A study of gaits in Parkinson's patients using autoregressive model

Han, Yang; Ma, Zhanhong; Zhou, Ping

doi:10.1109/bicta.2009.5338143

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…In addition, the speech signal can be analyzed using higher order spectra (HOS) (also known as polyspectra), that is, spectral representations of higher order moments or cumulants of a signal (Nikias and Raghuveer 1987). Currently, most feature extraction methods are based on the autoregressive (AR) models (Ganapathy et al 2014;Mesot and Barber 2007;Han et al 2009). However, these schemes are assumed to be linear, Gaussian and minimum phase, i.e., the speech signals are normally distributed, their frequency components are uncorrelated and their statistical properties do not change over time (Oveisgharan and Shamsollahi 2004;Shekofteh and Almasganj 2013).…”

Section: Introductionmentioning

confidence: 99%

A novel method for early diagnosis of Alzheimer’s disease based on higher-order spectral estimation of spontaneous speech signals

2016

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One main challenge for medical investigators is the early diagnosis of Alzheimer's disease (AD) because it provides greater opportunities for patients to be eligible for more clinical trials. In this study, higher order spectra of human speech signals during AD were explored to analyze and compare the quadratic phase coupling of spontaneous speech signals for healthy and AD subjects using bispectrum and bicoherence. The results showed that the quadratic phase couplings of spontaneous speech signal of persons with Alzheimer's were reduced compared to healthy subject. However, the speech phase coupled harmonics shifted to the higher frequencies in Alzheimer's than healthy subjects. In addition, it was shown not only are there significant differences between Alzheimer's and control subjects in parameters estimated, but also the speech patterns appeared to have fluctuated in both types of spontaneous speech.

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Section: Introductionmentioning

confidence: 99%

A novel method for early diagnosis of Alzheimer’s disease based on higher-order spectral estimation of spontaneous speech signals

2016

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“…On the other hand, those lines in PD patient are comparatively close to each other. This feature can be indicated by ADI, which shows that PD patients always possess larger ADI than that of control subjects [18]. Ref.…”

Section: Previous Workmentioning

confidence: 97%

“…The analysis of the gait dynamics of NDD patients has been widely applied for studying movement patterns in NDD patients. Multiple recent studies record the gait dynamics in different NDD patients and extract various features to distinguish each NDD [12][13][14][15][16][17][18][19][20].…”

Section: Previous Workmentioning

confidence: 99%

“…The least-squares support vector machine (LS-SVM) was used to distinguish the stride patterns between the ALS patients and healthy controls with an accuracy of 82.8% using data recordings from Physionet.org [17]. An Associated Discrete Index (ADI) was defined in [18] in order to measure the discrete degree at the same frequency. 93 patients with PD and 72 healthy controls were selected.…”

Section: Previous Workmentioning

confidence: 99%

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A Wearable Neuro-Degenerative Diseases Classification System Using Human Gait Dynamics

Saadeh

Altaf

2019

VLSI-SoC: Opportunities and Challenges Beyond the Internet of Things

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The increasing prevalence of neurodegenerative diseases (NDDs) impose substantial medical and public health burdens on populations throughout the world. NDDs are chronic diseases that affect the human central nervous system causing loss of neurons within the brain and/or spinal cord. This causes deterioration in movement and mental functioning of the patients. The current medications for this group of disorders are limited and aim to treat the symptoms only. A better understanding of the mechanisms underlying neurodegeneration should lead to more effective, disease-modifying treatments in the future. Continuous assessment of NDD patients is a key element of future care and treatment. This contribution proposes a wearable NDD detection system based on patient's gait dynamics using an unobtrusive force resistive sensor embedded in patient's shoe. The NDD classification is based on 3 fundamental gait features: stride time, stride time's fluctuation and the autocorrelation decay factor. It is designed to discriminate between healthy subjects and NDD patients and moreover identify the NDD type: (Huntington's disease (HD), Parkinson Disease (PD), and Amyotrophic Lateral Sclerosis (ALS)). The proposed NDD classification algorithm is implemented on FPGA and verified experimentally using Gait Dynamics dataset from Physionet. It offers a classification accuracy of 93.8%, 89.1%, 94% and 93.3%, for ALS, HD, PD, and healthy person, respectively, from a total set of 64 subjects.

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A study of gaits in Parkinson's patients using autoregressive model

Cited by 2 publications

References 7 publications

A novel method for early diagnosis of Alzheimer’s disease based on higher-order spectral estimation of spontaneous speech signals

A novel method for early diagnosis of Alzheimer’s disease based on higher-order spectral estimation of spontaneous speech signals

A Wearable Neuro-Degenerative Diseases Classification System Using Human Gait Dynamics

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