1
|
Teo YX, Lee RE, Nurzaman SG, Tan CP, Chan PY. Action tremor features discovery for essential tremor and Parkinson's disease with explainable multilayer BiLSTM. Comput Biol Med 2024; 180:108957. [PMID: 39098236 DOI: 10.1016/j.compbiomed.2024.108957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 07/04/2024] [Accepted: 07/26/2024] [Indexed: 08/06/2024]
Abstract
The tremors of Parkinson's disease (PD) and essential tremor (ET) are known to have overlapping characteristics that make it complicated for clinicians to distinguish them. While deep learning is robust in detecting features unnoticeable to humans, an opaque trained model is impractical in clinical scenarios as coincidental correlations in the training data may be used by the model to make classifications, which may result in misdiagnosis. This work aims to overcome the aforementioned challenge of deep learning models by introducing a multilayer BiLSTM network with explainable AI (XAI) that can better explain tremulous characteristics and quantify the respective discovered important regions in tremor differentiation. The proposed network classifies PD, ET, and normal tremors during drinking actions and derives the contribution from tremor characteristics, (i.e., time, frequency, amplitude, and actions) utilized in the classification task. The analysis shows that the XAI-BiLSTM marks the regions with high tremor amplitude as important in classification, which is verified by a high correlation between relevance distribution and tremor displacement amplitude. The XAI-BiLSTM discovered that the transition phases from arm resting to lifting (during the drinking cycle) is the most important action to classify tremors. Additionally, the XAI-BiLSTM reveals frequency ranges that only contribute to the classification of one tremor class, which may be the potential distinctive feature to overcome the overlapping frequencies problem. By revealing critical timing and frequency patterns unique to PD and ET tremors, this proposed XAI-BiLSTM model enables clinicians to make more informed classifications, potentially reducing misclassification rates and improving treatment outcomes.
Collapse
Affiliation(s)
- Yu Xuan Teo
- Department of Electrical & Robotics Engineering, School of Engineering, Monash University Malaysia, Malaysia.
| | - Rui En Lee
- Department of Electrical & Robotics Engineering, School of Engineering, Monash University Malaysia, Malaysia.
| | - Surya Girinatha Nurzaman
- Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, Bandar Sunway, Malaysia.
| | - Chee Pin Tan
- Department of Electrical & Robotics Engineering, School of Engineering, Monash University Malaysia, Malaysia.
| | - Ping Yi Chan
- Department of Electrical & Robotics Engineering, School of Engineering, Monash University Malaysia, Malaysia.
| |
Collapse
|
2
|
Muñoz-Mata BG, Dorantes-Méndez G, Piña-Ramírez O. Classification of Parkinson's disease severity using gait stance signals in a spatiotemporal deep learning classifier. Med Biol Eng Comput 2024:10.1007/s11517-024-03148-2. [PMID: 38884852 DOI: 10.1007/s11517-024-03148-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 06/03/2024] [Indexed: 06/18/2024]
Abstract
Parkinson's disease (PD) is a degenerative nervous system disorder involving motor disturbances. Motor alterations affect the gait according to the progression of PD and can be used by experts in movement disorders to rate the severity of the disease. However, this rating depends on the expertise of the clinical specialist. Therefore, the diagnosis may be inaccurate, particularly in the early stages of PD where abnormal gait patterns can result from normal aging or other medical conditions. Consequently, several classification systems have been developed to enhance PD diagnosis. In this paper, a PD gait severity classification algorithm was developed using vertical ground reaction force (VGRF) signals. The VGRF records used are from a public database that includes 93 PD patients and 72 healthy controls adults. The work presented here focuses on modeling each foot's gait stance phase signals using a modified convolutional long deep neural network (CLDNN) architecture. Subsequently, the results of each model are combined to predict PD severity. The classifier performance was evaluated using ten-fold cross-validation. The best-weighted accuracies obtained were 99.296(0.128)% and 99.343(0.182)%, with the Hoehn-Yahr and UPDRS scales, respectively, outperforming previous results presented in the literature. The classifier proposed here can effectively differentiate gait patterns of different PD severity levels based on gait signals of the stance phase.
Collapse
Affiliation(s)
- Brenda G Muñoz-Mata
- Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Av. Parque Chapultepec 1570, San Luis Potosí, 78295, San Luis Potosí, México
| | - Guadalupe Dorantes-Méndez
- Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Av. Parque Chapultepec 1570, San Luis Potosí, 78295, San Luis Potosí, México.
| | - Omar Piña-Ramírez
- Departamento de Bioinformática y Análisis Estadísticos, Instituto Nacional de Perinatología "Isidro Espinosa de los Reyes", Montes Urales 800, Ciudad de México, 11000, Ciudad de México, México
| |
Collapse
|
3
|
Guo Q, Liu S, Wang L, Feng K, Yang S. Analysis of microstate features for Parkinson's disease based on reliability validation. J Neurosci Methods 2024; 406:110115. [PMID: 38531478 DOI: 10.1016/j.jneumeth.2024.110115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/13/2024] [Accepted: 03/20/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND Parkinson's disease (PD) is a disorder with abnormal changes in brain activity. The lack of objective indicators makes the assessment of PD progression difficult. Assessment of brain activity changes in PD may offer a potential solution. NEW METHOD Electroencephalogram (EEG) microstates reflect global dynamic changes in the brain. Therefore, we utilized microstates to assess changes in PD brain activity. However, the effect of epoch duration on the reliability of microstate analyses in PD is unclear. Thus, we first assessed the effect of data duration on the reliability of microstate topography and temporal features in PD and older healthy individuals. According to the reliability assessment, EEG epochs with high reliability were selected for microstate analysis in PD. Finally, we investigated the correlation between microstate features and clinical scales to determine whether these features could serve as objective indicators to evaluate PD progression. RESULTS Microstate analysis features that show high reliability for 3 min and above epoch durations. The topology of microstate D was significantly changed in PD compared to healthy controls, as well as the temporal features of microstates C and D. Additionally, the occurrence of C was negatively correlated with MoCA, and the duration of D was positively correlated with UPDRS. COMPARISON WITH EXISTING METHOD(S) High reliability of PD microstate features obtained by our approach. CONCLUSION EEG for PD microstate analysis should be at least 3 min. Microstate analysis is expected to provide new ideas and objective indicators for assessing Parkinson's disease progression in the clinical setting.
Collapse
Affiliation(s)
- Qingfang Guo
- Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; State Key Laboratory of Reliable and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
| | - Shuo Liu
- Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; State Key Laboratory of Reliable and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
| | - Lei Wang
- Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; State Key Laboratory of Reliable and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
| | - Keke Feng
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China.
| | - Shuo Yang
- Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; State Key Laboratory of Reliable and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China.
| |
Collapse
|
4
|
Abumalloh RA, Nilashi M, Samad S, Ahmadi H, Alghamdi A, Alrizq M, Alyami S. Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review. Ageing Res Rev 2024; 96:102285. [PMID: 38554785 DOI: 10.1016/j.arr.2024.102285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 03/20/2024] [Accepted: 03/24/2024] [Indexed: 04/02/2024]
Abstract
Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.
Collapse
Affiliation(s)
- Rabab Ali Abumalloh
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar
| | - Mehrbakhsh Nilashi
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam; School of Computer Science, Duy Tan University, Da Nang, Vietnam; UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, Cheras, Kuala Lumpur 56000, Malaysia; Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, Penang 11800, Malaysia.
| | - Sarminah Samad
- Faculty of Business, UNITAR International University, Tierra Crest, Jalan SS6/3, Petaling Jaya, Selangor 47301, Malaysia
| | - Hossein Ahmadi
- Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth PL4 8AA, UK
| | - Abdullah Alghamdi
- Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia; AI Lab, Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia
| | - Mesfer Alrizq
- Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia; AI Lab, Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia
| | - Sultan Alyami
- AI Lab, Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia; Computer Science Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
| |
Collapse
|
5
|
Demir B, Ulukaya S, Erdem O. Detection of Parkinson's disease with keystroke data. Comput Methods Biomech Biomed Engin 2023; 26:1653-1667. [PMID: 37599616 DOI: 10.1080/10255842.2023.2245516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/21/2023] [Accepted: 07/16/2023] [Indexed: 08/22/2023]
Abstract
Parkinson's disease (PD) is one of the most widespread neurological disorders associated with nerve damage without definitive treatment. Impairments, such as trembling and slowing down in hand movements are among the first symptoms. For this purpose, in this study, machine learning (ML)-based models were developed by using keyboard keystroke dynamics. According to patients' drug use status, disease severity, and gender, we created 14 different sub-datasets and extracted 378 features using raw keystroke data. We developed alternative models with Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) algorithms. We further used Minimum Redundancy Maximum Relevance (mRmR), RELIEF, sequential forward selection (SFS), and RF feature selection methods to investigate prominent features in distinguishing PD. We developed ML models that jointly use the most popular features of selection algorithms (feature ensemble [FE]) and an ensemble classifier by combining multiple ML algorithms utilizing majority vote (model ensemble [ME]). With 14 different sets, FE and ME models provided accuracy (Acc.) in the range of 91.73 - 100% and 81.08 - 100%, respectively.
Collapse
Affiliation(s)
- Bahar Demir
- Department of Computational Science, Trakya University, Edirne, Turkey
| | - Sezer Ulukaya
- Department of Electrical and Electronics Engineering, Trakya University, Edirne, Turkey
| | - Oğuzhan Erdem
- Department of Electrical and Electronics Engineering, Trakya University, Edirne, Turkey
| |
Collapse
|
6
|
Nijhawan R, Kumar M, Arya S, Mendirtta N, Kumar S, Towfek SK, Khafaga DS, Alkahtani HK, Abdelhamid AA. A Novel Artificial-Intelligence-Based Approach for Classification of Parkinson's Disease Using Complex and Large Vocal Features. Biomimetics (Basel) 2023; 8:351. [PMID: 37622956 PMCID: PMC10452203 DOI: 10.3390/biomimetics8040351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/28/2023] [Accepted: 07/31/2023] [Indexed: 08/26/2023] Open
Abstract
Parkinson's disease (PD) affects a large proportion of elderly people. Symptoms include tremors, slow movement, rigid muscles, and trouble speaking. With the aging of the developed world's population, this number is expected to rise. The early detection of PD and avoiding its severe consequences require a precise and efficient system. Our goal is to create an accurate AI model that can identify PD using human voices. We developed a transformer-based method for detecting PD by retrieving dysphonia measures from a subject's voice recording. It is uncommon to use a neural network (NN)-based solution for tabular vocal characteristics, but it has several advantages over a tree-based approach, including compatibility with continuous learning and the network's potential to be linked with an image/voice encoder for a more accurate multi modal solution, shifting SOTA approach from tree-based to a neural network (NN) is crucial for advancing research in multimodal solutions. Our method outperforms the state of the art (SOTA), namely Gradient-Boosted Decision Trees (GBDTs), by at least 1% AUC, and the precision and recall scores are also improved. We additionally offered an XgBoost-based feature-selection method and a fully connected NN layer technique for including continuous dysphonia measures, in addition to the solution network. We also discussed numerous important discoveries relating to our suggested solution and deep learning (DL) and its application to dysphonia measures, such as how a transformer-based network is more resilient to increased depth compared to a simple MLP network. The performance of the proposed approach and conventional machine learning techniques such as MLP, SVM, and Random Forest (RF) have also been compared. A detailed performance comparison matrix has been added to this article, along with the proposed solution's space and time complexity.
Collapse
Affiliation(s)
- Rahul Nijhawan
- Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, India
| | - Mukul Kumar
- Blackstraw Technologies Pvt Ltd., Chennai 160015, India
| | | | - Neha Mendirtta
- Computer Science and Engineering, Chandigarh University, Ajitgarh 140413, India
| | - Sunil Kumar
- Department of Computer Science and Artificial Intelligence, SR University, Warangal 506371, India
- Department of Computer Science, Graphic Era Hill University, Dehradun 248001, India
| | - S. K. Towfek
- Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
| | - Doaa Sami Khafaga
- Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Hend K. Alkahtani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Abdelaziz A. Abdelhamid
- Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia;
- Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
| |
Collapse
|
7
|
Wu P, Cao B, Liang Z, Wu M. The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson's disease. Front Aging Neurosci 2023; 15:1191378. [PMID: 37502426 PMCID: PMC10368956 DOI: 10.3389/fnagi.2023.1191378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/05/2023] [Indexed: 07/29/2023] Open
Abstract
Background Parkinson's disease is a neurological disorder that can cause gait disturbance, leading to mobility issues and falls. Early diagnosis and prediction of freeze episodes are essential for mitigating symptoms and monitoring the disease. Objective This review aims to evaluate the use of artificial intelligence (AI)-based gait evaluation in diagnosing and managing Parkinson's disease, and to explore the potential benefits of this technology for clinical decision-making and treatment support. Methods A thorough review of published literature was conducted to identify studies, articles, and research related to AI-based gait evaluation in Parkinson's disease. Results AI-based gait evaluation has shown promise in preventing freeze episodes, improving diagnosis, and increasing motor independence in patients with Parkinson's disease. Its advantages include higher diagnostic accuracy, continuous monitoring, and personalized therapeutic interventions. Conclusion AI-based gait evaluation systems hold great promise for managing Parkinson's disease and improving patient outcomes. They offer the potential to transform clinical decision-making and inform personalized therapies, but further research is needed to determine their effectiveness and refine their use.
Collapse
Affiliation(s)
- Peng Wu
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Biwei Cao
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei, China
- Hubei Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
| | - Zhendong Liang
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Miao Wu
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei, China
- Hubei Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
| |
Collapse
|
8
|
Pedrero-Sánchez JF, Belda-Lois JM, Serra-Añó P, Mollà-Casanova S, López-Pascual J. Classification of Parkinson's disease stages with a two-stage deep neural network. Front Aging Neurosci 2023; 15:1152917. [PMID: 37333459 PMCID: PMC10272759 DOI: 10.3389/fnagi.2023.1152917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/16/2023] [Indexed: 06/20/2023] Open
Abstract
Introduction Parkinson's disease is one of the most prevalent neurodegenerative diseases. In the most advanced stages, PD produces motor dysfunction that impairs basic activities of daily living such as balance, gait, sitting, or standing. Early identification allows healthcare personnel to intervene more effectively in rehabilitation. Understanding the altered aspects and impact on the progression of the disease is important for improving the quality of life. This study proposes a two-stage neural network model for the classifying the initial stages of PD using data recorded with smartphone sensors during a modified Timed Up & Go test. Methods The proposed model consists on two stages: in the first stage, a semantic segmentation of the raw sensor signals classifies the activities included in the test and obtains biomechanical variables that are considered clinically relevant parameters for functional assessment. The second stage is a neural network with three input branches: one with the biomechanical variables, one with the spectrogram image of the sensor signals, and the third with the raw sensor signals. Results This stage employs convolutional layers and long short-term memory. The results show a mean accuracy of 99.64% for the stratified k-fold training/validation process and 100% success rate of participants in the test phase. Discussion The proposed model is capable of identifying the three initial stages of Parkinson's disease using a 2-min functional test. The test easy instrumentation requirements and short duration make it feasible for use feasible in the clinical context.
Collapse
Affiliation(s)
| | - Juan Manuel Belda-Lois
- Instituto de Biomecánica (IBV), Universitat Politècnica de València, Valencia, Spain
- Department of Mechanical and Materials Engineering (DIMM), Universitat Politècnica de València, Valencia, Spain
| | - Pilar Serra-Añó
- UBIC, Department of Physiotherapy, Faculty of Physiotherapy, Universitat de València, Valencia, Spain
| | - Sara Mollà-Casanova
- UBIC, Department of Physiotherapy, Faculty of Physiotherapy, Universitat de València, Valencia, Spain
| | - Juan López-Pascual
- Instituto de Biomecánica (IBV), Universitat Politècnica de València, Valencia, Spain
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
Zhu M, Men Q, Ho ESL, Leung H, Shum HPH. A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction. J Med Syst 2022; 46:76. [PMID: 36201114 PMCID: PMC9537228 DOI: 10.1007/s10916-022-01857-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 08/17/2022] [Indexed: 11/24/2022]
Abstract
Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations may not always be objective. To facilitate early diagnosis, recent deep learning-based methods have shown promising results for automated analysis, which can discover patterns that have not been found in traditional machine learning methods. We observe that existing work mostly applies deep learning on individual joint features such as the time series of joint positions. Due to the challenge of discovering inter-joint features such as the distance between feet (i.e. the stride width) from generally smaller-scale medical datasets, these methods usually perform sub-optimally. As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data. Due to the distinctive nature of the two types of features, we introduce a two-stream framework, with one stream learning from the time series of joint position and the other from the time series of relative joint displacement. We further develop a mid-layer fusion module to combine the discovered patterns in these two streams for diagnosis, which results in a complementary representation of the data for better prediction performance. We validate our system with a benchmark dataset of 3D skeleton motion that involves 45 patients with musculoskeletal and neurological disorders, and achieve a prediction accuracy of 95.56%, outperforming state-of-the-art methods.
Collapse
Affiliation(s)
- Manli Zhu
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Qianhui Men
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Edmond S. L. Ho
- School of Computing Science, University of Glasgow, Glasgow, UK
| | - Howard Leung
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
| | | |
Collapse
|
11
|
A deep learning approach for parkinson’s disease severity assessment. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00698-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
Abstract
Purpose
Parkinson’s Disease comes on top among neurodegenerative diseases affecting 10 million worldwide. To detect Parkinson’s Disease in a prior state, gait analysis is an effective choice. However, monitoring of Parkinson’s Disease using gait analysis is time consuming and exhaustive for patients and physicians. To assess severity of symptoms, a rating scale called Unified Parkinson's Disease Rating Scale is used. It determines mild and severe cases. Today, Parkinson’s Disease severity assessment is made in gait laboratories and by manual examination. These are time consuming and it is costly for health institutions to build and maintain laboratories. By using low-cost wearables and an effective model, aforementioned problems can be solved.
Methods
We provide a computerized solution for quantifiable assessment of Parkinson’s Disease symptoms severity. By using wearable sensors, our framework can predict exact symptom values to assess Parkinson’s Disease severity. We propose a deep learning approach that utilizes Ground Reaction Force sensors. From sensor signals, features are extracted and fed to a hybrid deep learning model. This model is the combination of Convolutional Neural Networks and Locally Weighted Random Forest.
Results
Proposed framework achieved 0.897, 3.009, 4.556 in terms of Correlation Coefficient, Mean Absolute Error and Root Mean Square Error, respectively. Proposed framework outperformed other machine and deep learning models. We also evaluated classification performance for disease detection. We outperformed most of the previous studies, achieving 99.5% accuracy, 98.7% sensitivity and 99.1% specificity.
Conclusion
This is the first study to use a deep learning regression approach to predict exact symptom value of Parkinson’s Disease patients. Results show that this approach can be effectively employed as a disease severity assessment tool using wearable sensors.
Collapse
|
12
|
Guo Y, Yang J, Liu Y, Chen X, Yang GZ. Detection and assessment of Parkinson's disease based on gait analysis: A survey. Front Aging Neurosci 2022; 14:916971. [PMID: 35992585 PMCID: PMC9382193 DOI: 10.3389/fnagi.2022.916971] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.
Collapse
Affiliation(s)
- Yao Guo
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Jianxin Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Yuxuan Liu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Xun Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
13
|
Yuan X, Chen S, Sun C, Yuwen L. A novel early diagnostic framework for chronic diseases with class imbalance. Sci Rep 2022; 12:8614. [PMID: 35597855 PMCID: PMC9123399 DOI: 10.1038/s41598-022-12574-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: 02/18/2022] [Accepted: 05/12/2022] [Indexed: 11/09/2022] Open
Abstract
Chronic diseases are one of the most severe health issues in the world, due to their terrible clinical presentations such as long onset cycle, insidious symptoms, and various complications. Recently, machine learning has become a promising technique to assist the early diagnosis of chronic diseases. However, existing works ignore the problems of feature hiding and imbalanced class distribution in chronic disease datasets. In this paper, we present a universal and efficient diagnostic framework to alleviate the above two problems for diagnosing chronic diseases timely and accurately. Specifically, we first propose a network-limited polynomial neural network (NLPNN) algorithm to efficiently capture high-level features hidden in chronic disease datasets, which is data augmentation in terms of its feature space and can also avoid over-fitting. Then, to alleviate the class imbalance problem, we further propose an attention-empowered NLPNN algorithm to improve the diagnostic accuracy for sick cases, which is also data augmentation in terms of its sample space. We evaluate the proposed framework on nine public and two real chronic disease datasets (partly with class imbalance). Extensive experiment results demonstrate that the proposed diagnostic algorithms outperform state-of-the-art machine learning algorithms, and can achieve superior performances in terms of accuracy, recall, F1, and G_mean. The proposed framework can help to diagnose chronic diseases timely and accurately at an early stage.
Collapse
Affiliation(s)
- Xiaohan Yuan
- School of Big Data and Software Engineering, Chongqing University, Chongqing, China
| | - Shuyu Chen
- School of Big Data and Software Engineering, Chongqing University, Chongqing, China.
| | - Chuan Sun
- School of Big Data and Software Engineering, Chongqing University, Chongqing, China
| | - Lu Yuwen
- School of Big Data and Software Engineering, Chongqing University, Chongqing, China
| |
Collapse
|
14
|
Giannakopoulou KM, Roussaki I, Demestichas K. Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:1799. [PMID: 35270944 PMCID: PMC8915040 DOI: 10.3390/s22051799] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/17/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
Parkinson's disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients' quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson's disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson's disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson's disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.
Collapse
Affiliation(s)
- Konstantina-Maria Giannakopoulou
- School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (K.-M.G.); (K.D.)
- Institute of Communication and Computer Systems, 10682 Athens, Greece
| | - Ioanna Roussaki
- School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (K.-M.G.); (K.D.)
- Institute of Communication and Computer Systems, 10682 Athens, Greece
| | - Konstantinos Demestichas
- School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (K.-M.G.); (K.D.)
- Institute of Communication and Computer Systems, 10682 Athens, Greece
| |
Collapse
|
15
|
A hybrid deep transfer learning-based approach for Parkinson's disease classification in surface electromyography signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103161] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
16
|
Loh HW, Hong W, Ooi CP, Chakraborty S, Barua PD, Deo RC, Soar J, Palmer EE, Acharya UR. Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021). SENSORS 2021; 21:s21217034. [PMID: 34770340 PMCID: PMC8587636 DOI: 10.3390/s21217034] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/07/2021] [Accepted: 10/19/2021] [Indexed: 12/18/2022]
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.
Collapse
Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
| | - Wanrong Hong
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Ravinesh C Deo
- School of Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia
| | - Jeffrey Soar
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Elizabeth E Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick, NSW 2031, Australia
- School of Women's and Children's Health, University of New South Wales, Randwick, NSW 2031, Australia
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413, Taiwan
- Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
| |
Collapse
|
17
|
A dual-branch model for diagnosis of Parkinson’s disease based on the independent and joint features of the left and right gait. APPL INTELL 2021. [DOI: 10.1007/s10489-020-02182-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
18
|
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.
Collapse
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
| |
Collapse
|
19
|
E B, D B, Elumalai VK, K U. Data-driven gait analysis for diagnosis and severity rating of Parkinson's disease. Med Eng Phys 2021; 91:54-64. [PMID: 34074466 DOI: 10.1016/j.medengphy.2021.03.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 03/03/2021] [Accepted: 03/19/2021] [Indexed: 10/21/2022]
Abstract
Parkinsons disease (PD) is the second most neurodegenerative disease, which results in gradual loss of movements. To diagnose PD in a clinical setting, clinicians generally use clinical manifestations like motor and non-motor symptoms and rate the severity based on unified Parkinsons disease rating scale (UPDRS). Such clinical assessment largely depends on the expertise and experience of the clinicians and it is subjective leading to variation in assessment between clinicians. As the gait of people with Parkinson's generally differs from gait of healthy age-matched adults, the assessment of gait abnormalities can lead to not only the diagnosis of PD but also the rating of severity level based on motor symptoms. Hence, in this paper, a data-driven gait classification framework using the supervised machine learning algorithms is presented. Using the publicly available gait datasets acquired using vertical ground reaction force (VGRF) sensors, we present a correlation based feature extraction technique for improved stage classification of PD. Significant biomarkers from spatiotemporal gait features are obtained based on the correlation, and the normal distribution of the gait dataset is assessed using the Shapiro-Wilk test. Subsequently, four supervised machine learning algorithms, namely, K-nearest neighbours (KNN), Naive Bayes (NB), Ensemble classifier (EC) and Support vector machine (SVM) are used to rate the severity level of PD according to the Hoehn and Yahr (H&Y) scale. The performance of the classifiers, assessed using the confusion matrix and parallel coordinate plots, highlights that SVM can result in a classification accuracy of 98.4%. Moreover, with minimal gait feature set acquired based on the rank correlation, the proposed approach outperforms several other state-of-the-art methods that have used the same dataset for PD stage classification.
Collapse
Affiliation(s)
- Balaji E
- Department of Biomedical Engineering, PSG College of Technology, Coimbatore 641004, India.
| | - Brindha D
- Department of Biomedical Engineering, PSG College of Technology, Coimbatore 641004, India
| | - Vinodh Kumar Elumalai
- School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamilnadu 632014, India
| | - Umesh K
- Department of Biomedical Engineering, PSG College of Technology, Coimbatore 641004, India
| |
Collapse
|