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Machine Learning Approaches for Neurodegenerative Diseases Diagnosis

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 76259

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Prof. Dr. Markos G. Tsipouras

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Department of Electrical and Computer Engineering, University of Western Macedonia, GR50100 Kozani, Greece
Interests: biomedical signal processing; EEG signal processing; data mining; decision support and medical expert systems; data modelling; computational intelligence; image processing; biomedical engineering
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Dr. Alexandros Tzallas

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Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
Interests: neuroscience; EEG; biomedical engineering; decision support medical systems; biomedical applications
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Prof. Dr. Nikolaos Giannakeas

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School of Informatics and Telecommunications, Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, GR-47100 Arta, Greece
Interests: biomedical image and signal processing; EEG signal processing; brain computer interface systems; wearable devices; bioinformatics; machine learning; biomedical engineering
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Dr. Katerina D. Tzimourta

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Laboratory of Medical Physics, School of Medicine, University of Ioannina, Ioannina, Greece
Interests: EEG signal processing; brain computer interface; machine learning; EEG wearable devices
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Special Issue Information

Dear Colleagues,

Neurodegenerative diseases, such as Parkinson’s disease, Alzheimer’s disease and other types of dementia, Huntington’s disease, motor neuron disease and Prion disease, are progressive brain disorders with great prevalence in the general population. It is estimated that around 10 million people live with Parkinson’s disease worldwide (Marras C., et al. 2018), and a recent report of 2018 (Prince M., et al. 2015) recorded that 33 million people suffer from Alzheimer’s disease. These disorders afflict both the individual and the caregivers, and have a great impact on the global health economy (Xu J., et al. 2017).

Several diagnostic procedures are performed for differential diagnoses, including brain imaging, EEG analysis, molecular analysis, and cognitive, psychological and physical examination (Tzimourta K.D., et al. 2019). The research focuses on novel machine learning and artificial intelligence approaches, to elucidate the pathogenesis of neurodegenerative disorders and provide early diagnosis, aiming to develop effective treatments, improve the quality of the patient’s life and increase life expectancy (Myszczynska M.A., et al. 2020).

The current Special Issue addresses the urgent need for novel strategies and advanced machine learning methods, to provide the foundation for beyond state-of-the-art diagnostic procedures for neurodegenerative diseases.

Dr. Markos G. Tsipouras
Dr. Alexandros T. Tzallas
Prof. Dr. Nikolaos Giannakeas
Dr. Katerina D. Tzimourta
Guest Editors

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Keywords

neurodegenerative diseases
Alzheimer’s disease
Parkinson’s disease
dementia
EEG
neuroimaging
machine learning
deep learning
wearable devices
neurodegenerative diseases diagnosis
neurodegenerative diseases staging
neurodegenerative disease progression
biomarkers
quantitative EEG
mini-mental state examination

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Published Papers (11 papers)

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24 pages, 3753 KiB

Open AccessArticle

Parkinson’s Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques

by Majid Aljalal, Saeed A. Aldosari, Khalil AlSharabi, Akram M. Abdurraqeeb and Fahd A. Alturki

Diagnostics 2022, 12(5), 1033; https://doi.org/10.3390/diagnostics12051033 - 20 Apr 2022

Cited by 57 | Viewed by 7521

Abstract

Parkinson’s disease (PD) is a very common brain abnormality that affects people all over the world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent disease progression. Electroencephalography (EEG) is one of the most important PD diagnostic tools since this disease is linked to the brain. In this study, novel efficient common spatial pattern-based approaches for detecting Parkinson’s disease in two cases, off–medication and on–medication, are proposed. First, the EEG signals are preprocessed to remove major artifacts before spatial filtering using a common spatial pattern. Several features are extracted from spatially filtered signals using different metrics, namely, variance, band power, energy, and several types of entropy. Machine learning techniques, namely, random forest, linear/quadratic discriminant analysis, support vector machine, and k-nearest neighbor, are investigated to classify the extracted features. The impacts of frequency bands, segment length, and reduction number on the results are also investigated in this work. The proposed methods are tested using two EEG datasets: the SanDiego dataset (31 participants, 93 min) and the UNM dataset (54 participants, 54 min). The results show that the proposed methods, particularly the combination of common spatial patterns and log energy entropy, provide competitive results when compared to methods in the literature. The achieved results in terms of classification accuracy, sensitivity, and specificity in the case of off-medication PD detection are around 99%. In the case of on-medication PD, the results range from 95% to 98%. The results also reveal that features extracted from the alpha and beta bands have the highest classification accuracy. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)

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19 pages, 1506 KiB

Open AccessArticle

Hierarchical Boosting Dual-Stage Feature Reduction Ensemble Model for Parkinson’s Disease Speech Data

by Mingyao Yang, Jie Ma, Pin Wang, Zhiyong Huang, Yongming Li, He Liu and Zeeshan Hameed

Diagnostics 2021, 11(12), 2312; https://doi.org/10.3390/diagnostics11122312 - 9 Dec 2021

Cited by 9 | Viewed by 2777

Abstract

As a neurodegenerative disease, Parkinson’s disease (PD) is hard to identify at the early stage, while using speech data to build a machine learning diagnosis model has proved effective in its early diagnosis. However, speech data show high degrees of redundancy, repetition, and unnecessary noise, which influence the accuracy of diagnosis results. Although feature reduction (FR) could alleviate this issue, the traditional FR is one-sided (traditional feature extraction could construct high-quality features without feature preference, while traditional feature selection could achieve feature preference but could not construct high-quality features). To address this issue, the Hierarchical Boosting Dual-Stage Feature Reduction Ensemble Model (HBD-SFREM) is proposed in this paper. The major contributions of HBD-SFREM are as follows: (1) The instance space of the deep hierarchy is built by an iterative deep extraction mechanism. (2) The manifold features extraction method embeds the nearest neighbor feature preference method to form the dual-stage feature reduction pair. (3) The dual-stage feature reduction pair is iteratively performed by the AdaBoost mechanism to obtain instances features with higher quality, thus achieving a substantial improvement in model recognition accuracy. (4) The deep hierarchy instance space is integrated into the original instance space to improve the generalization of the algorithm. Three PD speech datasets and a self-collected dataset are used to test HBD-SFREM in this paper. Compared with other FR algorithms and deep learning algorithms, the accuracy of HBD-SFREM in PD speech recognition is improved significantly and would not be affected by a small sample dataset. Thus, HBD-SFREM could give a reference for other related studies. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)

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14 pages, 2186 KiB

Open AccessArticle

The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images

by Yu-Ching Ni, Fan-Pin Tseng, Ming-Chyi Pai, Ing-Tsung Hsiao, Kun-Ju Lin, Zhi-Kun Lin, Chia-Yu Lin, Pai-Yi Chiu, Guang-Uei Hung, Chiung-Chih Chang, Ya-Ting Chang, Keh-Shih Chuang and Alzheimer’s Disease Neuroimaging Initiative

Diagnostics 2021, 11(11), 2091; https://doi.org/10.3390/diagnostics11112091 - 12 Nov 2021

Cited by 7 | Viewed by 2996

Abstract

The correct differential diagnosis of dementia has an important impact on patient treatment and follow-up care strategies. Tc-99m-ECD SPECT imaging, which is low cost and accessible in general clinics, is used to identify the two common types of dementia, Alzheimer’s disease (AD) and Lewy body dementia (LBD). Two-stage transfer learning technology and reducing model complexity based on the ResNet-50 model were performed using the ImageNet data set and ADNI database. To improve training accuracy, the three-dimensional image was reorganized into three sets of two-dimensional images for data augmentation and ensemble learning, then the performance of various deep learning models for Tc-99m-ECD SPECT images to distinguish AD/normal cognition (NC), LBD/NC, and AD/LBD were investigated. In the AD/NC, LBD/NC, and AD/LBD tasks, the AUC values were around 0.94, 0.95, and 0.74, regardless of training models, with an accuracy of 90%, 87%, and 71%, and F1 scores of 89%, 86%, and 76% in the best cases. The use of transfer learning and a modified model resulted in better prediction results, increasing the accuracy by 32% for AD/NC. The proposed method is practical and could rapidly utilize a deep learning model to automatically extract image features based on a small number of SPECT brain perfusion images in general clinics to objectively distinguish AD and LBD. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)

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13 pages, 1526 KiB

Open AccessArticle

Assessing Parkinson’s Disease at Scale Using Telephone-Recorded Speech: Insights from the Parkinson’s Voice Initiative

by Siddharth Arora and Athanasios Tsanas

Diagnostics 2021, 11(10), 1892; https://doi.org/10.3390/diagnostics11101892 - 14 Oct 2021

Cited by 26 | Viewed by 4991

Abstract

Numerous studies have reported on the high accuracy of using voice tasks for the remote detection and monitoring of Parkinson’s Disease (PD). Most of these studies, however, report findings on a small number of voice recordings, often collected under acoustically controlled conditions, and therefore cannot scale at large without specialized equipment. In this study, we aimed to evaluate the potential of using voice as a population-based PD screening tool in resource-constrained settings. Using the standard telephone network, we processed 11,942 sustained vowel /a/ phonations from a US-English cohort comprising 1078 PD and 5453 control participants. We characterized each phonation using 304 dysphonia measures to quantify a range of vocal impairments. Given that this is a highly unbalanced problem, we used the following strategy: we selected a balanced subset (n = 3000 samples) for training and testing using 10-fold cross-validation (CV), and the remaining (unbalanced held-out dataset, n = 8942) samples for further model validation. Using robust feature selection methods we selected 27 dysphonia measures to present into a radial-basis-function support vector machine and demonstrated differentiation of PD participants from controls with 67.43% sensitivity and 67.25% specificity. These findings could help pave the way forward toward the development of an inexpensive, remote, and reliable diagnostic support tool for PD using voice as a digital biomarker. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)

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12 pages, 467 KiB

Open AccessArticle

Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods

by Andreas Miltiadous, Katerina D. Tzimourta, Nikolaos Giannakeas, Markos G. Tsipouras, Theodora Afrantou, Panagiotis Ioannidis and Alexandros T. Tzallas

Diagnostics 2021, 11(8), 1437; https://doi.org/10.3390/diagnostics11081437 - 9 Aug 2021

Cited by 91 | Viewed by 9574

Abstract

Dementia is the clinical syndrome characterized by progressive loss of cognitive and emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer’s disease (AD) is the most common neurogenerative disorder, making up 50–70% of total dementia cases. Another dementia type is frontotemporal dementia (FTD), which is associated with circumscribed degeneration of the prefrontal and anterior temporal cortex and mainly affects personality and social skills. With the rapid advancement in electroencephalogram (EEG) sensors, the EEG has become a suitable, accurate, and highly sensitive biomarker for the identification of neuronal and cognitive dynamics in most cases of dementia, such as AD and FTD, through EEG signal analysis and processing techniques. In this study, six supervised machine-learning techniques were compared on categorizing processed EEG signals of AD and FTD cases, to provide an insight for future methods on early dementia diagnosis. K-fold cross validation and leave-one-patient-out cross validation were also compared as validation methods to evaluate their performance for this classification problem. The proposed methodology accuracy scores were 78.5% for AD detection with decision trees and 86.3% for FTD detection with random forests. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)

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19 pages, 2596 KiB

Open AccessArticle

Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson’s Disease Based on Gait Signals

by S. Jeba Priya, Arockia Jansi Rani, M. S. P. Subathra, Mazin Abed Mohammed, Robertas Damaševičius and Neha Ubendran

Diagnostics 2021, 11(8), 1395; https://doi.org/10.3390/diagnostics11081395 - 1 Aug 2021

Cited by 55 | Viewed by 3875

Abstract

Parkinson’s disease (PD) is a neuro-degenerative disorder primarily triggered due to the deterioration of dopamine-producing neurons in the substantia nigra of the human brain. The early detection of Parkinson’s disease can assist in preventing deteriorating health. This paper analyzes human gait signals using Local Binary Pattern (LBP) techniques during feature extraction before classification. Supplementary to the LBP techniques, Local Gradient Pattern (LGP), Local Neighbour Descriptive Pattern (LNDP), and Local Neighbour Gradient Pattern (LNGP) were utilized to extract features from gait signals. The statistical features were derived and analyzed, and the statistical Kruskal–Wallis test was carried out for the selection of an optimal feature set. The classification was then carried out by an Artificial Neural Network (ANN) for the identified feature set. The proposed Symmetrically Weighted Local Neighbour Gradient Pattern (SWLNGP) method achieves a better performance, with 96.28% accuracy, 96.57% sensitivity, and 95.94% specificity. This study suggests that SWLNGP could be an effective feature extraction technique for the recognition of Parkinsonian gait. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)

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16 pages, 1275 KiB

Open AccessArticle

Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network

by Modupe Odusami, Rytis Maskeliūnas, Robertas Damaševičius and Tomas Krilavičius

Diagnostics 2021, 11(6), 1071; https://doi.org/10.3390/diagnostics11061071 - 10 Jun 2021

Cited by 194 | Viewed by 26370

Abstract

One of the first signs of Alzheimer’s disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)

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15 pages, 1162 KiB

Open AccessArticle

A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer’s Disease

by Jorge I. Vélez, Luiggi A. Samper, Mauricio Arcos-Holzinger, Lady G. Espinosa, Mario A. Isaza-Ruget, Francisco Lopera and Mauricio Arcos-Burgos

Diagnostics 2021, 11(5), 887; https://doi.org/10.3390/diagnostics11050887 - 17 May 2021

Cited by 5 | Viewed by 4062

Abstract

Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer’s disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R²), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)

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17 pages, 936 KiB

Open AccessArticle

Ethical Implications of Alzheimer’s Disease Prediction in Asymptomatic Individuals through Artificial Intelligence

by Frank Ursin, Cristian Timmermann and Florian Steger

Diagnostics 2021, 11(3), 440; https://doi.org/10.3390/diagnostics11030440 - 4 Mar 2021

Cited by 16 | Viewed by 5573

Abstract

Biomarker-based predictive tests for subjectively asymptomatic Alzheimer’s disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to the new ethical challenges that AI brings to the early diagnosis in asymptomatic individuals, beyond contributing to research purposes, when we still lack adequate treatment. The aim of this paper is to explore the ethical arguments put forward for AI aided AD prediction in subjectively asymptomatic individuals and their ethical implications. The ethical assessment is based on a systematic literature search. Thematic analysis was conducted inductively of 18 included publications. The ethical framework includes the principles of autonomy, beneficence, non-maleficence, and justice. Reasons for offering predictive tests to asymptomatic individuals are the right to know, a positive balance of the risk-benefit assessment, and the opportunity for future planning. Reasons against are the lack of disease modifying treatment, the accuracy and explicability of AI aided prediction, the right not to know, and threats to social rights. We conclude that there are serious ethical concerns in offering early diagnosis to asymptomatic individuals and the issues raised by the application of AI add to the already known issues. Nevertheless, pre-symptomatic testing should only be offered on request to avoid inflicted harm. We recommend developing training for physicians in communicating AI aided prediction. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)

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2 pages, 550 KiB

Open AccessReply

Reply to Nicholas et al. Using a ResNet-18 Network to Detect Features of Alzheimer’s Disease on Functional Magnetic Resonance Imaging: A Failed Replication. Comment on “Odusami et al. Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network. Diagnostics 2021, 11, 1071”

by Modupe Odusami, Rytis Maskeliūnas, Robertas Damaševičius and Tomas Krilavičius

Diagnostics 2022, 12(5), 1097; https://doi.org/10.3390/diagnostics12051097 - 27 Apr 2022

Cited by 1 | Viewed by 1632

Abstract

We have studied the manuscript of Nicholas et al. [...] Full article

(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)

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4 pages, 197 KiB

Open AccessComment

Using a ResNet-18 Network to Detect Features of Alzheimer’s Disease on Functional Magnetic Resonance Imaging: A Failed Replication. Comment on Odusami et al. Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network. Diagnostics 2021, 11, 1071

by Peter J. Nicholas, Alex To, Onur Tanglay, Isabella M. Young, Michael E. Sughrue and Stéphane Doyen

Diagnostics 2022, 12(5), 1094; https://doi.org/10.3390/diagnostics12051094 - 27 Apr 2022

Cited by 11 | Viewed by 2216

Abstract

There is considerable interest in developing effective tools to detect Alzheimer’s Disease (AD) early in its course, prior to clinical progression [...] Full article

(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)

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