodology on a couple of well-known classification machines employing the ADNI database devoted to assessing the combined prognostic value of several AD biomarkers. The obtained experimental results show that our proposed method of Instance-based representation using multiple kernel learning enables detecting mild cognitive impairment as well as predicting conversion to Alzheimers disease within three years from the initial screening. Besides, the brain structures with larger combination weights are directly related to memory and cognitive functions.
Collapse
Affiliation(s)
- D. Collazos-Huertas
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Km 9 Vía al Aeropuerto la Nubia, Manizales, Colombia
| | - D. Cárdenas-Peña
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Km 9 Vía al Aeropuerto la Nubia, Manizales, Colombia
| | - G. Castellanos-Dominguez
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Km 9 Vía al Aeropuerto la Nubia, Manizales, Colombia
| |
Collapse
|
11
|
Ortiz A, Munilla J, Martínez-Murcia FJ, Górriz JM, Ramírez J. Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling. Int J Neural Syst 2019; 29:1850040. [DOI: 10.1142/s0129065718500405] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.
Collapse
Affiliation(s)
- Andrés Ortiz
- Communications Engineering Department, University of Málaga, Málaga 29071, Spain
| | - Jorge Munilla
- Communications Engineering Department, University of Málaga, Málaga 29071, Spain
| | | | - Juan M. Górriz
- Department of Signal Theory, Communications and Networking, University of Granada, Granada 18060, Spain
| | - Javier Ramírez
- Department of Signal Theory, Communications and Networking, University of Granada, Granada 18060, Spain
| |
Collapse
|
12
|
Valenzuela O, Jiang X, Carrillo A, Rojas I. Multi-Objective Genetic Algorithms to Find Most Relevant Volumes of the Brain Related to Alzheimer's Disease and Mild Cognitive Impairment. Int J Neural Syst 2018; 28:1850022. [DOI: 10.1142/s0129065718500223] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Computer-Aided Diagnosis (CAD) represents a relevant instrument to automatically classify between patients with and without Alzheimer's Disease (AD) using several actual imaging techniques. This study analyzes the optimization of volumes of interest (VOIs) to extract three-dimensional (3D) textures from Magnetic Resonance Image (MRI) in order to diagnose AD, Mild Cognitive Impairment converter (MCIc), Mild Cognitive Impairment nonconverter (MCInc) and Normal subjects. A relevant feature of the proposed approach is the use of 3D features instead of traditional two-dimensional (2D) features, by using 3D discrete wavelet transform (3D-DWT) approach for performing feature extraction from T-1 weighted MRI. Due to the high number of coefficients when applying 3D-DWT to each of the VOIs, a feature selection algorithm based on mutual information is used, as is the minimum Redundancy Maximum Relevance (mRMR) algorithm. Region optimization has been performed in order to discover the most relevant regions (VOIs) in the brain with the use of Multi-Objective Genetic Algorithms, being one of the objectives to be optimize the accuracy of the system. The error index of the system is computed by the confusion matrix obtained by the multi-class support vector machine (SVM) classifier. Principal Component Analysis (PCA) is used with the purpose of reducing the number of features to the classifier. The cohort of subjects used in the study consisted of 296 different patients. A first group of 206 patients was used to optimize VOI selection and another group of 90 independent subjects (that did not belong to the first group) was used to test the solutions yielded by the genetic algorithm. The proposed methodology obtains excellent results in multi-class classification achieving accuracies of 94.4% and also extracting significant information on the location of the most relevant points of the brain. This suggests that the proposed method could aid in the research of other neurodegenerative diseases, improving the accuracy of the diagnosis and finding the most relevant regions of the brain associated with them.
Collapse
Affiliation(s)
- Olga Valenzuela
- Department of Applied Mathematics, University of Granada, Spain
| | - Xiaoyi Jiang
- Department of Computer Science, University of Munster, Germany
| | - Antonio Carrillo
- Department of Computer Architecture and Computer Technology, University of Granada, Spain
| | - Ignacio Rojas
- Department of Computer Architecture and Computer Technology, CITIC-UGR, University of Granada, Spain
| |
Collapse
|
13
|
Bhat S, Acharya UR, Hagiwara Y, Dadmehr N, Adeli H. Parkinson's disease: Cause factors, measurable indicators, and early diagnosis. Comput Biol Med 2018; 102:234-241. [PMID: 30253869 DOI: 10.1016/j.compbiomed.2018.09.008] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 09/12/2018] [Accepted: 09/12/2018] [Indexed: 12/17/2022]
Abstract
Parkinson's disease (PD) is a neurodegenerative disease of the central nervous system caused due to the loss of dopaminergic neurons. It is classified under movement disorder as patients with PD present with tremor, rigidity, postural changes, and a decrease in spontaneous movements. Comorbidities including anxiety, depression, fatigue, and sleep disorders are observed prior to the diagnosis of PD. Gene mutations, exposure to toxic substances, and aging are considered as the causative factors of PD even though its genesis is unknown. This paper reviews PD etiologies, progression, and in particular measurable indicators of PD such as neuroimaging and electrophysiology modalities. In addition to gene therapy, neuroprotective, pharmacological, and neural transplantation treatments, researchers are actively aiming at identifying biological markers of PD with the goal of early diagnosis. Neuroimaging modalities used together with advanced machine learning techniques offer a promising path for the early detection and intervention in PD patients.
Collapse
Affiliation(s)
- Shreya Bhat
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal, 576104, India
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, 599491, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Malaysia.
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Nahid Dadmehr
- Board-certified Neurologist, Columbus, OH, United States
| | - Hojjat Adeli
- Departments of Biomedical Informatics, Neurology, and Neuroscience, The Ohio State University, United States
| |
Collapse
|
14
|
Fang C, Li C, Cabrerizo M, Barreto A, Andrian J, Rishe N, Loewenstein D, Duara R, Adjouadi M. Gaussian Discriminant Analysis for Optimal Delineation of Mild Cognitive Impairment in Alzheimer’s Disease. Int J Neural Syst 2018; 28:1850017. [DOI: 10.1142/s012906571850017x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer’s disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group. A novel Gaussian discriminant analysis-based algorithm is thus introduced to achieve a more effective and accurate classification performance than the aforementioned state-of-the-art algorithms. This study makes use of magnetic resonance imaging (MRI) data uniquely as input to two separate high-dimensional decision spaces that reflect the structural measures of the two brain hemispheres. The data used include 190 CN, 305 MCI and 133 AD subjects as part of the AD Big Data DREAM Challenge #1. Using 80% data for a 10-fold cross-validation, the proposed algorithm achieved an average F1 score of 95.89% and an accuracy of 96.54% for discriminating AD from CN; and more importantly, an average F1 score of 92.08% and an accuracy of 90.26% for discriminating MCI from CN. Then, a true test was implemented on the remaining 20% held-out test data. For discriminating MCI from CN, an accuracy of 80.61%, a sensitivity of 81.97% and a specificity of 78.38% were obtained. These results show significant improvement over existing algorithms for discriminating the subtle differences between MCI participants and the CN group.
Collapse
Affiliation(s)
- Chen Fang
- Center for Advanced Technology and Education (CATE), Florida International University, 10555 W Flagler St., Miami, Florida 33174, USA
| | - Chunfei Li
- Center for Advanced Technology and Education (CATE), Florida International University, 10555 W Flagler St., Miami, Florida 33174, USA
| | - Mercedes Cabrerizo
- Center for Advanced Technology and Education (CATE), Florida International University, 10555 W Flagler St., Miami, Florida 33174, USA
| | - Armando Barreto
- Center for Advanced Technology and Education (CATE), Florida International University, 10555 W Flagler St., Miami, Florida 33174, USA
| | - Jean Andrian
- Center for Advanced Technology and Education (CATE), Florida International University, 10555 W Flagler St., Miami, Florida 33174, USA
| | - Naphtali Rishe
- Center for Advanced Technology and Education (CATE), Florida International University, 10555 W Flagler St., Miami, Florida 33174, USA
| | - David Loewenstein
- Wien Center for Alzheimer’s Disease & Memory Disorders, Mount Sinai Medical Center Miami Beach, Florida 33140, USA
- Department of Psychiatry & Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, Florida 33136, USA
- Florida Alzheimer’s Disease Research Center (ADRC), University of Florida, Gainesville, Florida 32610, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease & Memory Disorders, Mount Sinai Medical Center Miami Beach, Florida 33140, USA
- Florida Alzheimer’s Disease Research Center (ADRC), University of Florida, Gainesville, Florida 32610, USA
- Herbert Wertheim College of Medicine, Florida International University, Miami, Florida 33174, USA
| | - Malek Adjouadi
- Center for Advanced Technology and Education (CATE), Florida International University, 10555 W Flagler St., Miami, Florida 33174, USA
- Florida Alzheimer’s Disease Research Center (ADRC), University of Florida, Gainesville, Florida 32610, USA
- Herbert Wertheim College of Medicine, Florida International University, Miami, Florida 33174, USA
| |
Collapse
|
15
|
Amezquita-Sanchez JP, Valtierra-Rodriguez M, Adeli H, Perez-Ramirez CA. A Novel Wavelet Transform-Homogeneity Model for Sudden Cardiac Death Prediction Using ECG Signals. J Med Syst 2018; 42:176. [PMID: 30117048 DOI: 10.1007/s10916-018-1031-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Accepted: 08/07/2018] [Indexed: 02/01/2023]
Abstract
Sudden cardiac death (SCD) is one of the main causes of death among people. A new methodology is presented for predicting the SCD based on ECG signals employing the wavelet packet transform (WPT), a signal processing technique, homogeneity index (HI), a nonlinear measurement for time series signals, and the Enhanced Probabilistic Neural Network classification algorithm. The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 20 SCD and 18 normal patients. The proposed methodology presents the following significant advantages: (1) compared with previous works, the proposed methodology achieves a higher accuracy using a single nonlinear feature, HI, thus requiring low computational resource for predicting an SCD onset in real-time, unlike other methodologies proposed in the literature where a large number of nonlinear features are used to predict an SCD event; (2) it is capable of predicting the risk of developing an SCD event up to 20 min prior to the onset with a high accuracy of 95.8%, superseding the prior 12 min prediction time reported recently, and (3) it uses the ECG signal directly without the need for transforming the signal to a heart rate variability signal, thus saving time in the processing.
Collapse
Affiliation(s)
- Juan P Amezquita-Sanchez
- Faculty of Engineering, Departments Biomedical and Electromechanical, ENAP-RG, Autonomous University of Queretaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, C. P, 76807, San Juan del Río, Qro., Mexico
| | - Martin Valtierra-Rodriguez
- Faculty of Engineering, Departments Biomedical and Electromechanical, ENAP-RG, Autonomous University of Queretaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, C. P, 76807, San Juan del Río, Qro., Mexico
| | - Hojjat Adeli
- Departments Biomedical Informatics, Neuroscience, and Neurology, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 43210, USA.
| | - Carlos A Perez-Ramirez
- Faculty of Engineering, Departments Biomedical and Electromechanical, ENAP-RG, Autonomous University of Queretaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, C. P, 76807, San Juan del Río, Qro., Mexico
| |
Collapse
|
16
|
Mammone N, Ieracitano C, Adeli H, Bramanti A, Morabito FC. Permutation Jaccard Distance-Based Hierarchical Clustering to Estimate EEG Network Density Modifications in MCI Subjects. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:5122-5135. [PMID: 29994428 DOI: 10.1109/tnnls.2018.2791644] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In this paper, a novel electroencephalographic (EEG)-based method is introduced for the quantification of brain-electrical connectivity changes over a longitudinal evaluation of mild cognitive impaired (MCI) subjects. In the proposed method, a dissimilarity matrix is constructed by estimating the coupling strength between every pair of EEG signals, Hierarchical clustering is then applied to group the related electrodes according to the dissimilarity estimated on pairs of EEG recordings. Subsequently, the connectivity density of the electrodes network is calculated. The technique was tested over two different coupling strength descriptors: wavelet coherence (WC) and permutation Jaccard distance (PJD), a novel metric of coupling strength between time series introduced in this paper. Twenty-five MCI patients were enrolled within a follow-up program that consisted of two successive evaluations, at time T0 and at time T1, three months later. At T1, four subjects were diagnosed to have converted to Alzheimer's Disease (AD). When applying the PJD-based method, the converted patients exhibited a significantly increased PJD (p < 0.05), i.e., a reduced overall coupling strength, specifically in delta and θ bands and in the overall range (0.5-32 Hz). In addition, in contrast to stable MCI patients, converted patients exhibited a network density reduction in every subband (delta, θ, alpha, and beta). When WC was used as coupling strength descriptor, the method resulted in a less sensitive and specific outcome. The proposed method, mixing nonlinear analysis to a machine learning approach, appears to provide an objective evaluation of the connectivity density modifications associated to the MCI-AD conversion, just processing noninvasive EEG signals.
Collapse
|
17
|
López-Sanz D, Garcés P, Álvarez B, Delgado-Losada ML, López-Higes R, Maestú F. Network Disruption in the Preclinical Stages of Alzheimer’s Disease: From Subjective Cognitive Decline to Mild Cognitive Impairment. Int J Neural Syst 2017; 27:1750041. [DOI: 10.1142/s0129065717500411] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Introduction: Subjective Cognitive Decline (SCD) is a largely unknown state thought to represent a preclinical stage of Alzheimer’s Disease (AD) previous to mild cognitive impairment (MCI). However, the course of network disruption in these stages is scarcely characterized. Methods: We employed resting state magnetoencephalography in the source space to calculate network smallworldness, clustering, modularity and transitivity. Nodal measures (clustering and node degree) as well as modular partitions were compared between groups. Results: The MCI group exhibited decreased smallworldness, clustering and transitivity and increased modularity in theta and beta bands. SCD showed similar but smaller changes in clustering and transitivity, while exhibiting alterations in the alpha band in opposite direction to those showed by MCI for modularity and transitivity. At the node level, MCI disrupted both clustering and nodal degree while SCD showed minor changes in the latter. Additionally, we observed an increase in modular partition variability in both SCD and MCI in theta and beta bands. Conclusion: SCD elders exhibit a significant network disruption, showing intermediate values between HC and MCI groups in multiple parameters. These results highlight the relevance of cognitive concerns in the clinical setting and suggest that network disorganization in AD could start in the preclinical stages before the onset of cognitive symptoms.
Collapse
Affiliation(s)
- David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid 28223, Spain
- Department of Basic Psychology II, Complutense University of Madrid 28223, Spain
| | - Pilar Garcés
- Laboratory of Cognitive Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid 28223, Spain
| | - Blanca Álvarez
- Memory Decline Prevention Center Madrid Salud, Ayuntamiento de Madrid 28006, Spain
| | | | - Ramón López-Higes
- Department of Basic Psychology II, Complutense University of Madrid 28223, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid 28223, Spain
- Department of Basic Psychology II, Complutense University of Madrid 28223, Spain
| |
Collapse
|
18
|
|