4.7 Review

Multi-Modal Deep Learning Diagnosis of Parkinson's Disease-A Systematic Review

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

A Vision-Based Framework for Predicting Multiple Sclerosis and Parkinson's Disease Gait Dysfunctions-A Deep Learning Approach

Rachneet Kaur et al.

Summary: This study examined the effectiveness of a vision-based framework for predicting gait dysfunction in multiple sclerosis (MS) and Parkinson's disease (PD). The study collected gait video data and extracted 3D joint keypoints from MS, PD patients and healthy older adults. By comparing 16 different machine learning and deep learning algorithms, the study demonstrated the potential of using a multi-view digital camera-based gait analysis framework for neurological gait dysfunction prediction. This research suggests the possibility of using inexpensive vision-based systems for diagnosing certain neurological disorders.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2023)

Review Computer Science, Artificial Intelligence

Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review

Yogesh H. Bhosale et al.

Summary: Covid-19 is a severe and alarming disease of the twentieth century, and radiography imaging using deep learning has been employed for identification and prediction of the virus. Various artificial intelligence-based systems have been developed for early detection of Covid-19, but there are still limitations and security concerns.

NEURAL PROCESSING LETTERS (2023)

Article Chemistry, Analytical

Evaluating Gait Impairment in Parkinson's Disease from Instrumented Insole and IMU Sensor Data

Vassilis Tsakanikas et al.

Summary: Parkinson's disease is a complex disease with motor and non-motor symptoms, some of which affect gait and balance. Monitoring patients' mobility and extracting gait parameters using sensors has become a reliable method for evaluating treatment efficacy and disease progression. The use of insoles and body-worn IMU-based devices has been evaluated in this study for assessing gait impairment, and both have shown promise for accurate machine learning-based detection of PD gait impairment.

SENSORS (2023)

Article Computer Science, Artificial Intelligence

Real-time detection of freezing of gait in Parkinson's disease using multi-head convolutional neural networks and a single inertial sensor

Luigi Borzi et al.

Summary: This work proposes a robust real-time freezing of gait detection algorithm that can be implemented in stand-alone devices working in non-supervised conditions, and has demonstrated good classification performance on data sets of Parkinson's disease patients and healthy elderly subjects.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2023)

Review Computer Science, Interdisciplinary Applications

A Systematic Review of Artificial Intelligence (AI) Based Approaches for the Diagnosis of Parkinson's Disease

S. Saravanan et al.

Summary: Parkinson's disease is a neurodegenerative disorder that primarily affects the elderly, and there is currently no cure. Early diagnosis can improve the patient's quality of life and predict other neurodegenerative diseases. Artificial Intelligence techniques show promise in Parkinson's disease diagnosis.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2022)

Article Computer Science, Information Systems

A Pairwise Deep Ranking Model for Relative Assessment of Parkinson's Disease Patients From Gait Signals

Burcin Buket Ogul et al.

Summary: Continuous monitoring of PD symptoms is essential for improving patients' quality of life. Existing assessment methods are subjective and unreliable, leading to the development of a new model for relative evaluation of patients using gait signals from foot-worn sensors. This model shows promising results in pairwise ranking predictions, outperforming previous methods for PD monitoring.

IEEE ACCESS (2022)

Review Materials Science, Multidisciplinary

Plantar Pressure-Based Insole Gait Monitoring Techniques for Diseases Monitoring and Analysis: A Review

Jun-Liang Chen et al.

Summary: Insole-based plantar pressure monitoring systems have emerged as a leading technology for monitoring the progression of chronic diseases worldwide. This technology utilizes the strong correlation between gait and disease status. The review covers working principles, design considerations, algorithms, disease monitoring applications, and potential solutions to common challenges in the field.

ADVANCED MATERIALS TECHNOLOGIES (2022)

Article Engineering, Biomedical

An investigation about the relationship between dysarthria level of speech and the neurological state of Parkinson?s patients

Biswajit Karan et al.

Summary: Parkinson's disease is a common neurological disorder that primarily affects elderly individuals. This study investigates the relationship between voice disorders and the neurological state of patients by analyzing speech data. The findings suggest that the severity of dysarthria can reflect the progression of Parkinson's disease.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2022)

Review Computer Science, Interdisciplinary Applications

Medical deep learning-A systematic meta-review

Jan Egger et al.

Summary: Deep learning has made remarkable impact in various scientific disciplines, such as image processing and autonomous driving. It has also shown great potential in the medical domain. However, obtaining a complete overview of the field of 'medical deep learning' is becoming increasingly difficult due to the abundance of patient data and the rapid growth of deep learning research.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

Review Chemistry, Analytical

Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis

Lauren C. Benson et al.

Summary: This scoping review summarizes the research on using IMUs to record running biomechanics over the past 20 years. Most studies were conducted indoors, over small distances, and focused on recreational runners. Future research should aim to incorporate more real-world settings.

SENSORS (2022)

Article Multidisciplinary Sciences

Motion characteristics of subclinical tremors in Parkinson's disease and normal subjects

Ping Yi Chan et al.

Summary: The study found that there are differences in tremor motion and characteristics between subclinical Parkinson's disease tremors and normal tremors. By measuring hand-arm motion, the study found that subclinical Parkinson's disease tremors have significantly higher amplitude and peak frequency in specific predominant motions compared to normal tremors. Additionally, the characteristic of increased frequency in flexion-extension of normal postural tremor is observed in patients with Parkinson's disease, specifically in the wrist and elbow joints.

SCIENTIFIC REPORTS (2022)

Article Computer Science, Information Systems

Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson's Disease: A Novel Deep One-Class Classifier

Nader Naghavi et al.

Summary: Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson's disease, which impairs walking and increases fall risk. On-demand external cueing systems can help individuals overcome freezing, but predicting and detecting FoG remains challenging. A Deep Gait Anomaly Detector (DGAD) using transfer learning showed improvement in detection accuracy, with average sensitivity of 63.0% and specificity of 98.6%.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Article Neurosciences

Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms

Xupo Xing et al.

Summary: This study evaluated seven predictive models using machine learning algorithms to differentiate between Parkinson's disease and essential tremor. The results showed that random forest and extreme gradient boosting models had the best predictive ability. The analysis also revealed that the dominant frequency and average amplitude of surface electromyogram signals from flexors, as well as resting and winging postures, had the greatest impact on the diagnosis of Parkinson's disease.

FRONTIERS IN NEUROSCIENCE (2022)

Article Engineering, Biomedical

A Kinematic Data-Driven Approach to Differentiate Involuntary Choreic Movements in Individuals With Neurological Conditions

Yunda Liu et al.

Summary: This study investigates the kinematic differences between HD chorea and PD LID and successfully develops a classification model to differentiate between them, laying the foundation for the development of a screening tool and providing insights into their pathophysiology.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2022)

Article Engineering, Biomedical

Validation of a Spatiotemporal Gait Model Using Inertial Measurement Units for Early-Stage Parkinson's Disease Detection During Turns

Yifan Yang et al.

Summary: This study proposes a spatiotemporal gait model using inertial measurement units for assessing gait performance in both straight walking and turns in early-stage Parkinson's disease (PD) patients. The results demonstrate that the proposed model achieves highly accurate and reliable gait measurement, and can detect decreased stride length and reduced walking speed in early-stage PD patients during turns.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2022)

Article Chemistry, Analytical

Can Gait Features Help in Differentiating Parkinson's Disease Medication States and Severity Levels? A Machine Learning Approach

Chariklia Chatzaki et al.

Summary: This study developed an experimental protocol for evaluating lower extremity motor symptoms in Parkinson's disease (PD) and collected data from PD patients, elderly individuals, and adults using sensor insoles. Eighteen temporal and spatial characteristics were extracted from gait data, and classification tasks were performed using AdaBoost, Extra Trees, and Random Forest classifiers. The results showed high recognition accuracy for distinguishing PD from non-PD groups, PD medication states, and PD severity levels.

SENSORS (2022)

Article Neurosciences

Motor cortex excitability is reduced during freezing of upper limb movement in Parkinson's disease

Marlene Topka et al.

Summary: This study investigated the excitability of the motor cortex in Parkinson's disease patients with freezing symptoms and found that there was a reduction in excitability during freezing and the transition period prior to a freeze. These findings support the critical role of motor cortex excitability in freezing in Parkinson's disease.

NPJ PARKINSONS DISEASE (2022)

Article Computer Science, Artificial Intelligence

Parkinson's Disease Classification and Clinical Score Regression via United Embedding and Sparse Learning From Longitudinal Data

Zhongwei Huang et al.

Summary: In this study, a novel adaptive unsupervised feature selection approach is proposed for early PD diagnosis by exploiting manifold learning from longitudinal multimodal data. The method performs united embedding and sparse regression to determine similarity matrices and discriminative features, with an effective iterative optimization algorithm. Experiments on the PPMI dataset demonstrate superior performance in classification and clinical score regression compared to state-of-the-art approaches.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

Review Neurosciences

A review on pathology, mechanism, and therapy for cerebellum and tremor in Parkinson's disease

Yuke Zhong et al.

Summary: This review summarizes the pathological, structural, and functional changes of the cerebellum in Parkinson's disease and discusses the role of the cerebellum in PD-related tremor.

NPJ PARKINSONS DISEASE (2022)

Article Health Care Sciences & Services

Automated video-based assessment of facial bradykinesia in de-novo Parkinson's disease

Michal Novotny et al.

Summary: Even though hypomimia is a common feature of Parkinson's disease, there is a lack of objective tools to assess it. This study developed an automated video-based assessment tool for hypomimia and found that 57% of newly diagnosed PD patients had hypomimia. The tool was able to accurately distinguish between PD patients and healthy controls, and different facial regions affected by hypomimia were associated with different clinical features.

NPJ DIGITAL MEDICINE (2022)

Article Computer Science, Artificial Intelligence

A tree-structure-guided graph convolutional network with contrastive learning for the assessment of parkinsonian hand movements

Rui Guo et al.

Summary: This paper proposes an automated assessment scheme for bradykinesia in Parkinson's disease using a tree-structure-guided graph convolutional network with contrastive learning. The method achieves accurate evaluation of PD bradykinesia through video analysis, providing a convenient tool for PD telemedicine applications.

MEDICAL IMAGE ANALYSIS (2022)

Article Chemistry, Analytical

Validation of Pressure-Sensing Insoles in Patients with Parkinson's Disease during Overground Walking in Single and Cognitive Dual-Task Conditions

Monica Parati et al.

Summary: This study aimed to examine the validity and reliability of gait parameters computed by pressure-sensing insoles. The results showed good correlations and agreement between the pressure-sensing insoles and traditional tools. Our findings support the use of these insoles as complementary instruments to conventional tools during single and dual-task conditions.

SENSORS (2022)

Article Automation & Control Systems

Multimodal Gait Recognition for Neurodegenerative Diseases

Aite Zhao et al.

Summary: This article proposes a novel hybrid model to learn gait differences between different neurodegenerative diseases, Parkinson's disease severity levels, and healthy individuals and patients through fusion and aggregation of data from multiple sensors. The model utilizes a spatial feature extractor and a new correlative memory neural network architecture to capture temporal information, along with a multiswitch discriminator to associate observations with individual state estimations. Compared to several state-of-the-art techniques, the framework shows more accurate classification results.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

Article Health Care Sciences & Services

Detecting motor symptom fluctuations in Parkinson's disease with generative adversarial networks

Vishwajith Ramesh et al.

Summary: Parkinson's disease is a neurodegenerative disorder characterized by motor symptoms. There is no cure, but levodopa therapy can help alleviate symptoms. Efforts have been made to develop continuous, objective measures of motor symptoms using wearable sensors, aiming for more reliable and continuous tracking of Parkinson's disease symptoms.

NPJ DIGITAL MEDICINE (2022)

Proceedings Paper Computer Science, Artificial Intelligence

Monitoring Neurological Disorder Patients via Deep Learning Based Facial Expressions Analysis

Muhammad Munsif et al.

Summary: In this study, a deep learning-based facial expression recognition framework is developed for automatic classification of neurological disorders patients. By processing facial images, extracting facial features, and performing classification, an accuracy of 93% is achieved. Qualitative and quantitative results on a standard dataset confirm the effectiveness of the proposed model.

ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS. AIAI 2022 IFIP WG 12.5 INTERNATIONAL WORKSHOPS (2022)

Proceedings Paper Computer Science, Information Systems

Deep learning and Blockchain-based Essential and Parkinson Tremor Classification Scheme

Jigna J. Hathaliya et al.

Summary: This paper discusses the characteristics and medical symptoms of essential tremor and Parkinson's tremor, as well as the method of using deep learning to address related issues. The combination of GRU and LSTM algorithms is used to predict tremor severity, and a blockchain network is employed to validate the performance of the model. The proposed model demonstrates excellent performance in both training and testing.

IEEE INFOCOM 2022 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS) (2022)

Review Health Care Sciences & Services

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

Fan Bo et al.

Summary: With the rapid development of IoT technologies, IMU-based systems have played a significant role in disease detection. However, traditional numerical interpretation methods struggle to provide satisfactory accuracy due to low-quality raw data and strong electromagnetic interference. Recent years have seen the proposal of machine learning techniques to map IMU-captured data on disease detection and progress. Through the analysis of 81 articles, it is concluded that ML technology can be crucial in disease diagnosis, severity assessment, characteristic estimation, and rehabilitation monitoring.

HEALTHCARE (2022)

Article Engineering, Biomedical

PD-ResNet for Classification of Parkinson's Disease From Gait

X. I. A. O. L. I. YANG et al.

Summary: We have developed an objective and efficient method based on a novel artificial intelligence model to automatically identify Parkinson's disease (PD) patients and healthy control (HC) groups, as well as classify PD patients with different severity levels. Our model achieves excellent performance with high accuracy, precision, recall, specificity, and F1-score in clinical gait dataset experiments. The proposed method shows better performance compared to traditional machine learning and deep learning methods.

IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE (2022)

Article Computer Science, Information Systems

Early Detection of Parkinson's Disease by Neural Network Models

Chin-Hsien Lin et al.

Summary: This study developed neural network models that can identify Parkinson's disease (PD) and differentiate patients at different severity stages. The models achieved high accuracy in recognizing advanced-stage PD patients and distinguishing early-stage PD patients from normal elderly subjects.

IEEE ACCESS (2022)

Article Engineering, Electrical & Electronic

Gait Spatiotemporal Signal Analysis for Parkinson's Disease Detection and Severity Rating

Abdullah S. Alharthi et al.

Summary: Deep learning models are used to classify PD severity based on spatiotemporal GRF signals, achieving an F1-score of 95.5% with LRP interpretation highlighting significant features. Specific gait elements are identified as indicative for healthy or advanced PD classification, indicating potential for detecting postural balance deterioration and rating PD severity.

IEEE SENSORS JOURNAL (2021)

Article Computer Science, Interdisciplinary Applications

On the use of histograms of oriented gradients for tremor detection from sinusoidal and spiral handwritten drawings of people with Parkinson's disease

Joao Paulo Folador et al.

Summary: This study introduces HOG descriptors and machine learning techniques, as well as a deep learning method to automatically identify tremors in PD patients. Traditional sinusoidal patterns are more suitable for tremor detection than spiral patterns.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2021)

Article Clinical Neurology

Hypomimia in Parkinson's Disease: What Is It Telling Us?

Teresa Maycas-Cepeda et al.

Summary: Amimia is correlated with motor and nonmotor symptoms, cognitive status, depression, and quality of life in PD patients. Blinking frequency is associated with amimia and motor symptoms. Amimia could serve as a useful marker of overall disease severity in PD, including cognitive decline.

FRONTIERS IN NEUROLOGY (2021)

Article Neurosciences

Multimodal phenotypic axes of Parkinson's disease

Ross D. Markello et al.

Summary: Individuals with Parkinson's disease exhibit a complex clinical phenotype, where data fusion can capture inter-dependencies among different modalities, with neuroimaging data playing a critical role.

NPJ PARKINSONS DISEASE (2021)

Article Engineering, Biomedical

Prediction and detection of freezing of gait in Parkinson's disease from plantar pressure data using long short-term memory neural-networks

Gaurav Shalin et al.

Summary: Using plantar pressure data, this study successfully detected and predicted Freezing of Gait (FOG) in patients with Parkinson's disease. The results suggest the potential of using this method for FOG detection and prediction, while further research is needed to improve prediction performance by training with a larger sample of individuals experiencing FOG.

JOURNAL OF NEUROENGINEERING AND REHABILITATION (2021)

Article Health Care Sciences & Services

Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study

Avner Abrami et al.

Summary: The study demonstrates that computer vision techniques can effectively detect hypomimia in Parkinson's disease patients, classify drug states, and play an important role in telemedicine.

JOURNAL OF MEDICAL INTERNET RESEARCH (2021)

Article Multidisciplinary Sciences

An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters

Tiwana Varrecchia et al.

Summary: The study developed an automated diagnostic algorithm based on machine-learning techniques to classify gait deficits of PwPD and identified a minimum set of gait classifiers. Through computerized gait analysis and principal component analysis, specific feature combinations were identified to distinguish PwPD from healthy controls and characterize gait patterns between different H-Y stages.

PLOS ONE (2021)

Review Chemistry, Analytical

Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021)

Hui Wen Loh et al.

Summary: Parkinson's disease is the second most common neurodegenerative disorder globally, with no curative treatments currently available. Early diagnosis is crucial for maintaining patient self-sufficiency, but is often delayed due to various factors. Computer-aided diagnostic tools for automated PD diagnosis have potential, but face limitations in application.

SENSORS (2021)

Review Engineering, Biomedical

Advances in Parkinson's Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects

Laureano Moro-Velazquez et al.

Summary: Parkinson's Disease affects speech through dysphonia and hypokinetic dysarthria. Recent studies focus on developing new automatic tools for diagnosis and assessment. However, there is currently no standard methodology validated in clinical trials, and further research is needed.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

Article Biophysics

Discrimination of idiopathic Parkinson's disease and vascular parkinsonism based on gait time series and the levodopa effect

Carlos Fernandes et al.

Summary: A new approach using convolutional neural networks was proposed to differentiate between idiopathic Parkinson's disease and vascular parkinsonism based on gait assessment and response to levodopa medication, achieving high accuracy rates. The study highlights the potential of wearable sensors and machine learning techniques in distinguishing between different types of parkinsonian syndromes.

JOURNAL OF BIOMECHANICS (2021)

Review Clinical Neurology

Challenges in the diagnosis of Parkinson's disease

Eduardo Tolosa et al.

Summary: Parkinson's disease is the second most common neurodegenerative disease, with efforts being made to accurately diagnose and characterize it, including validation of diagnostic criteria and identification of genetic subtypes. Progress in diagnostic biomarkers has opened up possibilities for earlier identification, recognition of diverse subtypes, and development of novel treatments.

LANCET NEUROLOGY (2021)

Article Neurosciences

A real-world study of wearable sensors in Parkinson's disease

Jamie L. Adams et al.

Summary: Most wearable sensor studies on Parkinson's disease are conducted in clinical settings, but this study observed activity, gait, and tremor using sensors both inside and outside the clinic. Results showed that individuals with Parkinson's disease walked significantly less and had higher tremor duration compared to age-matched controls, providing valuable insights into the real-world manifestations of Parkinson's disease.

NPJ PARKINSONS DISEASE (2021)

Article Chemistry, Analytical

The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson's Patients

Chariklia Chatzaki et al.

Summary: Gait analysis is crucial for detecting and managing various neurological and musculoskeletal disorders. The Smart-Insole Dataset is introduced for the development and evaluation of computational methods focusing on gait analysis. The dataset includes data from pressure sensor insoles and has been used to discriminate between different groups and verify assumptions regarding gait characteristics in elderly and Parkinson's disease patients.

SENSORS (2021)

Article Multidisciplinary Sciences

A deep explainable artificial intelligent framework for neurological disorders discrimination

Soroosh Shahtalebi et al.

Summary: Pathological hand tremor (PHT) is a common symptom of Parkinson's disease (PD) and essential tremor (ET), affecting manual targeting and motor coordination. The NeurDNet model, trained on hand motion signals from patients, shows exceptional accuracy in differentiating between PD and ET, providing clinically viable insights on patient classification. The overlap in symptoms between PD and ET requires expert knowledge and specialized diagnostic methods for accurate diagnosis and treatment.

SCIENTIFIC REPORTS (2021)

Review Geriatrics & Gerontology

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Jie Mei et al.

Summary: Diagnosis of Parkinson's disease relies on medical observations and clinical signs, but traditional methods can be subjective. Machine learning approaches have shown potential in improving diagnosis and informing clinical decision making.

FRONTIERS IN AGING NEUROSCIENCE (2021)

Article Multidisciplinary Sciences

Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers

Luay Fraiwan et al.

Summary: This study proposed a computer-aided framework to identify gait fluctuations associated with degenerative neuromuscular diseases and health conditions, utilizing statistical and classification comparisons, as well as various ensemble methods. Experimental results demonstrated the effectiveness of this approach in achieving high accuracy and sensitivity.

PLOS ONE (2021)

Article Chemistry, Analytical

Atypical Gait Cycles in Parkinson's Disease

Marco Ghislieri et al.

Summary: Finding objective biomarkers to evaluate gait in Parkinson's Disease (PD) is crucial. One study utilized foot-switch signals analyzed through Statistical Gait Analysis (SGA) to identify atypical gait cycles in PD patients, characterized by a forefoot strike instead of a normal heel strike. The increase in atypical cycles was significantly correlated with the motor clinical score UPDRS-III, highlighting the importance of these cycles as a valid biomarker for subtle gait dysfunctions in PD patients.

SENSORS (2021)

Article Clinical Neurology

Probing the Pre-diagnostic Phase of Parkinson's Disease in Population-Based Studies

Lisanne J. Dommershuijsen et al.

Summary: Parkinson's disease presents a wide range of symptoms, with increasing recognition of its heterogeneity. Population-based studies offer specific advantages in overcoming biases and broadening data collection, providing insights into the pre-diagnostic phase of the disease. Bridging study designs will be crucial in making vital advances in understanding the heterogeneity of pre-diagnostic Parkinson's disease.

FRONTIERS IN NEUROLOGY (2021)

Review Neurosciences

A systematic review on exercise and training-based interventions for freezing of gait in Parkinson's disease

Moran Gilat et al.

Summary: The analysis found that exercise and training can be beneficial in reducing freezing of gait (FOG) in Parkinson's disease patients, with dedicated training aimed at reducing FOG episodes or ameliorating the underlying correlates of FOG showing moderate effectiveness. Generic exercises, however, were not as effective in reducing FOG severity.

NPJ PARKINSONS DISEASE (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Detecting Freezing of Gait in Parkinson's Disease Patient via Deep Residual Network

Runfeng Miao et al.

Summary: A deep residual network was proposed to detect Freezing of Gait (FoG), showing superior performance compared to traditional methods and deep learning techniques in offline analysis. The method achieved high sensitivity and specificity, helping improve the quality of life for Parkinson's disease patients and evaluate symptoms of FoG.

20TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2021) (2021)

Proceedings Paper Engineering, Electrical & Electronic

Freezing of Gait Detection Using Discrete Wavelet Transform and Hybrid Deep Learning Architecture

Nguyen Thi Hoai Thu et al.

Summary: The study proposes a framework for Freezing of gait (FoG) detection using a combination of hand-crafted features and hybrid deep learning algorithms. The input features are extracted from raw sensor signals using a multi-level discrete wavelet transform (DWT) and then processed by a hybrid architecture of convolutional neural network (CNN) and bidirectional long short-term memory network. Performance comparisons are conducted on different input data types and machine learning methods using the Daphnet public dataset.

12TH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS (ICUFN 2021) (2021)

Article Computer Science, Information Systems

Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson's Disease Diagnosis

Fatih Demir et al.

Summary: A novel approach for Parkinson's disease diagnosis based on speech disorders was developed, which achieved high classification accuracy without feature selection. The deep LSTM network performed both feature extraction and classification processes in an end-to-end manner, resulting in better performance compared to existing methods.

IEEE ACCESS (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Improving Parkinson Detection using Dynamic Features from Evoked Expressions in Video

Luis F. Gomez et al.

Summary: This study explores the use of static and dynamic features for analyzing facial gestures in PD patients, proposing a multimodal PD detection system. Results show that dynamic features can improve PD detection accuracy and there are differences in the performance of evoked face gestures in this PD detection task.

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021 (2021)

Article Engineering, Electrical & Electronic

CNN-Based PD Hand Tremor Detection Using Inertial Sensors

Lina Tong et al.

Summary: A convolutional neural network-based method for detecting Parkinson's disease hand tremors is proposed, which shows effective performance through experiments and validation.

IEEE SENSORS LETTERS (2021)

Review Engineering, Electrical & Electronic

Advances in healthcare wearable devices

Sheikh M. A. Iqbal et al.

Summary: Wearable devices have various applications in healthcare, from monitoring physiological diseases to serving as drug delivery systems. Despite facing challenges, they hold promise in becoming personalized healthcare systems.

NPJ FLEXIBLE ELECTRONICS (2021)

Article Engineering, Biomedical

Video Based Shuffling Step Detection for Parkinsonian Patients Using 3D Convolution

Xugang Cao et al.

Summary: This study proposes a purely video-based method for automatically detecting the shuffling step of Parkinson's Disease patients with an average accuracy of 90.8% and assessing the severity of walking abnormalities, leading to more frequent and accurate monitoring of the patients' condition at lower costs.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2021)

Letter Clinical Neurology

Association Between Hypomimia and Mild Cognitive Impairment in De Novo Parkinson's Disease Patients

Carmen Gasca-Salas et al.

CANADIAN JOURNAL OF NEUROLOGICAL SCIENCES (2020)

Review Biochemistry & Molecular Biology

How Machine Learning Will Transform Biomedicine

Jeremy Goecks et al.

Article Health Care Sciences & Services

Diagnosing Parkinson Disease Through Facial Expression Recognition: Video Analysis

Bo Jin et al.

JOURNAL OF MEDICAL INTERNET RESEARCH (2020)

Article Clinical Neurology

Hypomimia in Parkinson's disease: an axial sign responsive to levodopa

L. Ricciardi et al.

EUROPEAN JOURNAL OF NEUROLOGY (2020)

Article Engineering, Electrical & Electronic

Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging

Syed Aziz Shah et al.

IEEE SENSORS JOURNAL (2020)

Article Computer Science, Artificial Intelligence

Detection of Parkinson's disease from handwriting using deep learning: a comparative study

Catherine Taleb et al.

EVOLUTIONARY INTELLIGENCE (2020)

Article Computer Science, Information Systems

A Spectrogram-Based Deep Feature Assisted Computer-Aided Diagnostic System for Parkinsons Disease

Laiba Zahid et al.

IEEE ACCESS (2020)

Article Computer Science, Artificial Intelligence

Graph Sequence Recurrent Neural Network for Vision-Based Freezing of Gait Detection

Kun Hu et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

Review Computer Science, Information Systems

A State-of-the-Art Survey on Deep Learning Theory and Architectures

Md Zahangir Alom et al.

ELECTRONICS (2019)

Article Engineering, Biomedical

Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data

John Prince et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2019)

Review Clinical Neurology

Gait impairments in Parkinson's disease

Anat Mirelman et al.

LANCET NEUROLOGY (2019)

Article Acoustics

Acoustical Assessment of Voice Disorder With Continuous Speech Using ASR Posterior Features

Yuanyuan Liu et al.

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING (2019)

Article Chemistry, Analytical

Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements

Murtadha D. Hssayeni et al.

SENSORS (2019)

Article Computer Science, Information Systems

Multimodal Assessment of Parkinson's Disease: A Deep Learning Approach

Juan Camilo Vasquez-Correa et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2019)

Article Computer Science, Artificial Intelligence

Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait

Imanne El Maachi et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)

Proceedings Paper Engineering, Biomedical

Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection

Alexandros Papadopoulos et al.

2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) (2019)

Article Computer Science, Information Systems

Deep Learning-Based Parkinson's Disease Classification Using Vocal Feature Sets

Hakan Gunduz

IEEE ACCESS (2019)

Article Clinical Neurology

The functional network signature of heterogeneity in freezing of gait

Kaylena A. Ehgoetz Martens et al.

BRAIN (2018)

Article Mathematical & Computational Biology

Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling

Ritesh A. Ramdhani et al.

Frontiers in Computational Neuroscience (2018)

Article Computer Science, Information Systems

Gait Anomaly Detection of Subjects With Parkinson's Disease Using a Deep Time Series-Based Approach

Giovanni Paragliola et al.

IEEE ACCESS (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Predicting Freezing of Gait in Parkinsons Disease Patients using Machine Learning

Natasa K. Orphanidou et al.

2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) (2018)

Article Clinical Neurology

Gait analysis and clinical correlations in early Parkinson's disease

Michele Pistacchi et al.

FUNCTIONAL NEUROLOGY (2017)

Article Engineering, Biomedical

Classification and Prediction of Clinical Improvement in Deep Brain Stimulation From Intraoperative Microelectrode Recordings

Kyriaki Kostoglou et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2017)

Article Engineering, Biomedical

Gait Rhythm Fluctuation Analysis for Neurodegenerative Diseases by Empirical Mode Decomposition

Peng Ren et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2017)

Article Clinical Neurology

A Case of Apparent Upper-Body Freezing in Parkinsonism while Using a Wheelchair

Samuel T. Nemanich et al.

FRONTIERS IN NEUROLOGY (2017)

Review Chemistry, Analytical

Gait Partitioning Methods: A Systematic Review

Juri Taborri et al.

SENSORS (2016)

Review Clinical Neurology

MDS clinical diagnostic criteria for Parkinson's disease

Ronald B. Postuma et al.

MOVEMENT DISORDERS (2015)

Review Clinical Neurology

Technical and clinical view on ambulatory assessment in Parkinson's disease

M. A. Hobert et al.

ACTA NEUROLOGICA SCANDINAVICA (2014)

Review Computer Science, Information Systems

A systematic review of systematic review process research in software engineering

Barbara Kitchenham et al.

INFORMATION AND SOFTWARE TECHNOLOGY (2013)

Review Clinical Neurology

Clinical Syndromes: Parkinsonian Gait

Georg Ebersbach et al.

MOVEMENT DISORDERS (2013)

Article Neurosciences

Impact of Falls and Fear of Falling on Health-Related Quality of Life in Patients with Parkinson's Disease

Yvette A. M. Grimbergen et al.

JOURNAL OF PARKINSONS DISEASE (2013)

Article Clinical Neurology

A study of subtle motor signs in early Parkinson's disease

Susanne A. Schneider et al.

MOVEMENT DISORDERS (2012)

Article Biochemical Research Methods

Ambulatory monitoring of freezing of gait in Parkinson's disease

Steven T. Moore et al.

JOURNAL OF NEUROSCIENCE METHODS (2008)

Review Medicine, General & Internal

Editorial peer review for improving the quality of reports of biomedical studies

T. Jefferson et al.

COCHRANE DATABASE OF SYSTEMATIC REVIEWS (2007)