Accurate telemonitoring of Parkinson's disease diagnosis using robust inference system

Mandal, Indrajit; Sairam, N.

doi:10.1016/j.ijmedinf.2012.10.006

Cited by 42 publications

(22 citation statements)

References 75 publications

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“…To address this shift in population structure, new Public Health strategies for promoting pro-active ageing could be of great societal value [6,7]. In all fields of society, especially in the health and geriatric care sector, ICT-enabled innovations designed for consumers with special needs offer a huge opportunity to face the challenges of ageing societies [8][9][10]. As a first step, ICT-based assistance in everyday life provides the key to consumer's independency and self care [9].…”

Section: Introductionmentioning

confidence: 99%

ICT and the future of health care: aspects of health promotion

Haluza

Jungwirth

2015

International Journal of Medical Informatics

128

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Section: Introductionmentioning

confidence: 99%

ICT and the future of health care: aspects of health promotion

Haluza

Jungwirth

2015

International Journal of Medical Informatics

128

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“…Moreover, other researchers have interested in investigating these data to classify the regression of the illness. In that, [19] reproduces the results from [17] and [18] and compares the two methods (SVM and ANN) with AdaBoost model by using binary classification data set. It argues that the last one has a better performance in processing Parkinson's disease, especially in the regressing tasks.…”

Section: B Related Workmentioning

confidence: 86%

“…It has been used in diagnosing of the Parkinson's disease and examining the people to decide their health status [16]. [17] and [18] compared the results of the SVM with the performance of the NN in handling this issue. [17] found out that the two methods (SVM and NN) have the same classification accuracy (94.8%) while [18] highlights that the SVM can work more efficiency and better accuracy (93%) comparing with NN (86%).…”

Section: B Related Workmentioning

confidence: 99%

“…[17] and [18] compared the results of the SVM with the performance of the NN in handling this issue. [17] found out that the two methods (SVM and NN) have the same classification accuracy (94.8%) while [18] highlights that the SVM can work more efficiency and better accuracy (93%) comparing with NN (86%). Moreover, other researchers have interested in investigating these data to classify the regression of the illness.…”

Section: B Related Workmentioning

confidence: 99%

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Efficient diagnosis system for Parkinson's disease using deep belief network

Al-Fatlawi

Jabardi

Ling

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-In this paper, a deep belief network (DBN) has been adopted as an efficient technique to diagnosis the Parkinson's disease (PD). This diagnosis has been established based on the speech signal of the patients. Through the distinguishing and analyzing of the speech signal, the DBN has the ability to diagnose Parkinson's disease. To realize the diagnosis of Parkinson's disease by using DBN, the proposed system has been trained and tested with voices from a number of patients and healthy people. A feature extraction process has been prepared to be inputted to the deep belief network (DBN) which is used to create a template matching of the voices. In this paper, DBN is used to classify the Parkinson's disease which composes two stacked Restricted Boltzmann Machines (RBMs) and one output layer. Two stages of learning need to be applied to optimize the networks' parameters. The first stage is unsupervised learning which uses RBMs to overcome the problem that can cause because of the random value of the initial weights. Secondly, backpropagation algorithm is used as a supervised learning for the fine tuning. To illustrate the effectiveness of the proposed system, the experimental results are compared with different approaches and related works. The overall testing accuracy of the proposed system is 94% which is better than all of the compared methods. In short, the DBN is an effective way to diagnosis Parkinson's disease by using the speech signal.

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“…There are studies which use these features to classify subjects as early PD and healthy normal [11,[14][15][16][17][18][19][20][21][22]. [14] performed smell identification tests using culturally adapted translations of the University of Pennsylvania Smell Identification Test (UPSIT) and Sniffin' Sticks (SS) test in 106 PD and 118 normal subjects.…”

Section: Neuroimaging Markersmentioning

confidence: 99%