Home-Based Monitoring and Assessment of Parkinson's Disease

Isenkul

IEEE J. Biomed. Health Inform.

et al. 2013

532

301

There has been an increased interest in speech pattern analysis applications of Parkinsonism for building predictive telediagnosis and telemonitoring models. For this purpose, we have collected a wide variety of voice samples, including sustained vowels, words, and sentences compiled from a set of speaking exercises for people with Parkinson's disease. There are two main issues in learning from such a dataset that consists of multiple speech recordings per subject: 1) How predictive these various types, e.g., sustained vowels versus words, of voice samples are in Parkinson's disease (PD) diagnosis? 2) How well the central tendency and dispersion metrics serve as representatives of all sample recordings of a subject? In this paper, investigating our Parkinson dataset using well-known machine learning tools, as reported in the literature, sustained vowels are found to carry more PD-discriminative information. We have also found that rather than using each voice recording of each subject as an independent data sample, representing the samples of a subject with central tendency and dispersion metrics improves generalization of the predictive model.

mentioning

confidence: 99%

Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings

Isenkul

IEEE J. Biomed. Health Inform.

et al. 2013

532

301

2016 IEEE Congress on Evolutionary Computation (CEC)

“…In North America, for example, there are about one million people are suffering from PD [3]. There are four primary symptoms of PD which are bradykinesia (slowness of movement), rigidity, tremor, and poor balance [4], [5], [6], [7]. Thus, it is classified within the motor system disorders.…”

Section: A the Parkinson's Diseasementioning

confidence: 99%

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

Al-Fatlawi

Jabardi

Ling

2016

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.

IEEE Trans. Inform. Technol. Biomed.

“…This study identified lower mean peak velocities in users with PD. Cunningham et al [21], [22] developed a similar tool incorporating a point and click exercise to be completed using the mouse, and found significant differences in cursor movement speeds and task completion times not only between PD and non-PD users, but also between PD users who were in "on" and "off" medication states. However, significant differences in median completion times between experienced and inexperienced computer users with PD suggest that a more intuitive input device may be advantageous for such assessments.…”

Section: Related Researchmentioning

confidence: 99%

WiiPD—Objective Home Assessment of Parkinson's Disease Using the Nintendo Wii Remote

Synnott

Chen

Nugent

et al. 2012

Current clinical methods for the assessment of Parkinson's disease suffer from inconvenience, infrequency and subjectivity. WiiPD is an approach for the objective home based assessment of Parkinson's disease which utilizes the intuitive and sensor rich Nintendo Wii Remote. Combined with an electronic patient diary, a suite of mini-games, a metric analyzer, and a visualization engine, we propose that this system can complement existing clinical practice by providing objective metrics gathered frequently over extended periods of time. In this paper we detail the approach and introduce a series of metrics deemed capable of quantifying the severity of tremor and bradykinesia in those with Parkinson's disease. The system has been tested on a 71 year old participant with Parkinson's disease over a period of 15 days, a 72 year old control user without Parkinson's disease, and a group of 8 young adults. Results indicate a clear correlation between patient self rating scores of tremor severity and metric values obtained, in addition to clear differences in metrics obtained from each user group. These results suggest that this approach is capable of indicating the presence and severity of the motor symptoms of Parkinson's disease that affect arm motor control.