Parkinsons disease classification using wavelet transform based feature extraction of gait data

Baby, Mariya; Saji, A. J.; Kumar, C. Sathish

doi:10.1109/iccpct.2017.8074230

Cited by 35 publications

(20 citation statements)

References 10 publications

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“…Wearable devices have also been developed to incorporate Biopotential sensors like EMG used to record muscle activity data [39], [45], [82]–[85]. Other sensors like insole force or pressure sensors have been used to evaluate the vertical ground reaction force generated when the subject is walking to assess their gait, balance, or posture [86]–[92]. Figure 11(b) shows that the number of papers published using wearable devices has increased significantly in the last 11 years and is expected to keep growing.…”

Section: Resultsmentioning

confidence: 99%

Trends in Technology Usage for Parkinson’s Disease Assessment: A Systematic Review

Deb

Bhat

et al. 2021

Preprint

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Parkinson's disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified further due to its dependency on patient diaries and the neurologist's subjective assessment of clinical scales. This challenge can be addressed by the advances in mobile technology, which can enable objective, accurate, and continuous patient monitoring. Indeed, a significant amount of recent work explores new cost-effective and subjective assessment methods of PD symptoms. For example, smart technologies, such as wearable sensors, have been used to analyze a PD patients' symptoms to assess their disease progression and even to detect signs in their nascent stage for early diagnosis of PD. This review focuses on the use of modern wearable and mobile equipment for PD applications in the last decade. Four significant fields of research were identified: Assistance to Diagnosis, Prognosis or Monitoring of Symptoms and their Severity, Predicting Response to Treatment, and Assistance to Therapy or Rehabilitation. This study starts with 31,940 articles published between January 2008 and December 2019 in the following four databases: Pubmed Central, Science Direct, IEEE Xplore and MDPI. A total of 976 papers are manually investigated and included in this review after removing unrelated articles, duplicate entries, publications in languages other than English, and other articles that did not fulfill the selection criteria. Our analysis shows that the numbers of published papers every year has increased at a constant rate from 2008 to 2015, while the rate of increase has significantly grown from 2016 to 2019. Majority of the papers (62%) were published in the last four years, and 21% papers in just 2019. In terms of the symptoms, gait and tremor are two major ones that researchers have focused on. The trend shows the growing interest in assessing Parkinson's Disease with wearable devices in the last decade, particularly in the last 4 years. Our automated script makes the review easily reproducible for publications published in the future.

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Section: Resultsmentioning

confidence: 99%

Trends in Technology Usage for Parkinson’s Disease Assessment: A Systematic Review

Deb

Bhat

et al. 2021

Preprint

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“…Experiments involving the use of different transforms on images for feature extraction have also been attempted across various domains. These include the use of Radon transforms (Jadhav and Holambe, 2009), wavelet transforms (Baby et al, 2017) (Redhouane et al, 2014) and discrete wavelet transforms (Hamad et al, 2016). These attempts show that there is a need of devising optimal size features and efficient feature extraction methods for better performance of the machine learning algorithms, while also ensuring robustness against variations in scaling and illumination.…”

Section: Introductionmentioning

confidence: 99%

Haar Wavelet Pyramid-Based Melanoma Skin Cancer Identification With Ensemble of Machine Learning Algorithms

Thepade

Ramnani

2021

International Journal of Healthcare Information Systems and Informatics

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Melanoma is a mortal type of skin cancer. Early detection of melanoma significantly improves the patient’s chances of survival. Detection of melanoma at an early juncture demands expert doctors. The scarcity of such expert doctors is a major issue with healthcare systems globally. Computer-assisted diagnostics may prove helpful in this case. This paper proposes a health informatics system for melanoma identification using machine learning with dermoscopy skin images. In the proposed method, the features of dermoscopy skin images are extracted using the Haar wavelet pyramid various levels. These features are employed to train machine learning algorithms and ensembles for melanoma identification. The consideration of higher levels of Haar Wavelet Pyramid helps speed up the identification process. It is observed that the performance gradually improves from the Haar wavelet pyramid level 4x4 to 16x16, and shows marginal improvement further. The ensembles of machine learning algorithms have shown a boost in performance metrics compared to the use of individual machine learning algorithms.

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“…Various ML algorithms such as support vector regression, step-down linear regression, gradient boosted regression tree, negative binomial regression, least absolute shrinkage and selection operator linear regression model and generalised additive model were employed to forecast dengue incidence in China [19]. Furthermore, MLP, SVM and NB were employed to classify Parkinson's disease-afflicted patients and healthy persons [20, 21]. Patil [22] proposed a hybrid prediction model for the prediction of type-2 diabetes in patients.…”

Section: Introductionmentioning

confidence: 99%

Applications of machine learning techniques to predict filariasis using socio-economic factors

et al. 2019

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Filariasis is one of the major public health concerns in India. Approximately 600 million people spread across 250 districts of India are at risk of filariasis. To predict this disease, a pilot scale study was carried out in 30 villages of Karimnagar district of Telangana from 2004 to 2007 to collect epidemiological and socio-economic data. The collected data are analysed by employing various machine learning techniques such as Naïve Bayes (NB), logistic model tree, probabilistic neural network, J48 (C4.5), classification and regression tree, JRip and gradient boosting machine. The performances of these algorithms are reported using sensitivity, specificity, accuracy and area under ROC curve (AUC). Among all employed classification methods, NB yielded the best AUC of 64% and was equally statistically significant with the rest of the classifiers. Similarly, the J48 algorithm generated 23 decision rules that help in developing an early warning system to implement better prevention and control efforts in the management of filariasis.

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Parkinsons disease classification using wavelet transform based feature extraction of gait data

Cited by 35 publications

References 10 publications

Trends in Technology Usage for Parkinson’s Disease Assessment: A Systematic Review

Trends in Technology Usage for Parkinson’s Disease Assessment: A Systematic Review

Haar Wavelet Pyramid-Based Melanoma Skin Cancer Identification With Ensemble of Machine Learning Algorithms

Applications of machine learning techniques to predict filariasis using socio-economic factors

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