New Cues in Low-Frequency of Speech for Automatic Detection of Parkinson’s Disease

Belalcázar-Bolaños, Elkyn Alexander; Orozco-Arroyave, Juan Rafael; Vargas-Bonilla, Jesús Francisco; Arias-Londoño, Julián D.; Castellanos-Domínguez, G.; Nöth, Elmar

doi:10.1007/978-3-642-38637-4_29

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…Several studies have been published regarding the diagnosis of PD detection where speech processing were considered. But in case of audio dataset the size of database is a major problem as it is too small (less than 60 PD cases) [2,31,35]. Being a complex activity involving cognitive, sensory and perceptual-motor components [32], handwriting can be considered as a promising biomarker [4] in this case as abnormal handwriting is obvious in PD.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Parkinson’s Disease from Micrographic Static Hand Drawings

et al. 2021

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Parkinson's disease (PD) is a neurological illness that occurs by the degeneration of cells in the nervous system. Early symptoms include tremors or involuntary movements of the hands, arms, legs, and jaw. Currently, the only method to diagnose PD involves the observation of its prodromal symptoms. Moreover, detecting handwriting will work as a variable for clinitians to understand PD in patients better. With the advancement of technology, it is possible to build applications that will aid in diagnosing PD without any clinical intervention. The majority suffering from PD have handwriting abnormalities (referred to as micrographia), which is the most reported among earlier signs of the disease. So this research is undertaken by focusing on the implication of micrographia. For this purpose, handwritten images are collected from a group of 136 PD patients and 36 healthy patients. These images form a dataset of 800 images that are used to train a model which will accurately classify PD patients. To achieve this transfer learning is chosen because of its ability to produce accurate results regardless of the limited size of the dataset. Here, different models of transfer learning are trained to figure out the well-fitting model. It was observed that VGG-16 performed adequately with a training accuracy of 90.63% while a testing accuracy of 91.36%.

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Section: Introductionmentioning

confidence: 99%

Early Detection of Parkinson’s Disease from Micrographic Static Hand Drawings

et al. 2021

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“…Although this kind of tests are useful to assess dysphonia of the patient, their usefulness for evaluating the severity of the PD is still unclear. Different efforts to analyze the influence of the disease in the speech of PD patients have emerged, in 1 978-1-4799-1121-9/13/$31.00 c 2013 IEEE [6] and [7] the authors study changes in the low frequency spectra in order to characterize possible displacements of the velum due to the lack of control of this limb. According to the results, low frequency region gives important information able to characterize speech impairments in PPD.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of Parkinson's disease using noise measures of speech

Belalcázar-Bolaños

Orozco-Arroyave

Arias-Londoño

et al. 2013

Symposium of Signals, Images and Artificial Vision - 2013: STSIVA - 2013

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Abstract-Parkinson's disease (PD) is a neurodegenerative disorder that is characterized by the loss of dopaminergic neurons in the mid brain. It is demonstrated that about 90% of the people with PD also develop speech impairments, exhibiting symptoms such as monotonic speech, low pitch intensity, inappropriate pauses, imprecision in consonants and problems in prosody; although they are already identify problems, only 3% to 4% of the patients receive speech therapy. The research community has addressed the problem of the automatic detection of PD by means of noise measures; however, in such works only the phonation of the English vowel /a/ has been considered. In this paper, the five Spanish vowels uttered by 50 people with PD and 50 healthy controls (HC) are evaluated automatically considering a set of four noise measures: Harmonics to Noise Ratio (HNR), Normalized Noise Energy (NNE), Cepstral HNR (CHNR) and Glottal to Noise Excitation Ratio (GNE). The decision on whether a speech recording is from a person with PD or from a HC is taken by a K nearest neighbors (k-NN) classifier, finding an accuracy of 66.57% when only the vowel /i/ is considered.

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Speech disorders in Parkinson’s disease: early diagnostics and effects of medication and brain stimulation

et al. 2017

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Hypokinetic dysarthria (HD) occurs in 90% of Parkinson's disease (PD) patients. It manifests specifically in the areas of articulation, phonation, prosody, speech fluency, and faciokinesis. We aimed to systematically review papers on HD in PD with a special focus on (1) early PD diagnosis and monitoring of the disease progression using acoustic voice and speech analysis, and (2) functional imaging studies exploring neural correlates of HD in PD, and (3) clinical studies using acoustic analysis to evaluate effects of dopaminergic medication and brain stimulation. A systematic literature search of articles written in English before March 2016 was conducted in the Web of Science, PubMed, SpringerLink, and IEEE Xplore databases using and combining specific relevant keywords. Articles were categorized into three groups: (1) articles focused on neural correlates of HD in PD using functional imaging (n = 13); (2) articles dealing with the acoustic analysis of HD in PD (n = 52); and (3) articles concerning specifically dopaminergic and brain stimulation-related effects as assessed by acoustic analysis (n = 31); the groups were then reviewed. We identified 14 combinations of speech tasks and acoustic features that can be recommended for use in describing the main features of HD in PD. While only a few acoustic parameters correlate with limb motor symptoms and can be partially relieved by dopaminergic medication, HD in PD seems to be mainly related to non-dopaminergic deficits and associated particularly with non-motor symptoms. Future studies should combine non-invasive brain stimulation with voice behavior approaches to achieve the best treatment effects by enhancing auditory-motor integration.

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New Cues in Low-Frequency of Speech for Automatic Detection of Parkinson’s Disease

Cited by 7 publications

References 14 publications

Early Detection of Parkinson’s Disease from Micrographic Static Hand Drawings

Early Detection of Parkinson’s Disease from Micrographic Static Hand Drawings

Automatic detection of Parkinson's disease using noise measures of speech

Speech disorders in Parkinson’s disease: early diagnostics and effects of medication and brain stimulation

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