A computerized method to assess Parkinson’s disease severity from gait variability based on gender

Cantürk, İsmail

doi:10.1016/j.bspc.2021.102497

Cited by 8 publications

(3 citation statements)

References 33 publications

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“…Many remote detecting tests were utilized to detect the PD severity and realized that variables in gait signals could easily distinguish PD patients from healthy ones. In this regard, Cantürk et al [ 26 ] proposed a system to detect PD patients’ severity using gait signals. Their system was trained and tested with 306 publicly available signals with 93 PD patients and 73 healthy subjects based on different categories.…”

Section: Severity Identification Of Parkinson’s Diseasementioning

confidence: 99%

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

et al. 2023

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Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease. The problem of disease severity identification can be considered multi-class classification, where the class labels are the severity levels of the disease. Plenty of computational intelligence-based solutions have been presented by researchers for severity identification. This paper presents a comprehensive review of recent approaches for identifying disease severity levels using computational intelligence-based approaches. We followed the PRISMA guidelines and compiled several works related to the severity identification of multidisciplinary diseases of the last decade from well-known publishers, such as MDPI, Springer, IEEE, Elsevier, etc. This article is devoted toward the severity identification of two main diseases, viz. Parkinson’s Disease and Diabetic Retinopathy. However, severity identification of a few other diseases, such as COVID-19, autonomic nervous system dysfunction, tuberculosis, sepsis, sleep apnea, psychosis, traumatic brain injury, breast cancer, knee osteoarthritis, and Alzheimer’s disease, was also briefly covered. Each work has been carefully examined against its methodology, dataset used, and the type of disease on several performance metrics, accuracy, specificity, etc. In addition to this, we also presented a few public repositories that can be utilized to conduct research on disease severity identification. We hope that this review not only acts as a compendium but also provides insights to the researchers working on disease severity identification using computational intelligence-based approaches.

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Section: Severity Identification Of Parkinson’s Diseasementioning

confidence: 99%

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

et al. 2023

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“…Dopaminergic therapeutic response was determined using this score, which captured day-to-day symptom changes and was well associated with existing standard rating scales. The gait cycle, which can be broken down into several stages and periods to assess normative and aberrant gait [ 9 ], has been presented as a unique way to diagnose PD utilising the gait analysis. It was first necessary to reduce noise caused by variations in the subject's body's alignment during measurement by employing a Chebyshev type II high pass filter with a cutoff frequency of 0.8 Hz.…”

Section: Related Workmentioning

confidence: 99%

Severity Prediction over Parkinson’s Disease Prediction by Using the Deep Brooke Inception Net Classifier

Sarankumar¹,

Vinod

Anitha

et al. 2022

Computational Intelligence and Neuroscience

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Parkinson’s disease (PD) is a neurodegenerative illness that progresses and is long-lasting. It becomes more difficult to talk, write, walk, and do other basic functions when the brain’s dopamine-generating neurons are injured or killed. There is a gradual rise in the intensity of these symptoms over time. Using Parkinson’s Telemonitoring Voice Data Set from UCI and deep neural networks, we provide a strategy for predicting the severity of Parkinson’s disease in this research. An unprocessed speech recording contains a slew of unintelligible data that makes correct diagnosis difficult. Therefore, the raw signal data must be preprocessed using the signal error drop standardization while the features can be grouped by using the wavelet cleft fuzzy algorithm. Then the abnormal features can be selected by using the firming bacteria foraging algorithm for feature size decomposition process. Then classification was made using the deep brooke inception net classifier. The performances of the classifier are compared where the simulation results show that the proposed strategy accuracy in detecting severity of the Parkinson’s disease is better than other conventional methods. The proposed DBIN model achieved better accuracy compared to other existing techniques. It is also found that the classification based on extracted voice abnormality data achieves better accuracy (99.8%) over PD prediction; hence it can be concluded as a better metric for severity prediction.

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“…PhysioNet is a widely-used dataset that collects gait signals via 16 pressure sensors placed under each foot. Many researchers [3][4][5][6][7][8] have studied this dataset with promising results. For gait feature detection, Refs.…”

Section: Introductionmentioning

confidence: 99%

Twin-tower transformer network for skeleton-based Parkinson’s disease early detection

Ma,

Huo,

Liu

et al. 2024

Complex Intell. Syst.

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Parkinson’s disease is a chronic neurodegenerative condition accompanied by a variety of motor and non-motor clinical symptoms. Diagnosing Parkinson’s disease presents many challenges, such as excessive reliance on subjective scale scores and a lack of objective indicators in the diagnostic process. Developing efficient and convenient methods to assist doctors in diagnosing Parkinson’s disease is necessary. In this paper, we study the skeleton sequences obtained from gait videos of Parkinsonian patients for early detection of the disease. We designed a Transformer network based on feature tensor fusion to capture the subtle manifestations of Parkinson’s disease. Initially, we fully utilized the distance information between joints, converting it into a multivariate time series classification task. We then built twin towers to discover dependencies within and across sequence channels. Finally, a tensor fusion layer was employed to integrate the features from both towers. In our experiments, our model demonstrated superior performance over the current state-of-the-art algorithm, achieving an 86.8% accuracy in distinguishing Parkinsonian patients from healthy individuals using the PD-Walk dataset.

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A computerized method to assess Parkinson’s disease severity from gait variability based on gender

Cited by 8 publications

References 33 publications

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains

Severity Prediction over Parkinson’s Disease Prediction by Using the Deep Brooke Inception Net Classifier

Twin-tower transformer network for skeleton-based Parkinson’s disease early detection

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