Parkinson’s Disease Detection based on Changes of Emotions during Speech

Skibińska, Justyna; Bürget, Radim

doi:10.1109/icumt51630.2020.9222446

Cited by 5 publications

(2 citation statements)

References 28 publications

(35 reference statements)

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“…The training was done using stratified 10-fold cross-validation. The advantage of the XGBoost algorithm is that it uses specific loss function as function approximation as well as regularization techniques [109] . This same algorithm has special potential to perform the best among classical ML algorithms.…”

Section: Resultsmentioning

confidence: 99%

Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

Skibińska,

Hosek

2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

Skibińska,

Hosek

2023

Heliyon

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“…Sensitivity is defined as the ratio of positive cases regarded by the algorithm as positive cases to the whole set of real positive cases. Specificity is the ratio of negative cases classified as negative to the whole set of real negative cases [40]. The Matthews correlation coefficient is mostly dedicated to imbalanced datasets.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Diagnosis at Early Stage Based on Smartwatches and Machine Learning Techniques

et al. 2021

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Early detection of COVID-19 positive people are now extremely needed and considered to be one of the most effective ways how to limit spreading the infection. Commonly used screening methods are reverse transcription polymerase chain reaction (RT-PCR) or antigen tests, which need to be periodically repeated. This paper proposes a methodology for detecting the disease in non-invasive way using wearable devices and for the analysis of bio-markers using artificial intelligence. This paper have reused a publicly available dataset containing COVID-19, influenza, and Healthy control data. In total 27 COVID-19 positive and 27 healthy control were pre-selected for the experiment, and several feature extraction methods were applied to the data. This paper have experimented with several machine learning methods, such as XGBoost, knearest neighbour k-NN, support vector machine, logistic regression, decision tree, and random forest, and statistically evaluated their perfomance using various metrics, including accuracy, sensitivity and specificity. The proposed experiment reached 78 % accuracy using the k-NN algorithm which is significantly higher than reported for state-of-the-art methods. For the cohort containing influenza, the accuracy was 73 % for k-NN. Additionally, we identified the most relevant features that could indicate the changes between the healthy and infected state. The proposed methodology can complement the existing RT-PCR or antigen screening tests, and it can help to limit the spreading of the viral diseases, not only COVID-19, in the non-invasive way.

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Automated video-based assessment of facial bradykinesia in de-novo Parkinson’s disease

et al. 2022

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Even though hypomimia is a hallmark of Parkinson’s disease (PD), objective and easily interpretable tools to capture the disruption of spontaneous and deliberate facial movements are lacking. This study aimed to develop a fully automatic video-based hypomimia assessment tool and estimate the prevalence and characteristics of hypomimia in de-novo PD patients with relation to clinical and dopamine transporter imaging markers. For this cross-sectional study, video samples of spontaneous speech were collected from 91 de-novo, drug-naïve PD participants and 75 age and sex-matched healthy controls. Twelve facial markers covering areas of forehead, nose root, eyebrows, eyes, lateral canthal areas, cheeks, mouth, and jaw were used to quantitatively describe facial dynamics. All patients were evaluated using Movement Disorder Society-Unified PD Rating Scale and Dopamine Transporter Single-Photon Emission Computed Tomography. Newly developed automated facial analysis tool enabled high-accuracy discrimination between PD and controls with area under the curve of 0.87. The prevalence of hypomimia in de-novo PD cohort was 57%, mainly associated with dysfunction of mouth and jaw movements, and decreased variability in forehead and nose root wrinkles (p < 0.001). Strongest correlation was found between reduction of lower lip movements and nigro-putaminal dopaminergic loss (r = 0.32, p = 0.002) as well as limb bradykinesia/rigidity scores (r = −0.37 p < 0.001). Hypomimia represents a frequent, early marker of motor impairment in PD that can be robustly assessed via automatic video-based analysis. Our results support an association between striatal dopaminergic deficit and hypomimia in PD.

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Parkinson’s Disease Detection based on Changes of Emotions during Speech

Cited by 5 publications

References 28 publications

Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

COVID-19 Diagnosis at Early Stage Based on Smartwatches and Machine Learning Techniques

Automated video-based assessment of facial bradykinesia in de-novo Parkinson’s disease

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