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

Oğul, Burçin Buket; Özdemir, Suat

doi:10.1109/access.2021.3136724

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…Doctors often use a medical history and neurological examination to diagnose. These features are static and dynamic speech qualities relevant to PD identification 11 .…”

Section: Introductionmentioning

confidence: 99%

Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s disease

Lilhore,

Dalal,

Faujdar

et al. 2023

Sci Rep

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The patients’ vocal Parkinson’s disease (PD) changes could be identified early on, allowing for management before physically incapacitating symptoms appear. In this work, static as well as dynamic speech characteristics that are relevant to PD identification are examined. Speech changes or communication issues are among the challenges that Parkinson’s individuals may encounter. As a result, avoiding the potential consequences of speech difficulties brought on by the condition depends on getting the appropriate diagnosis early. PD patients’ speech signals change significantly from those of healthy individuals. This research presents a hybrid model utilizing improved speech signals with dynamic feature breakdown using CNN and LSTM. The proposed hybrid model employs a new, pre-trained CNN with LSTM to recognize PD in linguistic features utilizing Mel-spectrograms derived from normalized voice signal and dynamic mode decomposition. The proposed Hybrid model works in various phases, which include Noise removal, extraction of Mel-spectrograms, feature extraction using pre-trained CNN model ResNet-50, and the final stage is applied for classification. An experimental analysis was performed using the PC-GITA disease dataset. The proposed hybrid model is compared with traditional NN and well-known machine learning-based CART and SVM & XGBoost models. The accuracy level achieved in Neural Network, CART, SVM, and XGBoost models is 72.69%, 84.21%, 73.51%, and 90.81%. The results show that under these four machine approaches of tenfold cross-validation and dataset splitting without samples overlapping one individual, the proposed hybrid model achieves an accuracy of 93.51%, significantly outperforming traditional ML models utilizing static features in detecting Parkinson’s disease.

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“…Doctors often use a medical history and neurological examination to diagnose. These features are static and dynamic speech qualities relevant to PD identification 11 .…”

Section: Introductionmentioning

confidence: 99%

Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s disease

Lilhore,

Dalal,

Faujdar

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Furthermore, researchers in [77] performed pairwise analysis of gait data and introduced a novel method for assessing the relative severity level of PD patients based on the scores of UPDRS scale (normal, mild, and moderate severity level). In order to achieve this objective, a novel deep learning architecture for pairwise rating of multivariate time-series data acquired by Ground Reaction Force (GRF) sensors worn on the foot was developed.…”

Section: A Advanced DL Methods For Pd Diagnosis and Severity Assessme...mentioning

confidence: 99%

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

Skaramagkas

Pentari

Kefalopoulou

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Parkinson's Disease (PD) is among the most frequent neurological disorders. Approaches that employ artificial intelligence and notably deep learning, have been extensively embraced with promising outcomes. This study dispenses an exhaustive review between 2016 and January 2023 on deep learning techniques used in the prognosis and evolution of symptoms and characteristics of the disease based on gait, upper limb movement, speech and facial expression-related information as well as the fusion of more than one of the aforementioned modalities. The search resulted in the selection of 87 original research publications, of which we have summarized the relevant information regarding the utilized learning and development process, demographic information, primary outcomes, and sensory equipment related information. Various deep learning algorithms and frameworks have attained state-of-theart performance in many PD-related tasks by outperforming conventional machine learning approaches, according to the research reviewed. In the meanwhile, we identify significant drawbacks in the existing research, including a lack of data availability and interpretability of models. The fast advancements in deep learning and the rise in accessible data provide the opportunity to address these difficulties in the near future and for the broad application of this technology in clinical settings.

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“…Using a pairwise deep ranking algorithm, Oğul and Özdemir developed a relative evaluation of PD patients based on gait signals [30]. This model's data came from two PD patients who used information from many ground response force sensors.…”

Section: Survey On Deep Learning Model For Pd Severity Detectionmentioning

confidence: 99%

A Comprehensive Review of Deep Learning Techniques for Identifying Parkinson's Disease from Gait Analysis

SasiRekha

2024

ijarcs

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A group of neurological disorders that manifests in a wide range of motor and cognitive symptoms, Parkinson's Disease (PD) ranks second among those affecting older adults. Many people mistakenly believe that PD symptoms are caused by other conditions, such as essential tremor or natural aging. There may not be a cure for PD, but there are numerous medications that can help with symptoms. Since gait impairment is a prominent and early sign of PD in a clinical context, doctors typically use visual observations to analyse gait abnormalities as one of many manifestations to determine the severity of PD. Nevertheless, therapists' reliance on their own knowledge and judgment makes gait examination a difficult and subjective process. Extracting useful gait features from various input signals for gait analysis has recently been used Deep Learning (DL) models. Intermediate Medical Unit (IMU) patients with movement problems may benefit from a quick and clinically relevant evaluation of gait abnormalities performed automatically utilizing DL algorithms. Using gait analysis, this study provides a comprehensive overview of various DL frameworks that have been created for the purpose of PD prediction and categorization. At the outset, In the first part of the study, various PD prediction and classification systems that have been developed by various researchers and that rely on DL algorithms via gait analysis are reviewed briefly. To find a better way to identify and categorize the PD, we next undertake a comparative analysis to learn about the shortcomings of those algorithms.

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

Cited by 8 publications

References 32 publications

Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s disease

Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson’s disease

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

A Comprehensive Review of Deep Learning Techniques for Identifying Parkinson's Disease from Gait Analysis

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