1
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Mishra S, Jena L, Mishra N, Chang HT. PD-DETECTOR: A sustainable and computationally intelligent mobile application model for Parkinson's disease severity assessment. Heliyon 2024; 10:e34593. [PMID: 39130458 PMCID: PMC11315181 DOI: 10.1016/j.heliyon.2024.e34593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/26/2024] [Accepted: 07/12/2024] [Indexed: 08/13/2024] Open
Abstract
This paper introduces a mobile cloud-based predictive model for assisting Parkinson's disease (PD) patients. PD, a chronic neurodegenerative disorder, impairs motor functions and daily tasks due to the degeneration of dopamine-producing neurons in the brain. The model utilizes smartphones to aid patients in collecting voice samples, which are then sent to a cloud service for storage and processing. A hybrid deep learning model, trained using the UCI Parkinson's Telemonitoring Voice dataset, analyzes this data to estimate the severity of PD symptoms. The model's performance is noteworthy, with accuracy, sensitivity, and specificity metrics of 96.2 %, 94.15 %, and 96.15 %, respectively. Additionally, it boasts a rapid response time of just 13 s. Results are delivered to users via smartphone alert notifications, coupled with a knowledge base feature that educates them about PD. This system provides reliable home-based assessment and monitoring of PD and enables prompt medical intervention, significantly enhancing the quality of life for patients with Parkinson's disease.
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Affiliation(s)
- Sushruta Mishra
- School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, India
| | - Lambodar Jena
- Center for Data Science, Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be)University, Bhubaneswar, India
| | - Nilamadhab Mishra
- School of Computing Science and Engineering, VIT Bhopal University, Sehore, India
| | - Hsien-Tsung Chang
- Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, 333, Taiwan
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, 333, Taiwan
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Taoyuan, 333, Taiwan
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2
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Islam M, Hasan Majumder M, Hussein M, Hossain KM, Miah M. A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets. Heliyon 2024; 10:e25469. [PMID: 38356538 PMCID: PMC10865258 DOI: 10.1016/j.heliyon.2024.e25469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/30/2023] [Accepted: 01/27/2024] [Indexed: 02/16/2024] Open
Abstract
Parkinson's Disease (PD) is a prevalent neurodegenerative disorder with significant clinical implications. Early and accurate diagnosis of PD is crucial for timely intervention and personalized treatment. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as promis-ing tools for improving PD diagnosis. This review paper presents a detailed analysis of the current state of ML and DL-based PD diagnosis, focusing on voice, handwriting, and wave spiral datasets. The study also evaluates the effectiveness of various ML and DL algorithms, including classifiers, on these datasets and highlights their potential in enhancing diagnostic accuracy and aiding clinical decision-making. Additionally, the paper explores the identifi-cation of biomarkers using these techniques, offering insights into improving the diagnostic process. The discussion encompasses different data formats and commonly employed ML and DL methods in PD diagnosis, providing a comprehensive overview of the field. This review serves as a roadmap for future research, guiding the development of ML and DL-based tools for PD detection. It is expected to benefit both the scientific community and medical practitioners by advancing our understanding of PD diagnosis and ultimately improving patient outcomes.
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Affiliation(s)
- Md.Ariful Islam
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Nilkhet Rd, Dhaka, 1000, Bangladesh
| | - Md.Ziaul Hasan Majumder
- Institute of Electronics, Bangladesh Atomic Energy Commission, Dhaka, 1207, Bangladesh
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md.Alomgeer Hussein
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Khondoker Murad Hossain
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md.Sohel Miah
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
- Moulvibazar Polytechnic Institute, Bangladesh
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3
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Iyer A, Kemp A, Rahmatallah Y, Pillai L, Glover A, Prior F, Larson-Prior L, Virmani T. A machine learning method to process voice samples for identification of Parkinson's disease. Sci Rep 2023; 13:20615. [PMID: 37996478 PMCID: PMC10667335 DOI: 10.1038/s41598-023-47568-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023] Open
Abstract
Machine learning approaches have been used for the automatic detection of Parkinson's disease with voice recordings being the most used data type due to the simple and non-invasive nature of acquiring such data. Although voice recordings captured via telephone or mobile devices allow much easier and wider access for data collection, current conflicting performance results limit their clinical applicability. This study has two novel contributions. First, we show the reliability of personal telephone-collected voice recordings of the sustained vowel /a/ in natural settings by collecting samples from 50 people with specialist-diagnosed Parkinson's disease and 50 healthy controls and applying machine learning classification with voice features related to phonation. Second, we utilize a novel application of a pre-trained convolutional neural network (Inception V3) with transfer learning to analyze the spectrograms of the sustained vowel from these samples. This approach considers speech intensity estimates across time and frequency scales rather than collapsing measurements across time. We show the superiority of our deep learning model for the task of classifying people with Parkinson's disease as distinct from healthy controls.
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Affiliation(s)
- Anu Iyer
- Georgia Institute of Technology, Atlanta, 30332, USA
| | - Aaron Kemp
- Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, 72205, USA.
| | - Yasir Rahmatallah
- Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
| | - Lakshmi Pillai
- Neurology, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
| | - Aliyah Glover
- Neurology, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
| | - Fred Prior
- Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
| | - Linda Larson-Prior
- Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
- Neurology, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
- Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
| | - Tuhin Virmani
- Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
- Neurology, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
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4
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Dhanalakshmi S, Maanasaa RS, Maalikaa RS, Senthil R. A review of emergent intelligent systems for the detection of Parkinson's disease. Biomed Eng Lett 2023; 13:591-612. [PMID: 37872986 PMCID: PMC10590348 DOI: 10.1007/s13534-023-00319-2] [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: 05/25/2023] [Revised: 08/11/2023] [Accepted: 09/07/2023] [Indexed: 10/25/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder affecting people worldwide. The PD symptoms are divided into motor and non-motor symptoms. Detection of PD is very crucial and essential. Such challenges can be overcome by applying artificial intelligence to diagnose PD. Many studies have also proposed the implementation of computer-aided diagnosis for the detection of PD. This systematic review comprehensively analyzed all appropriate algorithms for detecting and assessing PD based on the literature from 2012 to 2023 which are conducted as per PRISMA model. This review focused on motor symptoms, namely handwriting dynamics, voice impairments and gait, multimodal features, and brain observation using single photon emission computed tomography, magnetic resonance and electroencephalogram signals. The significant challenges are critically analyzed, and appropriate recommendations are provided. The critical discussion of this review article can be helpful in today's PD community in such a way that it allows clinicians to provide proper treatment and timely medication.
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Affiliation(s)
- Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India
| | - Ramesh Sai Maanasaa
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India
| | - Ramesh Sai Maalikaa
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India
| | - Ramalingam Senthil
- Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India
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5
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Doumari SA, Berahmand K, Ebadi MJ. Early and High-Accuracy Diagnosis of Parkinson's Disease: Outcomes of a New Model. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2023; 2023:1493676. [PMID: 37304324 PMCID: PMC10256450 DOI: 10.1155/2023/1493676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 03/02/2023] [Accepted: 03/06/2023] [Indexed: 06/13/2023]
Abstract
Parkinson's disease (PD) is one of the significant common neurological disorders of the current age that causes uncontrollable movements like shaking, stiffness, and difficulty. The early clinical diagnosis of this disease is essential for preventing the progression of PD. Hence, an innovative method is proposed here based on combining the crow search algorithm and decision tree (CSADT) for the early PD diagnosis. This approach is used on four crucial Parkinson's datasets, including meander, spiral, voice, and speech-Sakar. Using the presented method, PD is effectively diagnosed by evaluating each dataset's critical features and extracting the primary practical outcomes. The used algorithm was compared with other machine learning algorithms of k-nearest neighbor (KNN), support vector machine (SVM), naive Baye (NB), multilayer perceptron (MLP), decision tree (DT), random tree, logistic regression, support vector machine of radial base functions (SVM of RBFs), and combined classifier in terms of accuracy, recall, and combination measure F1. The analytical results emphasize the used algorithm's superiority over the other selected ones. The proposed model yields nearly 100% accuracy through various trials on the datasets. Notably, a high detection speed achieved the lowest detection time of 2.6 seconds. The main novelty of this paper is attributed to the accuracy of the presented PD diagnosis method, which is much higher than its counterparts.
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Affiliation(s)
- Sajjad Amiri Doumari
- Department of Mathematics and Computer Science, Sirjan University of Technology, Sirjan, Iran
| | - Kamal Berahmand
- Department of Information Technology and Communications, Azarbaijan Shahid Madani University, Tabriz, Iran
| | - M. J. Ebadi
- Department of Mathematics, Chabahar Maritime University, Chabahar, Iran
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Luna-Ortiz I, Aldape-Pérez M, Uriarte-Arcia AV, Rodríguez-Molina A, Alarcón-Paredes A, Ventura-Molina E. Parkinson's Disease Detection from Voice Recordings Using Associative Memories. Healthcare (Basel) 2023; 11:healthcare11111601. [PMID: 37297740 DOI: 10.3390/healthcare11111601] [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: 03/24/2023] [Revised: 05/21/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Parkinson's disease (PD) is a neurological condition that is chronic and worsens over time, which presents a challenging diagnosis. An accurate diagnosis is required to recognize PD patients from healthy individuals. Diagnosing PD at early stages can reduce the severity of this disorder and improve the patient's living conditions. Algorithms based on associative memory (AM) have been applied in PD diagnosis using voice samples of patients with this health condition. Even though AM models have achieved competitive results in PD classification, they do not have any embedded component in the AM model that can identify and remove irrelevant features, which would consequently improve the classification performance. In this paper, we present an improvement to the smallest normalized difference associative memory (SNDAM) algorithm by means of a learning reinforcement phase that improves classification performance of SNDAM when it is applied to PD diagnosis. For the experimental phase, two datasets that have been widely applied for PD diagnosis were used. Both datasets were gathered from voice samples from healthy people and from patients who suffer from this condition at an early stage of PD. These datasets are publicly accessible in the UCI Machine Learning Repository. The efficiency of the ISNDAM model was contrasted with that of seventy other models implemented in the WEKA workbench and was compared to the performance of previous studies. A statistical significance analysis was performed to verify that the performance differences between the compared models were statistically significant. The experimental findings allow us to affirm that the proposed improvement in the SNDAM algorithm, called ISNDAM, effectively increases the classification performance compared against well-known algorithms. ISNDAM achieves a classification accuracy of 99.48%, followed by ANN Levenberg-Marquardt with 95.89% and SVM RBF kernel with 88.21%, using Dataset 1. ISNDAM achieves a classification accuracy of 99.66%, followed by SVM IMF1 with 96.54% and RF IMF1 with 94.89%, using Dataset 2. The experimental findings show that ISNDAM achieves competitive performance on both datasets and that statistical significance tests confirm that ISNDAM delivers classification performance equivalent to that of models published in previous studies.
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Affiliation(s)
- Irving Luna-Ortiz
- Instituto Politécnico Nacional, Center for Computing Innovation and Technological Development (CIDETEC), Computational Intelligence Laboratory (CIL), Mexico City 07700, Mexico
| | - Mario Aldape-Pérez
- Instituto Politécnico Nacional, Center for Computing Innovation and Technological Development (CIDETEC), Computational Intelligence Laboratory (CIL), Mexico City 07700, Mexico
| | - Abril Valeria Uriarte-Arcia
- Instituto Politécnico Nacional, Center for Computing Innovation and Technological Development (CIDETEC), Computational Intelligence Laboratory (CIL), Mexico City 07700, Mexico
| | - Alejandro Rodríguez-Molina
- Tecnológico Nacional de México/IT de Tlalnepantla, Research and Postgraduate Division, Tlalnepantla de Baz 54070, Mexico
| | - Antonio Alarcón-Paredes
- Instituto Politécnico Nacional, Center for Computing Research (CIC), Computational Intelligence Laboratory (CIL), Mexico City 07700, Mexico
| | - Elías Ventura-Molina
- Instituto Politécnico Nacional, Center for Computing Innovation and Technological Development (CIDETEC), Computational Intelligence Laboratory (CIL), Mexico City 07700, Mexico
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7
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Biswas SK, Nath Boruah A, Saha R, Raj RS, Chakraborty M, Bordoloi M. Early detection of Parkinson disease using stacking ensemble method. Comput Methods Biomech Biomed Engin 2023; 26:527-539. [PMID: 35587795 DOI: 10.1080/10255842.2022.2072683] [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] [Indexed: 11/03/2022]
Abstract
Parkinson's disease (PD) is a common progressive neurodegenerative disorder that occurs due to corrosion of the substantianigra, located in the thalamic region of the human brain, and is responsible for the transmission of neural signals throughout the human body using brain chemical, termed as "dopamine." Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include the presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Hence, sometimes the process of diagnosis may also be affected by human error. To overcome this problem some intelligent models have been proposed; however, most of them are single classifier-based models and due to this these models cannot handle noisy and imbalanced data properly and thus sometimes overfit the model. To reduce bias and variance, and to avoid overfitting of a single classifier-based model, this paper proposes an ensemble-based PD diagnosis model, named Ensembled Expert System for Diagnosis of Parkinson's Disease (EESDPD) with relevant features and a simple stacking ensemble technique. The proposed EESDPD aggregates diverse assumptions for making the prediction. The performance of the proposed EESDPD is compared with the performances of logistic regression, SVM, Naïve Bayes, Random Forest, XGBoost, simple Decision Tree, B-TDS-PD and B-TESM-PD in terms of classification accuracy, precision, recall and F1-score measures.
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Affiliation(s)
- Saroj Kumar Biswas
- Computer Science and Engineering Department, National Institute of Technology, Silchar, India
| | - Arpita Nath Boruah
- Computer Science and Engineering Department, National Institute of Technology, Silchar, India
| | - Rajib Saha
- Computer Science and Engineering Department, National Institute of Technology, Silchar, India
| | - Ravi Shankar Raj
- Computer Science and Engineering Department, National Institute of Technology, Silchar, India
| | - Manomita Chakraborty
- School of Computer Science and Engineering, VIT-AP University, Amaravathi, India
| | - Monali Bordoloi
- School of Computer Science and Engineering, VIT-AP University, Amaravathi, India
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8
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Nilashi M, Abumalloh RA, Alyami S, Alghamdi A, Alrizq M. Parkinson’s Disease Diagnosis Using Laplacian Score, Gaussian Process Regression and Self-Organizing Maps. Brain Sci 2023; 13:brainsci13040543. [PMID: 37190508 DOI: 10.3390/brainsci13040543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 03/10/2023] [Accepted: 03/18/2023] [Indexed: 03/29/2023] Open
Abstract
Parkinson’s disease (PD) is a complex degenerative brain disease that affects nerve cells in the brain responsible for body movement. Machine learning is widely used to track the progression of PD in its early stages by predicting unified Parkinson’s disease rating scale (UPDRS) scores. In this paper, we aim to develop a new method for PD diagnosis with the aid of supervised and unsupervised learning techniques. Our method is developed using the Laplacian score, Gaussian process regression (GPR) and self-organizing maps (SOM). SOM is used to segment the data to handle large PD datasets. The models are then constructed using GPR for the prediction of the UPDRS scores. To select the important features in the PD dataset, we use the Laplacian score in the method. We evaluate the developed approach on a PD dataset including a set of speech signals. The method was evaluated through root-mean-square error (RMSE) and adjusted R-squared (adjusted R²). Our findings reveal that the proposed method is efficient in the prediction of UPDRS scores through a set of speech signals (dysphonia measures). The method evaluation showed that SOM combined with the Laplacian score and Gaussian process regression with the exponential kernel provides the best results for R-squared (Motor-UPDRS = 0.9489; Total-UPDRS = 0.9516) and RMSE (Motor-UPDRS = 0.5144; Total-UPDRS = 0.5105) in predicting UPDRS compared with the other kernels in Gaussian process regression.
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Bhakar S, Sinwar D, Pradhan N, Dhaka VS, Cherrez-Ojeda I, Parveen A, Hassan MU. Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains. Diagnostics (Basel) 2023; 13:diagnostics13071212. [PMID: 37046431 PMCID: PMC10093052 DOI: 10.3390/diagnostics13071212] [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: 01/29/2023] [Revised: 03/06/2023] [Accepted: 03/08/2023] [Indexed: 04/14/2023] Open
Abstract
Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease. The problem of disease severity identification can be considered multi-class classification, where the class labels are the severity levels of the disease. Plenty of computational intelligence-based solutions have been presented by researchers for severity identification. This paper presents a comprehensive review of recent approaches for identifying disease severity levels using computational intelligence-based approaches. We followed the PRISMA guidelines and compiled several works related to the severity identification of multidisciplinary diseases of the last decade from well-known publishers, such as MDPI, Springer, IEEE, Elsevier, etc. This article is devoted toward the severity identification of two main diseases, viz. Parkinson's Disease and Diabetic Retinopathy. However, severity identification of a few other diseases, such as COVID-19, autonomic nervous system dysfunction, tuberculosis, sepsis, sleep apnea, psychosis, traumatic brain injury, breast cancer, knee osteoarthritis, and Alzheimer's disease, was also briefly covered. Each work has been carefully examined against its methodology, dataset used, and the type of disease on several performance metrics, accuracy, specificity, etc. In addition to this, we also presented a few public repositories that can be utilized to conduct research on disease severity identification. We hope that this review not only acts as a compendium but also provides insights to the researchers working on disease severity identification using computational intelligence-based approaches.
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Affiliation(s)
- Suman Bhakar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
| | - Deepak Sinwar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
| | - Nitesh Pradhan
- Department of Computer Science and Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
| | - Vijaypal Singh Dhaka
- Department of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
| | - Ivan Cherrez-Ojeda
- Allergy and Pulmonology, Espíritu Santo University, Samborondón 0901-952, Ecuador
| | - Amna Parveen
- College of Pharmacy, Gachon University, Medical Campus, No. 191, Hambakmoero, Yeonsu-gu, Incheon 21936, Republic of Korea
| | - Muhammad Umair Hassan
- Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway
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10
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Early diagnosis of Parkinson's disease: A combined method using deep learning and neuro-fuzzy techniques. Comput Biol Chem 2023; 102:107788. [PMID: 36410240 DOI: 10.1016/j.compbiolchem.2022.107788] [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: 06/03/2022] [Revised: 10/28/2022] [Accepted: 11/08/2022] [Indexed: 11/11/2022]
Abstract
Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predicting UPDRS. In this research, we target to present a combined approach for PD diagnosis using an ensemble learning approach with the ability of online learning from clinical large datasets. The method is developed using Deep Belief Network (DBN) and Neuro-Fuzzy approaches. A clustering approach, Expectation-Maximization (EM), is used to handle large datasets. The Principle Component Analysis (PCA) technique is employed for noise removal from the data. The UPDRS prediction models are constructed for PD diagnosis. To handle the missing data, K-NN is used in the proposed method. We use incremental machine learning approaches to improve the efficiency of the proposed method. We assess our approach on a real-world PD dataset and the findings are assessed compared to other PD diagnosis approaches developed by machine learning techniques. The findings revealed that the approach can improve the UPDRS prediction accuracy and the time complexity of previous methods in handling large datasets.
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11
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A type-2 neuro-fuzzy system with a novel learning method for Parkinson’s disease diagnosis. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04276-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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di Biase L, Pecoraro PM, Pecoraro G, Caminiti ML, Di Lazzaro V. Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring. SENSORS (BASEL, SWITZERLAND) 2022; 22:8486. [PMID: 36366187 PMCID: PMC9656920 DOI: 10.3390/s22218486] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/20/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent trends of telemedicine, there is an ongoing positive impulse in moving medical assistance and management from hospitals to home settings. Different technologies have been proposed for indoor monitoring over the past decades, with different degrees of invasiveness, complexity, and capabilities in full-body monitoring. The major classes of devices proposed are inertial-based sensors (IMU), vision-based devices, and geomagnetic and radiofrequency (RF) based sensors. In recent years, among all available technologies, there has been an increasing interest in using RF-based technology because it can provide a more accurate and reliable method of tracking patients' movements compared to other methods, such as camera-based systems or wearable sensors. Indeed, RF technology compared to the other two techniques has higher compliance, low energy consumption, does not need to be worn, is less susceptible to noise, is not affected by lighting or other physical obstacles, has a high temporal resolution without a limited angle of view, and fewer privacy issues. The aim of the present narrative review was to describe the potential applications of RF-based indoor monitoring techniques and highlight their differences compared to other monitoring technologies.
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Affiliation(s)
- Lazzaro di Biase
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
- Brain Innovations Lab, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy
| | - Pasquale Maria Pecoraro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| | - Giovanni Pecoraro
- Department of Electronics Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Maria Letizia Caminiti
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| | - Vincenzo Di Lazzaro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
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13
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Ghane M, Ang MC, Nilashi M, Sorooshian S. Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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14
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An Efficient Rotation Forest-Based Ensemble Approach for Predicting Severity of Parkinson’s Disease. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5524852. [PMID: 35783585 PMCID: PMC9246609 DOI: 10.1155/2022/5524852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 10/07/2021] [Accepted: 05/10/2022] [Indexed: 12/03/2022]
Abstract
Parkinson's disease (PD) is a neurodegenerative nervous system disorder that mainly affects body movement, and it is one of the most common diseases, particularly in elderly individuals. This paper proposes a new machine learning approach to predict Parkinson's disease severity using UCI's Parkinson's telemonitoring voice dataset. The proposed method analyses the patient's voice data and classifies them into “severe” and “nonsevere” classes. At first, a subset of features was selected, then a novel approach with a combination of Rotation Forest and Random Forest was applied on selected features to determine each patient's disease severity. Analysis of the experimental results shows that the proposed approach can detect the severity of PD patients in the early stages. Moreover, the proposed model is compared with several algorithms, and the results indicate that the model is highly successful in classifying records and outperformed the other methods concerning classification accuracy and F1-measure rate.
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Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol 2022; 39:120. [PMID: 35704152 PMCID: PMC9198206 DOI: 10.1007/s12032-022-01711-1] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 03/14/2022] [Indexed: 10/28/2022]
Abstract
The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.
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Affiliation(s)
- Sameer Quazi
- GenLab Biosolutions Private Limited, Bangalore, Karnataka, 560043, India.
- Department of Biomedical Sciences, School of Life Sciences, Anglia Ruskin University, Cambridge, UK.
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Abstract
The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.
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Affiliation(s)
- Sameer Quazi
- GenLab Biosolutions Private Limited, Bangalore, Karnataka, 560043, India.
- Department of Biomedical Sciences, School of Life Sciences, Anglia Ruskin University, Cambridge, UK.
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A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement. SENSORS 2022; 22:s22114070. [PMID: 35684689 PMCID: PMC9185243 DOI: 10.3390/s22114070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 12/10/2022]
Abstract
The recognition of abnormal gait behavior is important in the field of motion assessment and disease diagnosis. Currently, abnormal gait behavior is primarily recognized by pressure and inertial data obtained from wearable sensors. However, the data drift and wearing difficulties for patients have impeded the application of these wearable sensors. Here, we propose a contactless abnormal gait behavior recognition method that captures human pose data using a monocular camera. A lightweight OpenPose (OP) model is generated with Depthwise Separable Convolution to recognize joint points and extract their coordinates during walking in real time. For the walking data errors extracted in the 2D plane, a 3D reconstruction is performed on the walking data, and a total of 11 types of abnormal gait features are extracted by the OP model. Finally, the XGBoost algorithm is used for feature screening. The final experimental results show that the Random Forest (RF) algorithm in combination with 3D features delivers the highest precision (92.13%) for abnormal gait behavior recognition. The proposed scheme overcomes the data drift of inertial sensors and sensor wearing challenges in the elderly while reducing the hardware requirements for model deployment. With excellent real-time and contactless capabilities, the scheme is expected to enjoy a wide range of applications in the field of abnormal gait measurement.
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Parkinson’s disease diagnosis using neural networks: Survey and comprehensive evaluation. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Ali L, Chakraborty C, He Z, Cao W, Imrana Y, Rodrigues JJPC. A novel sample and feature dependent ensemble approach for Parkinson’s disease detection. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07046-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractParkinson’s disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed EOFSC model is constructed. To evaluate the final prediction of the EOFSC model, the majority voting methodology is adopted. Experimental results point out that feature selection integration with neural networks improves the performance of conventional neural networks. Additionally, feature selection integration with DNN outperforms feature selection integration with conventional machine learning models. Finally, the newly developed ensemble model is observed to improve PD detection accuracy by 6.5%.
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Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson's disease. Commun Biol 2022; 5:58. [PMID: 35039601 PMCID: PMC8763910 DOI: 10.1038/s42003-022-03002-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 12/23/2021] [Indexed: 11/09/2022] Open
Abstract
Parkinson's disease (PD) is one of the first diseases where digital biomarkers demonstrated excellent performance in differentiating disease from healthy individuals. However, no study has systematically compared and leveraged multiple types of digital biomarkers to predict PD. Particularly, machine learning works on the fine-motor skills of PD are limited. Here, we developed deep learning methods that achieved an AUC (Area Under the receiver operator characteristic Curve) of 0.933 in identifying PD patients on 6418 individuals using 75048 tapping accelerometer and position records. Performance of tapping is superior to gait/rest and voice-based models obtained from the same benchmark population. Assembling the three models achieved a higher AUC of 0.944. Notably, the models not only correlated strongly to, but also performed better than patient self-reported symptom scores in diagnosing PD. This study demonstrates the complementary predictive power of tapping, gait/rest and voice data and establishes integrative deep learning-based models for identifying PD.
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Azadi H, Akbarzadeh-T. MR, Shoeibi A, Kobravi HR. Evaluating the Effect of Parkinson's Disease on Jitter and Shimmer Speech Features. Adv Biomed Res 2021; 10:54. [PMID: 35127581 PMCID: PMC8781904 DOI: 10.4103/abr.abr_254_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 08/22/2021] [Accepted: 08/31/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Parkinson's disease (PD) is a neurological disorder caused by decreasing dopamine in the brain. Speech is one of the first functions that are disrupted. Accordingly, speech features are a promising indicator in PD diagnosis for telemedicine applications. The purpose of this study is to investigate the impact of Parkinson's disease on a minimal set of Jitter and Shimmer voice indicators and studying the difference between male and female speech features in noisy/noiseless environments. MATERIALS AND METHODS Our data includes 47 samples from nursing homes and neurology clinics, with 23 patients and 24 healthy individuals. The optimal feature for each category is studied separately for the men's and women's samples. The focus here is on the phonation in which the vowel/a/is expressed by the participants. The main features, including Jitter and Shimmer perturbations, are extracted. To find an optimal pair under both noisy and noiseless circumstance, we use the Relief feature selection strategy. RESULTS This research shows that the Jitter feature for men and women with Parkinson's is 21 and 33.4, respectively. While the Shimmer feature is 0.1 and 0.06. In addition, by using these two features alone, we reach a correct diagnosis rate of 79% and 81% for noisy and noiseless states, respectively. CONCLUSION The PD effects on the speech features can be accurately identified. Evaluating the extracted features suggests that the absolute value of the selected feature in men with PD is higher than for healthy ones. Whereas, in the case of women, this is the opposite.
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Affiliation(s)
- Hamid Azadi
- Department of Electrical Engineering, Biomedical Engineering Group, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mohammad-R. Akbarzadeh-T.
- Department of Electrical Engineering, Biomedical Engineering Group, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Ali Shoeibi
- Department of Neurology. School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamid Reza Kobravi
- Department of Biomedical Engineering, Islamic Azad University of Mashhad, Mashhad, Iran
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Yang M, Ma J, Wang P, Huang Z, Li Y, Liu H, Hameed Z. Hierarchical Boosting Dual-Stage Feature Reduction Ensemble Model for Parkinson's Disease Speech Data. Diagnostics (Basel) 2021; 11:diagnostics11122312. [PMID: 34943549 PMCID: PMC8700329 DOI: 10.3390/diagnostics11122312] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/24/2021] [Accepted: 11/30/2021] [Indexed: 11/16/2022] Open
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.
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Affiliation(s)
- Mingyao Yang
- College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400000, China; (M.Y.); (J.M.); (P.W.); (Z.H.)
| | - Jie Ma
- College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400000, China; (M.Y.); (J.M.); (P.W.); (Z.H.)
| | - Pin Wang
- College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400000, China; (M.Y.); (J.M.); (P.W.); (Z.H.)
| | - Zhiyong Huang
- College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400000, China; (M.Y.); (J.M.); (P.W.); (Z.H.)
- Correspondence: (Z.H.); (Y.L.); Tel.: +86-138-83216321 (Z.H.); +86-023-65103544 (Y.L.)
| | - Yongming Li
- College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400000, China; (M.Y.); (J.M.); (P.W.); (Z.H.)
- Correspondence: (Z.H.); (Y.L.); Tel.: +86-138-83216321 (Z.H.); +86-023-65103544 (Y.L.)
| | - He Liu
- Chongqing Academy of Educational Sciences, Chongqing 400000, China;
| | - Zeeshan Hameed
- College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400000, China; (M.Y.); (J.M.); (P.W.); (Z.H.)
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Naranjo L, Pérez CJ, Campos-Roca Y, Madruga M. Replication-based regularization approaches to diagnose Reinke's edema by using voice recordings. Artif Intell Med 2021; 120:102162. [PMID: 34629154 DOI: 10.1016/j.artmed.2021.102162] [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: 02/15/2021] [Revised: 08/21/2021] [Accepted: 08/31/2021] [Indexed: 10/20/2022]
Abstract
Reinke's edema is one of the most prevalent laryngeal pathologies. Its detection can be addressed by using computer-aided diagnosis systems based on features extracted from speech recordings. When extracting acoustic features from different voice recordings of a particular subject at a concrete moment, imperfections in technology and the very biological variability result in values that are close, but they are not identical. This suggests that the within-subject variability must be properly addressed in the statistical methodology. Regularization-based regression approaches can be used to reduce the classification errors by favoring the best predictors and penalizing the worst ones. Three replication-based regularization approaches for variable selection and classification have been specifically designed and implemented to take into account the underlying within-subject variability. In order to illustrate the applicability of these approaches, an experiment has been specifically conducted to discriminate Reinke's edema patients (30 subjects) from healthy people (30 subjects) in a hospital environment. The features have been extracted from four phonations of the sustained vowel /a/ recorded for each subject, leading to a database that has fed the proposed machine learning approaches. The proposed replication-based approaches have been proved to be reliable in terms of selected features and predictive ability, leading to a stable accuracy rate of 0.89 under a cross-validation framework. Also, a comparison with traditional independence-based regularization methods reports a great variability of the latter in terms of selected features and accuracy metrics. Therefore, the proposed approaches contribute to fill a gap in the scientific literature on statistical approaches considering within-subject variability and can be used to build a robust expert system.
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Affiliation(s)
- Lizbeth Naranjo
- Departamento de Matemáticas, Facultad de Ciencias, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
| | - Carlos J Pérez
- Departamento de Matemáticas, Facultad de Veterinaria, Universidad de Extremadura, 10003 Cáceres, Spain.
| | - Yolanda Campos-Roca
- Departamento de Tecnologías de los Computadores y de las Comunicaciones, Escuela Politécnica, Universidad de Extremadura, 10003 Cáceres, Spain
| | - Mario Madruga
- Departamento de Matemáticas, Facultad de Veterinaria, Universidad de Extremadura, 10003 Cáceres, Spain
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Ma J, Zhang Y, Li Y, Zhou L, Qin L, Zeng Y, Wang P, Lei Y. Deep dual-side learning ensemble model for Parkinson speech recognition. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102849] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Meghraoui D, Boudraa B, Merazi-Meksen T, Gómez Vilda P. A novel pre-processing technique in pathologic voice detection: Application to Parkinson’s disease phonation. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102604] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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26
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Application of Genetic Algorithms for the Selection of Neural Network Architecture in the Monitoring System for Patients with Parkinson’s Disease. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11125470] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This article describes an approach for collecting and pre-processing phone owner data, including their voice, in order to classify their condition using data mining methods. The most important research results presented in this article are the developed approaches for the processing of patient voices and the use of genetic algorithms to select the architecture of the neural network in the monitoring system for patients with Parkinson’s disease. The process used to pre-process a person’s voice is described in order to determine the main parameters that can be used in assessing a person’s condition. It is shown that the efficiency of using genetic algorithms for constructing neural networks depends on the composition of the data. As a result, the best result in the accuracy of assessing the patient’s condition can be obtained by a hybrid approach, where a part of the neural network architecture is selected analytically manually, while the other part is built automatically.
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Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00555-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
AbstractThe diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.
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28
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Mei J, Desrosiers C, Frasnelli J. Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature. Front Aging Neurosci 2021; 13:633752. [PMID: 34025389 PMCID: PMC8134676 DOI: 10.3389/fnagi.2021.633752] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 03/22/2021] [Indexed: 12/26/2022] Open
Abstract
Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.
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Affiliation(s)
- Jie Mei
- Chemosensory Neuroanatomy Lab, Department of Anatomy, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC, Canada
| | - Christian Desrosiers
- Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle (LIVIA), Department of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Johannes Frasnelli
- Chemosensory Neuroanatomy Lab, Department of Anatomy, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC, Canada
- Centre de Recherche de l'Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Île-de-Montréal (CIUSSS du Nord-de-l'Île-de-Montréal), Montreal, QC, Canada
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Probabilistic Neural Network-based Model for Identification of Parkinson’s Disease by using Voice Profile and Personal Data. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-020-05080-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Gunduz H. An efficient dimensionality reduction method using filter-based feature selection and variational autoencoders on Parkinson's disease classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102452] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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31
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Local discriminant preservation projection embedded ensemble learning based dimensionality reduction of speech data of Parkinson’s disease. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102165] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Butt AH, Rovini E, Fujita H, Maremmani C, Cavallo F. Data-Driven Models for Objective Grading Improvement of Parkinson's Disease. Ann Biomed Eng 2020; 48:2976-2987. [PMID: 33006005 PMCID: PMC7723941 DOI: 10.1007/s10439-020-02628-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 09/18/2020] [Indexed: 12/20/2022]
Abstract
Parkinson's disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients' quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time.
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Affiliation(s)
- Abdul Haleem Butt
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, 34, 56025, Pontedera, Italy.,Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, 56127, Pisa, Italy.,The Creative Technology Department, Faculty of Computing and Artificial Intelligence, Air University Islamabad Pakistan, Service Road E-9/E-8, Islamabad, Pakistan
| | - Erika Rovini
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, 34, 56025, Pontedera, Italy.,Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
| | - Hamido Fujita
- Intelligent Software Systems Lab, Iwate Prefectural University, 152-52, Sugo, Takizawa, Iwate, Japan
| | - Carlo Maremmani
- U.O. Neurologia, Ospedale delle Apuane (AUSL Toscana Nord Ovest), Viale Mattei 21, 54100, Massa, Italy
| | - Filippo Cavallo
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, 34, 56025, Pontedera, Italy. .,Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, 56127, Pisa, Italy. .,The Department of Industrial Engineering, University of Florence, Via Santa Marta 3, 50139, Florence, Italy.
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Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155135] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.
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Ul Haq A, Li J, Memon MH, khan J, Ali Z, Abbas SZ, Nazir S. Recognition of the parkinson’s disease using a hybrid feature selection approach. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-200075] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Amin Ul Haq
- School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
| | - Jianping Li
- School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
| | - Muhammad Hammad Memon
- School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
| | - Jalaluddin khan
- School of Computer Science and Engineering University of Electronic Science and Technology of China, Chengdu, China
| | - Zafar Ali
- School of Computer Science and Engineering Southeast University, Nanjing, China
| | - Syed Zaheer Abbas
- School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China
| | - Shah Nazir
- Department of Computer Science, University of Swabi, Pakistan
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Kazemipoor M, Rezaeian M, Kazemipoor M, Hamzah S, Shandilya SK. Computational Intelligence Techniques for Assessing Anthropometric Indices Changes in Female Athletes. Curr Med Imaging 2020; 16:288-295. [PMID: 32410532 DOI: 10.2174/1573405614666180905111814] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 06/28/2018] [Accepted: 07/30/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND Physical characteristics including body size and configuration, are considered as one of the key influences on the optimum performance in athletes. Despite several analyzing methods for modeling the slimming estimation in terms of reduction in anthropometric indices, there are still weaknesses of these models such as being very demanding including time taken for analysis and accuracy. OBJECTIVES This research proposes a novel approach for determining the slimming effect of a herbal composition as a natural medicine for weight loss. METHODS To build an effective prediction model, a modern hybrid approach, merging adaptivenetwork- based fuzzy inference system and particle swarm optimization (ANFIS-PSO) was constructed for prediction of changes in anthropometric indices including waist circumference, waist to hip ratio, thigh circumference and mid-upper arm circumference, on female athletes after consumption of caraway extract during ninety days clinical trial. RESULTS The outcomes showed that caraway extract intake was effective on lowering all anthropometric indices in female athletes after ninety days trial. The results of analysis by ANFIS-PSO was more accurate compared to SPSS. Also, the efficiency of the proposed approach was confirmed using the existing data. CONCLUSION It is concluded that a development in predictive accuracy and simplification capability could be attained by hybrid adaptive neuro-fuzzy techniques as modern approaches in detecting changes in body characteristics. These developed techniques could be more useful and valid than other conventional analytical methods for clinical applications.
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Affiliation(s)
| | - Mehdi Rezaeian
- Computer Engineering Department, Yazd University, Yazd, Iran
| | - Maryam Kazemipoor
- Department of Endodontics, Faculty of Dentistry, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Sareena Hamzah
- Sports Centre, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Shishir Kumar Shandilya
- Department of Computer Science & Engineering, School of Computing Science & Engineering, VIT Bhopal University, Bhopal, India
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A deep learning approach for prediction of Parkinson's disease progression. Biomed Eng Lett 2020; 10:227-239. [PMID: 32477610 DOI: 10.1007/s13534-020-00156-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 02/20/2020] [Accepted: 04/07/2020] [Indexed: 01/22/2023] Open
Abstract
This paper proposes a deep neural network (DNN) model using the reduced input feature space of Parkinson's telemonitoring dataset to predict Parkinson's disease (PD) progression. PD is a chronic and progressive nervous system disorder that affects body movement. PD is assessed by using the unified Parkinson's disease rating scale (UPDRS). In this paper, firstly, principal component analysis (PCA) is employed to the featured dataset to address the multicollinearity problems in the dataset and to reduce the dimension of input feature space. Then, the reduced input feature space is fed into the proposed DNN model with a tuned parameter norm penalty (L2) and analyses the prediction performance of it in PD progression by predicting Motor and Total-UPDRS score. The model's performance is evaluated by conducting several experiments and the result is compared with the result of previously developed methods on the same dataset. The model's prediction accuracy is measured by fitness parameters, mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). The MAE, RMSE, and R2 values are 0.926, 1.422, and 0.970 respectively for motor-UPDRS. These values are 1.334, 2.221, and 0.956 respectively for Total-UPDRS. Both the Motor and Total-UPDRS score is better predicted by the proposed method. This paper shows the usefulness and efficacy of the proposed method for predicting the UPDRS score in PD progression.
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Assessment of the Status of Patients with Parkinson's Disease Using Neural Networks and Mobile Phone Sensors. Diagnostics (Basel) 2020; 10:diagnostics10040214. [PMID: 32290633 PMCID: PMC7235735 DOI: 10.3390/diagnostics10040214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 04/09/2020] [Accepted: 04/11/2020] [Indexed: 11/16/2022] Open
Abstract
Parkinson's disease (PD) is one of the most common chronic neurological diseases and one of the significant causes of disability for middle-aged and elderly people. Monitoring the patient's condition and its compliance is the key to the success of the correction of the main clinical manifestations of PD, including the almost inevitable modification of the clinical picture of the disease against the background of prolonged dopaminergic therapy. In this article, we proposed an approach to assessing the condition of patients with PD using deep recurrent neural networks, trained on data measured using mobile phones. The data was received in two modes: background (data from the phone's sensors) and interactive (data directly entered by the user). For the classification of the patient's condition, we built various models of the neural network. Testing of these models showed that the most efficient was a recurrent network with two layers. The results of the experiment show that with a sufficient amount of the training sample, it is possible to build a neural network that determines the condition of the patient according to the data from the mobile phone sensors with a high probability.
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Polat K, Nour M. Parkinson disease classification using one against all based data sampling with the acoustic features from the speech signals. Med Hypotheses 2020; 140:109678. [PMID: 32197120 DOI: 10.1016/j.mehy.2020.109678] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/11/2020] [Accepted: 03/15/2020] [Indexed: 11/25/2022]
Abstract
Parkinson's disease (PD) is a long-term degenerative disease that primarily affects the motor system of the central nervous system. This disease is difficult to diagnose and is one of the common diseases in the public. In this paper, we have proposed a novel data sampling method for the classification of Parkinson disease based on the acoustic features from the speech signals. In the proposed data sampling method, the one against all (OGA) has been used to divide the dataset into five equal parts. With applying the OGA to the PD dataset having two classes (healthy and Parkinson disease), the minority and majority classes have been obtained. First of all, for healthy class in the dataset (first case), five equal partitions have been composed and then for PD class in the dataset (second case), five equal partitions have been composed. To classify the these all data partitions, we have used three different classifiers including the weighted k-NN (nearest neighbor), Logistic Regression (LR), and support vector machine with medium Gaussian kernel function. In order to evaluate the performance of the proposed hybrid models (the combination of classifiers and OGA based data sampling), the classification accuracy, the confusion matrix, and area under the Receiver Operating Characteristic (ROC) curve (AUC) have been used. While the LR, SVM with Gaussian, and weighted k-NN classifiers achieved the classification accuracies of 77.50%, 83.80%, and 82.10% in the classification of PD with the acoustic features, the combinations of classifiers and OGA based data sampling (first case) obtained the 79.04%, 87.36%, and 88.48% using the LR, SVM with Gaussian, and weighted k-NN classifiers, respectively. In the second case, the obtained classification accuracies are the 84.30%, 88.76%, and 89.46% using the LR, SVM with Gaussian, and weighted k-NN classifiers with the OGA based data sampling, respectively. The achieved results have shown that the proposed the one against all (OGA) based data sampling could be used in the combination of classifier algorithms as the data pre-processing method in the classification of Parkinson's disease with acoustic features.
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Affiliation(s)
- Kemal Polat
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, 14280 Bolu, Turkey.
| | - Majid Nour
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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Yücelbaş Ş. Simple Logistic Hybrid System Based on Greedy Stepwise Algorithm for Feature Analysis to Diagnose Parkinson’s Disease According to Gender. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020. [DOI: 10.1007/s13369-020-04357-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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40
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Ali L, Zhu C, Zhang Z, Liu Y. Automated Detection of Parkinson's Disease Based on Multiple Types of Sustained Phonations Using Linear Discriminant Analysis and Genetically Optimized Neural Network. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2019; 7:2000410. [PMID: 32166050 PMCID: PMC6876932 DOI: 10.1109/jtehm.2019.2940900] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 07/30/2019] [Accepted: 09/04/2019] [Indexed: 11/24/2022]
Abstract
Objective: Parkinson’s disease (PD) is a serious neurodegenerative disorder. It is
reported that most of PD patients have voice impairments. But these voice impairments are
not perceptible to common listeners. Therefore, different machine learning methods have
been developed for automated PD detection. However, these methods either lack
generalization and clinically significant classification performance or face the problem
of subject overlap. Methods: To overcome the problems discussed above, we attempt to
develop a hybrid intelligent system that can automatically perform acoustic analysis of
voice signals in order to detect PD. The proposed intelligent system uses linear
discriminant analysis (LDA) for dimensionality reduction and genetic algorithm (GA) for
hyperparameters optimization of neural network (NN) which is used as a predictive model.
Moreover, to avoid subject overlap, we use leave one subject out (LOSO) validation.
Results: The proposed method namely LDA-NN-GA is evaluated in numerical experiments on
multiple types of sustained phonations data in terms of accuracy, sensitivity,
specificity, and Matthew correlation coefficient. It achieves classification accuracy of
95% on training database and 100% on testing database using all the
extracted features. However, as the dataset is imbalanced in terms of gender, thus, to
obtain unbiased results, we eliminated the gender dependent features and obtained accuracy
of 80% for training database and 82.14% for testing database, which seems to
be more unbiased results. Conclusion: Compared with the previous machine learning methods,
the proposed LDA-NN-GA method shows better performance and lower complexity. Clinical
Impact: The experimental results suggest that the proposed automated diagnostic system has
the potential to classify PD patients from healthy subjects. Additionally, in future the
proposed method can also be exploited for prodromal and differential diagnosis, which are
considered challenging tasks.
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Affiliation(s)
- Liaqat Ali
- School of Information and Communication EngineeringUniversity of Electronic Science and Technology of China (UESTC)Chengdu611731China
| | - Ce Zhu
- School of Information and Communication EngineeringUniversity of Electronic Science and Technology of China (UESTC)Chengdu611731China
| | - Zhonghao Zhang
- School of Information and Communication EngineeringUniversity of Electronic Science and Technology of China (UESTC)Chengdu611731China
| | - Yipeng Liu
- School of Information and Communication EngineeringUniversity of Electronic Science and Technology of China (UESTC)Chengdu611731China
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41
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Shahid AH, Singh M. Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.05.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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42
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Kaur P, Sharma M. Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis. J Med Syst 2019; 43:204. [DOI: 10.1007/s10916-019-1341-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 05/03/2019] [Accepted: 05/13/2019] [Indexed: 10/26/2022]
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43
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Mostafa SA, Mustapha A, Mohammed MA, Hamed RI, Arunkumar N, Abd Ghani MK, Jaber MM, Khaleefah SH. Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease. COGN SYST RES 2019. [DOI: 10.1016/j.cogsys.2018.12.004] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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44
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A survey on computer-assisted Parkinson's Disease diagnosis. Artif Intell Med 2019; 95:48-63. [DOI: 10.1016/j.artmed.2018.08.007] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 06/14/2018] [Accepted: 08/25/2018] [Indexed: 12/28/2022]
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Naranjo L, Pérez CJ, Fuentes-García R, Martín J. A hidden Markov model addressing measurement errors in the response and replicated covariates for continuous nondecreasing processes. Biostatistics 2019; 21:743-757. [DOI: 10.1093/biostatistics/kxz004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Revised: 01/25/2019] [Accepted: 01/29/2019] [Indexed: 11/13/2022] Open
Abstract
Summary
Motivated by a study tracking the progression of Parkinson’s disease (PD) based on features extracted from voice recordings, an inhomogeneous hidden Markov model with continuous state-space is proposed. The approach addresses the measurement error in the response, the within-subject variability of the replicated covariates and presumed nondecreasing response. A Bayesian framework is described and an efficient Markov chain Monte Carlo method is developed. The model performance is evaluated through a simulation-based example and the analysis of a PD tracking progression dataset is presented. Although the approach was motivated by a PD tracking progression problem, it can be applied to any monotonic nondecreasing process whose continuous response variable is subject to measurement errors and where replicated covariates play a key role.
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Affiliation(s)
- Lizbeth Naranjo
- Departamento de Matemáticas, Facultad de Ciencias, Universidad Nacional Autónoma de México, Circuito Exterior s/n, Ciudad Universitaria, Del. Coyoacán, C.P. 04510 Ciudad de México, Mexico
| | - Carlos J Pérez
- Departamento de Matemáticas, Facultad de Veterinaria, Universidad de Extremadura, Avda. de la Universidad s/n, C.P. 10003 Cáceres, Spain
| | - Ruth Fuentes-García
- Departamento de Matemáticas, Facultad de Ciencias, Universidad Nacional Autónoma de México, Circuito Exterior s/n, Ciudad Universitaria, Del. Coyoacán, C.P. 04510 Ciudad de México, Mexico
| | - Jacinto Martín
- Departamento de Matemáticas, Facultad de Ciencias, Universidad de Extremadura, Avda. de Elvas s/n, C.P. 06006 Badajoz, Spain
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46
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Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G. Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04069-0] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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47
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Khoury N, Attal F, Amirat Y, Oukhellou L, Mohammed S. Data-Driven Based Approach to Aid Parkinson's Disease Diagnosis. SENSORS (BASEL, SWITZERLAND) 2019; 19:E242. [PMID: 30634600 PMCID: PMC6359125 DOI: 10.3390/s19020242] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 01/03/2019] [Accepted: 01/04/2019] [Indexed: 11/22/2022]
Abstract
This article presents a machine learning methodology for diagnosing Parkinson's disease (PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the gait cycle. A classification engine assigns subjects to healthy or Parkinsonian classes. The diagnosis process involves four steps: data pre-processing, feature extraction and selection, data classification and performance evaluation. The selected features are used as inputs of each classifier. Feature selection is achieved through a wrapper approach established using the random forest algorithm. The proposed methodology uses both supervised classification methods including K-nearest neighbour (K-NN), decision tree (DT), random forest (RF), Naïve Bayes (NB), support vector machine (SVM) and unsupervised classification methods such as K-means and the Gaussian mixture model (GMM). To evaluate the effectiveness of the proposed methodology, an online dataset collected within three different studies is used. This data set includes vGRF measurements collected from eight force sensors placed under each foot of the subjects. Ninety-three patients suffering from Parkinson's disease and 72 healthy subjects participated in the experiments. The obtained performances are compared with respect to various metrics including accuracy, precision, recall and F-measure. The classification performance evaluation is performed using the leave-one-out cross validation. The results demonstrate the ability of the proposed methodology to accurately differentiate between PD subjects and healthy subjects. For the purpose of validation, the proposed methodology is also evaluated with an additional dataset including subjects with neurodegenerative diseases (Amyotrophic Lateral Sclerosis (ALS) and Huntington's disease (HD)). The obtained results show the effectiveness of the proposed methodology to discriminate PD subjects from subjects with other neurodegenerative diseases with a relatively high accuracy.
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Affiliation(s)
- Nicolas Khoury
- Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Créteil (UPEC), 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France.
| | - Ferhat Attal
- Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Créteil (UPEC), 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France.
| | - Yacine Amirat
- Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Créteil (UPEC), 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France.
| | - Latifa Oukhellou
- French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR), University of Paris-Est, COSYS, GRETTIA, F-77447 Marne la Vallée, France.
| | - Samer Mohammed
- Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Créteil (UPEC), 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, France.
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Hegde S, Shetty S, Rai S, Dodderi T. A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders. J Voice 2018; 33:947.e11-947.e33. [PMID: 30316551 DOI: 10.1016/j.jvoice.2018.07.014] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 07/06/2018] [Accepted: 07/10/2018] [Indexed: 10/28/2022]
Abstract
The human voice production system is an intricate biological device capable of modulating pitch and loudness. Inherent internal and/or external factors often damage the vocal folds and result in some change of voice. The consequences are reflected in body functioning and emotional standing. Hence, it is paramount to identify voice changes at an early stage and provide the patient with an opportunity to overcome any ramification and enhance their quality of life. In this line of work, automatic detection of voice disorders using machine learning techniques plays a key role, as it is proven to help ease the process of understanding the voice disorder. In recent years, many researchers have investigated techniques for an automated system that helps clinicians with early diagnosis of voice disorders. In this paper, we present a survey of research work conducted on automatic detection of voice disorders and explore how it is able to identify the different types of voice disorders. We also analyze different databases, feature extraction techniques, and machine learning approaches used in these research works.
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Affiliation(s)
- Sarika Hegde
- NMAM Institute of Technology, Udupi, Karnataka, India.
| | | | - Smitha Rai
- NMAM Institute of Technology, Udupi, Karnataka, India
| | - Thejaswi Dodderi
- Nitte Institute of Speech & Hearing, Mangaluru, Karnataka, India
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49
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Cai Z, Gu J, Wen C, Zhao D, Huang C, Huang H, Tong C, Li J, Chen H. An Intelligent Parkinson's Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:2396952. [PMID: 30034509 PMCID: PMC6032994 DOI: 10.1155/2018/2396952] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 04/02/2018] [Accepted: 05/21/2018] [Indexed: 11/17/2022]
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.
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Affiliation(s)
- Zhennao Cai
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jianhua Gu
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Caiyun Wen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, China
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China
| | - Chunyu Huang
- College of Computer Science and Technology, Changchun University of Science Technology, Changchun 130032, China
| | - Hui Huang
- College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Changfei Tong
- College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Jun Li
- College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
| | - Huiling Chen
- College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
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50
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Tagaris A, Kollias D, Stafylopatis A, Tagaris G, Kollias S. Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset. INT J ARTIF INTELL T 2018. [DOI: 10.1142/s0218213018500112] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Neurodegenerative disorders, such as Alzheimer’s and Parkinson’s, constitute a major factor in long-term disability and are becoming more and more a serious concern in developed countries. As there are, at present, no effective therapies, early diagnosis along with avoidance of misdiagnosis seem to be critical in ensuring a good quality of life for patients. In this sense, the adoption of computer-aided-diagnosis tools can offer significant assistance to clinicians. In the present paper, we provide in the first place a comprehensive recording of medical examinations relevant to those disorders. Then, a review is conducted concerning the use of Machine Learning techniques in supporting diagnosis of neurodegenerative diseases, with reference to at times used medical datasets. Special attention has been given to the field of Deep Learning. In addition to that, we communicate the launch of a newly created dataset for Parkinson’s disease, containing epidemiological, clinical and imaging data, which will be publicly available to researchers for benchmarking purposes. To assess the potential of the new dataset, an experimental study in Parkinson’s diagnosis is carried out, based on state-of-the-art Deep Neural Network architectures and yielding very promising accuracy results.
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Affiliation(s)
- Athanasios Tagaris
- School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, Athens, 15780, Greece
| | - Dimitrios Kollias
- School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, Athens, 15780, Greece
| | - Andreas Stafylopatis
- School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, Athens, 15780, Greece
| | - Georgios Tagaris
- Department of Neurology, Georgios Gennimatas General Hospital, Athens, Greece
| | - Stefanos Kollias
- School of Computer Science, University of Lincoln, Lincoln, United Kingdom
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