LSTM and Convolution Networks exploration for Parkinson’s Diagnosis

Reyes, Juan Felipe; Montealegre, James Steven; Castaño, Yor; Urcuqui, Christian; Navarro, Andrés

doi:10.1109/colcomcon.2019.8809160

Cited by 14 publications

(8 citation statements)

References 7 publications

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“…Around half of the included studies used convolution neural networks ( n = 37); afterward, other neural networks ( n = 31) were implemented in the included studies, followed by artificial neural networks (ANNs) ( n = 10), recurrent neural networks (RNNs) ( n = 9), and fuzzy neural networks (FNNs), as shown in Table 3 . In the end, the most imitated neural network architecture in the included studies was LSTM ( n = 11) [ 6 , 34 , 36 , 38 , 40 , 65 , 70 , 74 , 77 , 80 , 83 ], VGG ( n = 3) [ 18 , 27 , 58 ], and DNN ( n = 6) [ 34 , 35 , 60 , 91 , 92 , 103 ]. Recently, with the developments of new techniques such as convolutional neural network [ 101 ] and transfer learning [ 63 ], deep learning gained significant advances in the computer vision tasks, e.g., ImageNet [ 77 ].…”

Section: Resultsmentioning

confidence: 99%

“…Clinical studies can refer to a video recorded for the patient while performing physical activities such as a PD bed test. As mentioned, in [ 18 , 43 , 70 , 87 ], a neural network was able to identify the symptoms of PD through a video sample of the patient. In the future, the clinical studies may analyze any video recorded in the hospital for other patients, for example, during therapy sessions, and predict if this patient is suspected of having PD in the future.…”

Section: Discussionmentioning

confidence: 99%

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The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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“…Seven machine learning algorithms were chosen based on the results of previous studies (Urcuqui et al, 2018 ; Reyes et al, 2019 ; Alzubaidi et al, 2021 ). Six of the selected algorithms were trained using R statistical software (logistic regression, decision tree without processing, pre-pruning decision tree, post-pruning decision tree, naive Bayes, and random forest).…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Classifiers to Evaluate Data From Gait Analysis With Depth Cameras in Patients With Parkinson’s Disease

Muñoz-Ospina

Álvarez-García

Clavijo-Moran

et al. 2022

Front. Hum. Neurosci.

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IntroductionThe assessments of the motor symptoms in Parkinson’s disease (PD) are usually limited to clinical rating scales (MDS UPDRS III), and it depends on the clinician’s experience. This study aims to propose a machine learning technique algorithm using the variables from upper and lower limbs, to classify people with PD from healthy people, using data from a portable low-cost device (RGB-D camera). And can be used to support the diagnosis and follow-up of patients in developing countries and remote areas.MethodsWe used Kinect®eMotion system to capture the spatiotemporal gait data from 30 patients with PD and 30 healthy age-matched controls in three walking trials. First, a correlation matrix was made using the variables of upper and lower limbs. After this, we applied a backward feature selection model using R and Python to determine the most relevant variables. Three further analyses were done using variables selected from backward feature selection model (Dataset A), movement disorders specialist (Dataset B), and all the variables from the dataset (Dataset C). We ran seven machine learning models for each model. Dataset was divided 80% for algorithm training and 20% for evaluation. Finally, a causal inference model (CIM) using the DoWhy library was performed on Dataset B due to its accuracy and simplicity.ResultsThe Random Forest model is the most accurate for all three variable Datasets (Dataset A: 81.8%; Dataset B: 83.6%; Dataset C: 84.5%) followed by the support vector machine. The CIM shows a relation between leg variables and the arms swing asymmetry (ASA) and a proportional relationship between ASA and the diagnosis of PD with a robust estimator (1,537).ConclusionsMachine learning techniques based on objective measures using portable low-cost devices (Kinect®eMotion) are useful and accurate to classify patients with Parkinson’s disease. This method can be used to evaluate patients remotely and help clinicians make decisions regarding follow-up and treatment.

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“…Firstly, postural and kinematic features are extracted from videos that are recorded while PD patients and healthy controls are walking, to discriminate them. In [109,110], ANNs are trained with signals collected via the Microsoft Kinect (MS Kinect) sensors to diagnose PD. In [109], the combination of a CNN with an LSTM outperforms the standalone CNN and LSTM classifiers, reaching up to 83.1% accuracy, while in [110], the ANN classifier outperforms the SVM model, with 89.4% accuracy.…”

Section: Image and Depth Sensorsmentioning

confidence: 99%

“…In [109,110], ANNs are trained with signals collected via the Microsoft Kinect (MS Kinect) sensors to diagnose PD. In [109], the combination of a CNN with an LSTM outperforms the standalone CNN and LSTM classifiers, reaching up to 83.1% accuracy, while in [110], the ANN classifier outperforms the SVM model, with 89.4% accuracy. In the last study, besides gait data, features extracted from finger-and foot-tapping activities have been evaluated, without however improving the classification performance.…”

Section: Image and Depth Sensorsmentioning

confidence: 99%

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Giannakopoulou

Roussaki

Demestichas

2022

Sensors

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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.

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LSTM and Convolution Networks exploration for Parkinson’s Diagnosis

Cited by 14 publications

References 7 publications

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

Machine Learning Classifiers to Evaluate Data From Gait Analysis With Depth Cameras in Patients With Parkinson’s Disease

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

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