| 1. |
- Khan, Taha, 1983-, et al.
(författare)
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Assessing Parkinson's disease severity using speech analysis in non-native speakers
- 2019
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Ingår i: Computer speech & language (Print). - London, UK : Academic Press. - 0885-2308 .- 1095-8363. ; 61
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Tidskriftsartikel (refereegranskat)abstract
- Background: Speech disorder is a common manifestation of Parkinson's disease with two main symptoms, dysprosody and dysphonia. Previous research studying objective measures of speech symptoms involved patients and examiners who were native language speakers. Measures such as cepstral separation difference (CSD) features to quantify dysphonia and dysprosody accurately distinguish the severity of speech impairment. Importantly CSD, together with other speech features, including Mel-frequency coefficients, fundamental-frequency variation, and spectral dynamics, characterize speech intelligibility in PD. However, non-native language speakers transfer phonological rules of their mother language that tamper speech assessment.Objectives: This paper explores CSD's capability: first, to quantify dysprosody and dysphonia of non-native language speakers, Parkinson patients and controls, and secondly, to characterize the severity of speech impairment when Parkinson's dysprosody accompanies non-native linguistic dysprosody.Methods: CSD features were extracted from 168 speech samples recorded from 19 healthy controls, 15 rehabilitated and 23 not-rehabilitated Parkinson patients in three different clinical speech tests based on Unified Parkinson's disease rating scale motor-speech examination. Statistical analyses were performed to compare groups using analysis of variance, intraclass correlation, and Guttman correlation coefficient µ2. Random forests were trained to classify the severity of speech impairment using CSD and the other speech features. Feature importance in classification was determined using permutation importance score.Results: Results showed that the CSD feature describing dysphonia was uninfluenced by non-native accents, strongly correlated with the clinical examination (µ2>0.5), and significantly discriminated between the healthy, rehabilitated, and not-rehabilitated patient groups based on the severity of speech symptoms. However, the feature describing dysprosody did not correlate with the clinical examination but significantly distinguished the groups. The classification model based on random forests and selected features characterized the severity of speech impairment of non-native language speakers with high accuracy. Importantly, the permutation importance score of the CSD feature representing dysphonia was the highest compared to other features. Results showed a strong negative correlation (µ2<-0.5) between L-dopa administration and the CSD features.Conclusions: Although non-native accents reduce speech intelligibility, the CSD features can accurately characterize speech impairment, which is not always possible in the clinical examination. Findings support using CSD for monitoring Parkinson's disease.© 2019 Elsevier Ltd. All rights reserved.
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| 2. |
- Stasiunas, Antanas, et al.
(författare)
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A multi-channel adaptive nonlinear filtering structure realizingsome properties of the hearing system
- 2005
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Ingår i: Computers in Biology and Medicine. - Amsterdam : Elsevier. - 0010-4825 .- 1879-0534. ; 35:6, s. 495-510
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Tidskriftsartikel (refereegranskat)abstract
- An adaptive nonlinear signal-filtering model of the cochlea is proposed based on the functional properties of the inner ear. The model consists of the cochlear filtering segments taking into account the longitudinal, transverse and radial pressure wave propagation. On the basis of an analytical description of different parts of the model and the results of computer modeling, the biological significance of the nonlinearity of signal transduction processes in the outer hair cells, their role in signal compression and adaptation, the efferent control over the characteristics of the filtering structures (frequency selectivity and sensitivity) are explained. © 2004 Elsevier Ltd. All rights reserved.
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| 3. |
- Stasiunas, Antanas, et al.
(författare)
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An adaptive panoramic filter bank as a qualitative model of the filtering system of the cochlea : The peculiarities in linear and nonlinear mode
- 2012
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Ingår i: Medical Engineering and Physics. - Oxford : Elsevier. - 1350-4533 .- 1873-4030. ; 34:2, s. 187-194
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Tidskriftsartikel (refereegranskat)abstract
- Outer hair cells in the cochlea of the ear, together with the local structures of the basilar membrane, reticular lamina and tectorial membrane constitute the adaptive primary filters (PF) of the second order. We used them for designing a serial-parallel signal filtering system. We determined a rational number of the PF included in Gaussian channels of the system, summation weights of the output signals, and distribution of the PF along the basilar membrane. A Gaussian panoramic filter bank each channel of which consists of five PF is presented as an example. The properties of the PF, the channel and the filter bank operating in the linear and nonlinear modes are determined during adaptation and under efferent control. The results suggest that application of biological filtering principles can be useful for designing cochlear implants with new speech encoding strategies. © 2011 IPEM.
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| 4. |
- Stasiunas, Antanas, et al.
(författare)
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Compression, adaptation and efferent control in a revised outer hair cell functional model
- 2005
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Ingår i: Medical Engineering and Physics. - Amsterdam : Elsevier. - 1350-4533 .- 1873-4030. ; 27:9, s. 780-789
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Tidskriftsartikel (refereegranskat)abstract
- In the cochlea of the inner ear, outer hair cells (OHC) together with the local passive structures of the tectorial and basilar membranes comprise non-linear resonance circuits with the local and central (afferent–efferent) feedback. The characteristics of these circuits and their control possibilities depend on the mechanomotility of the OHC. The main element of our functional model of the OHC is the mechanomotility circuit with the general transfer characteristic y = k tanh(x − a). The parameter k of this characteristic reflects the axial stiffness of the OHC, and the parameter a working position of the hair bundle. The efferent synaptic signals act on the parameter k directly and on the parameter a indirectly through changes in the membrane potential. The dependences of the sensitivity and selectivity on changes in the parameters a and k are obtained by the computer simulation. Functioning of the model at low-level input signals is linear. Due to the non-linearity of the transfer characteristic of the mechanomotility circuit the high-level signals are compressed. For the adaptation and efferent control, however, the transfer characteristic with respect to the initial operating point should be asymmetrical (a > 0). The asymmetry relies on the deflection of the hair bundle from the axis of the OHC.
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| 5. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Hierarchical neural network for color classification
- 1994
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Ingår i: The 1994 IEEE International Conference on Neural Networks. - Piscataway, NJ : IEEE Press. - 078031901X ; , s. 2938-2941
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Konferensbidrag (refereegranskat)abstract
- To make the hierarchical architecture, the neural networks of different type and different unsupervised learning techniques were combined. The classification accuracy obtained from such architecture is high enough to use it in the print quality control.
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| 6. |
- Alzghoul, Ahmad, et al.
(författare)
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Screening paper runnability in a web-offset pressroom by data mining
- 2009
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Ingår i: Proceedings of the 9th Industrial Conference on Advances in Data Mining. - Berlin : Springer Berlin/Heidelberg. - 9783642030666 ; , s. 161-175
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Konferensbidrag (refereegranskat)abstract
- This paper is concerned with data mining techniques for identifying the main parameters of the printing press, the printing process and paper affecting the occurrence of paper web breaks in a pressroom.Two approaches are explored. The first one treats the problem as a task of data classification into “break” and “non break” classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The search process results in a set of input variables providing the lowest average loss incurred in taking decisions. The second approach, also based on genetic search, combines procedures of input variable selection and data mapping into a low dimensional space. The tests have shown that the web tension parameters are amongst the most important ones. It was also found that, provided the basic off-line paper parameters are in an acceptable range, the paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the off-line ones. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of the break cases was equal to 76.7%.
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| 7. |
- Bacauskiene, Marija, et al.
(författare)
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Random forests based monitoring of human larynx using questionnaire data
- 2012
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Ingår i: Expert systems with applications. - Amsterdam : Elsevier. - 0957-4174 .- 1873-6793. ; 39:5, s. 5506-5512
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Tidskriftsartikel (refereegranskat)abstract
- This paper is concerned with soft computing techniques-based noninvasive monitoring of human larynx using subject’s questionnaire data. By applying random forests (RF), questionnaire data are categorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important questionnaire statements are determined using RF variable importance evaluations. To explore data represented by variables used by RF, the t-distributed stochastic neighbor embedding (t-SNE) and the multidimensional scaling (MDS) are applied to the RF data proximity matrix. When testing the developed tools on a set of data collected from 109 subjects, the 100% classification accuracy was obtained on unseen data in binary classification into the healthy and pathological classes. The accuracy of 80.7% was achieved when classifying the data into the healthy, cancerous, noncancerous classes. The t-SNE and MDS mapping techniques applied allow obtaining two-dimensional maps of data and facilitate data exploration aimed at identifying subjects belonging to a “risk group”. It is expected that the developed tools will be of great help in preventive health care in laryngology.
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| 8. |
- Bouguelia, Mohamed-Rafik, 1987-, et al.
(författare)
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Agreeing to disagree : active learning with noisy labels without crowdsourcing
- 2018
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Ingår i: International Journal of Machine Learning and Cybernetics. - Heidelberg : Springer. - 1868-8071 .- 1868-808X. ; 9:8, s. 1307-1319
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Tidskriftsartikel (refereegranskat)abstract
- We propose a new active learning method for classification, which handles label noise without relying on multiple oracles (i.e., crowdsourcing). We propose a strategy that selects (for labeling) instances with a high influence on the learned model. An instance x is said to have a high influence on the model h, if training h on x (with label y = h(x)) would result in a model that greatly disagrees with h on labeling other instances. Then, we propose another strategy that selects (for labeling) instances that are highly influenced by changes in the learned model. An instance x is said to be highly influenced, if training h with a set of instances would result in a committee of models that agree on a common label for x but disagree with h(x). We compare the two strategies and we show, on different publicly available datasets, that selecting instances according to the first strategy while eliminating noisy labels according to the second strategy, greatly improves the accuracy compared to several benchmarking methods, even when a significant amount of instances are mislabeled. © Springer-Verlag Berlin Heidelberg 2017
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| 9. |
- Brorsson, Sofia, 1973-, et al.
(författare)
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Differences in the muscle activities in the forearm muscles in healthy men and women
- 2012
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Ingår i: Proceedings of the XIXth Congress of the International Society of Electrophysiology & Kinesiology. - Brisbane, Australia. - 9780646582283 ; , s. 437-437
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Konferensbidrag (refereegranskat)abstract
- Balance between flexor and extensor muscle activity is essential for optimal function. This has been demonstrated previously for the lower extremity, trunk and shoulder function, but information on the relationship in hand function is lacking. AIM: Was to evaluate whether there are qualitative differences in finger extension force(fef), grip force, force duration, force balance and the muscle activities in the forearm flexor and extensor muscles in healthy men and women in different ages.
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| 10. |
- Englund, Cristofer, et al.
(författare)
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A novel approach to estimate proximity in a random forest : An exploratory study
- 2012
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Ingår i: Expert systems with applications. - Amsterdam : Elsevier BV. - 0957-4174 .- 1873-6793. ; 39:17, s. 13046-13050
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Tidskriftsartikel (refereegranskat)abstract
- A data proximity matrix is an important information source in random forests (RF) based data mining, including data clustering, visualization, outlier detection, substitution of missing values, and finding mislabeled data samples. A novel approach to estimate proximity is proposed in this work. The approach is based on measuring distance between two terminal nodes in a decision tree. To assess the consistency (quality) of data proximity estimate, we suggest using the proximity matrix as a kernel matrix in a support vector machine (SVM), under the assumption that a matrix of higher quality leads to higher classification accuracy. It is experimentally shown that the proposed approach improves the proximity estimate, especially when RF is made of a small number of trees. It is also demonstrated that, for some tasks, an SVM exploiting the suggested proximity matrix based kernel, outperforms an SVM based on a standard radial basis function kernel and the standard proximity matrix based kernel.
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