| 1. |
- Gharehbaghi, Arash, et al.
(författare)
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A pattern recognition framework for detecting dynamic changes on cyclic time series
- 2015
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Ingår i: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 48:3, s. 696-708
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a framework for binary classification of the time series with cyclic characteristics. The framework presents an iterative algorithm for learning the cyclic characteristics by introducing the discriminative frequency bands (DFBs) using the discriminant analysis along with k-means clustering method. The DFBs are employed by a hybrid model for learning dynamic characteristics of the time series within the cycles, using statistical and structural machine learning techniques. The framework offers a systematic procedure for finding the optimal design parameters associated with the hybrid model. The proposed model is optimized to detect the changes of the heart sound recordings (HSRs) related to aortic stenosis. Experimental results show that the proposed framework provides efficient tools for classification of the HSRs based on the heart murmurs. It is also evidenced that the hybrid model, proposed by the framework, substantially improves the classification performance when it comes to detection of the heart disease.
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| 2. |
- Khan, Taha, et al.
(författare)
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A computer vision framework for finger-tapping evaluation in Parkinson's disease
- 2014
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier BV. - 0933-3657 .- 1873-2860. ; 60:1, s. 27-40
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: The rapid finger-tapping test (RFT) is an important method for clinical evaluation of movement disorders, including Parkinson's disease (PD). In clinical practice, the naked-eye evaluation of RFT results in a coarse judgment of symptom scores. We introduce a novel computer-vision (CV) method for quantification of tapping symptoms through motion analysis of index-fingers. The method is unique as it utilizes facial features to calibrate tapping amplitude for normalization of distance variation between the camera and subject. Methods: The study involved 387 video footages of RFT recorded from 13 patients diagnosed with advanced PD. Tapping performance in these videos was rated by two clinicians between the symptom severity levels ('0: normal' to '3: severe') using the unified Parkinson's disease rating scale motor examination of finger-tapping (UPDRS-FT). Another set of recordings in this study consisted of 84 videos of RFT recorded from 6 healthy controls. These videos were processed by a CV algorithm that tracks the index-finger motion between the video-frames to produce a tapping time-series. Different features were computed from this time series to estimate speed, amplitude, rhythm and fatigue in tapping. The features were trained in a support vector machine (1) to categorize the patient group between UPDRS-FT symptom severity levels, and (2) to discriminate between PD patients and healthy controls. Results: A new representative feature of tapping rhythm, 'cross-correlation between the normalized peaks' showed strong Guttman correlation (mu(2) = -0.80) with the clinical ratings. The classification of tapping features using the support vector machine classifier and 10-fold cross validation categorized the patient samples between UPDRS-FT levels with an accuracy of 88%. The same classification scheme discriminated between RFT samples of healthy controls and PD patients with an accuracy of 95%. Conclusion: The work supports the feasibility of the approach, which is presumed suitable for PD monitoring in the home environment. The system offers advantages over other technologies (e.g. magnetic sensors, accelerometers, etc.) previously developed for objective assessment of tapping symptoms.
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| 3. |
- Ahlqvist, Josefin, et al.
(författare)
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Affinity binding of inclusion bodies on supermacroporous monolithic cryogels using labeling with specific antibodies
- 2006
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Ingår i: Journal of Biotechnology. - : Elsevier BV. - 0168-1656 .- 1873-4863. ; 122:2, s. 216-225
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Tidskriftsartikel (refereegranskat)abstract
- A new chromatographic method based on affinity supermacroporous monolithic cryogels is developed for binding and analyzing inclusion bodies during fermentation. The work demonstrated that it is possible to bind specific IgG and IgY antibodies to the 15 and 17 amino acids at the terminus ends of a 33 kDa target protein aggregated as inclusion bodies. The antibody treated inclusion bodies from lysed fermentation broth can be specifically retained in protein A and pseudo-biospecific ligand sulfamethazine modified supermacroporous cryogels. The degree of binding of IgG and IgY treated inclusion bodies to the Protein A and sulfamethazine gels are investigated, as well as the influence of pH on the sulfamethazine ligand. Optimum binding of 78 and 72% was observed on both protein A and sulfamethazine modified cryogel columns, respectively, using IgG labeling of the inclusion bodies. The antibody treated inclusion bodies pass through unretained in the sulfamethazine supermacroporous gel at pH that does not favour the binding between the ligand on the gel and the antibodies on the surface of inclusion bodies. Also the unlabeled inclusion bodies went through the gel unretained, showing no non-specific binding or trapping within the gel. These findings may very well be the foundation for the building of a powerful analytical tool during fermentation of inclusion bodies as well as a convenient way to purify them from fermentation broth. These results also support our earlier findings [Kumar, A., Plieva, F.M., Galaev, I.Yu., Mattiasson, B.. 2003. Affinity fractionation of lymphocytes using a monolithic cyogel. J. Immunol. Methods 283, 185-194] with mammalian cells that were surface labeled with specific antibodies and recognized on protein A supermacroporous gels. A general binding and separation system can be established on antibody binding cryogel affinity matrices.
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| 4. |
- Gharehbaghi, Arash, et al.
(författare)
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A novel method for discrimination between innocent and pathological heart murmurs
- 2015
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Ingår i: Medical Engineering and Physics. - : Elsevier. - 1350-4533 .- 1873-4030. ; 37:7, s. 674-682
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a novel method for discrimination between innocent and pathological murmurs using the growing time support vector machine (GTSVM). The proposed method is tailored for characterizing innocent murmurs (IM) by putting more emphasis on the early parts of the signal as IMs are often heard in early systolic phase. Individuals with mild to severe aortic stenosis (AS) and IM are the two groups subjected to analysis, taking the normal individuals with no murmur (NM) as the control group. The AS is selected due to the similarity of its murmur to IM, particularly in mild cases. To investigate the effect of the growing time windows, the performance of the GTSVM is compared to that of a conventional support vector machine (SVM), using repeated random sub-sampling method. The mean value of the classification rate/sensitivity is found to be 88%/86% for the GTSVM and 84%/83% for the SVM. The statistical evaluations show that the GTSVM significantly improves performance of the classification as compared to the SVM.
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| 5. |
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| 6. |
- Sperber, Göran O., et al.
(författare)
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Automated recognition of retroviral sequences in genomic data - RetroTector©
- 2007
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 35:15, s. 4964-4976
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Tidskriftsartikel (refereegranskat)abstract
- Eukaryotic genomes contain many endogenous retroviral sequences (ERVs). ERVs are often severely mutated, therefore difficult to detect. A platform independent (Java) program package, RetroTector© (ReTe), was constructed. It has three basic modules: (i) detection of candidate long terminal repeats (LTRs), (ii) detection of chains of conserved retroviral motifs fulfilling distance constraints and (iii) attempted reconstruction of original retroviral protein sequences, combining alignment, codon statistics and properties of protein ends. Other features are prediction of additional open reading frames, automated database collection, graphical presentation and automatic classification. ReTe favors elements >1000-bp long due to its dependence on order of and distances between retroviral fragments. It detects single or low-copy-number elements. ReTe assigned a 'retroviral' score of 890-2827 to 10 exogenous retroviruses from seven genera, and accurately predicted their genes. In a simulated model, ReTe was robust against mutational decay. The human genome was analyzed in 1-2 days on a LINUX cluster. Retroviral sequences were detected in divergent vertebrate genomes. Most ReTe detected chains were coincident with Repeatmasker output and the HERVd database. ReTe did not report most of the volutionary old HERV-L related and MalR sequences, and is not yet tailored for single LTR detection. Nevertheless, ReTe rationally detects and annotates many retroviral sequences.
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| 7. |
- Gerdtman, Christer, 1968-
(författare)
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Avancerade alternativa inmatningsenheter till datorer för funktionshindrade
- 2011
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Datorn är ett viktigt verktyg i vår vardag. För rörelsehindrade kan datorn vara en förutsättning för att kunna fungera i vardagen. Datorn har som tekniskt hjälpmedel gett funktionshindrade större möjligheter till meningsfulla aktiviteter, såsom att på egen hand skriva, läsa och kommunicera. Samtidigt är det inte alltid funktionshindrade klarar av att använda vanliga datorer utan behöver alternativa inmatnings- och styrenheter. Målet med denna licentiatavhandling har varit att utveckla tekniker som ska stödja personer med rörelsebegränsningar. Främst har arbetet varit att utveckla alternativa tekniker för att kunna manövrera en dator. Viktiga aspekter i arbetet har varit att göra hjälpmedlen användarvänliga, möjliga att individanpassa, och att ta tillvara de tänkta användarnas synpunkter kring behov och önskemål. Vidare har inmatningsenheten utvärderats av användare och även tillämpats som ett rehabiliteringsverktyg för en mindre patientgrupp med nackskador, så kallade pisksnärtskador. Utöver dessa vetenskapliga mål har produktionsaspekterna varit viktiga. För att säkerställa att enheten ska kunna bli en produkt måste den kunna produceras och säljas till ett rimligt pris och detta måste beaktas under hela utvecklingsprocessen. En alternativ datormus baserad på MEMS-gyroskop har utvecklats. Utvecklingen är gjord utifrån de krav de tilltänka användarna ställde på den alternativa datormusen och enheten är utprovad och förbättrad i en iterativ process mellan utvecklare och användare, så kallad användarcentrerad utvecklingsprocess. MEMS-gyron var den typ av rörelsesensor som bäst svarade mot de krav som ställdes på enheten. De användare som deltog vid ett längre fältprov var samtliga nöjda och ville behålla musen. För att underlätta processen att välja rätt typ av gyro och att kunna utvärdera deras stabilitet under olika omgivningsfaktorer, såsom temperatur och vibrationer, har en testrig för gyron utvecklats. Människors rörelsemönster skiljer sig från industriella tillämpningar och därför behövs en speciell testrigg. Testriggen roterar gyron och mäter sensorsignalen under olika betingelser. Flera gyron kan testas samtidigt och testdatat kan sparas och analyseras i efterhand. Ett interaktivt datorbaserat träningsprogram har utvecklats och utvärderats vid en pilotstudie tillsammans med den alternativa datormusen. Syftet var att låta nackskadade utföra huvudrörelser och få en feedback på att de tränar rätt. Resultatet pekar på att detta kan vara en lovande metod.
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| 8. |
- Ghareh Baghi, Arash, et al.
(författare)
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Extraction of diagnostic information from phonocardiographic signal using time-growing neural network
- 2019
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Ingår i: IFMBE Proceedings. - Singapore : Springer Verlag. ; , s. 849-853, s. 849-853
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Konferensbidrag (refereegranskat)abstract
- This paper presents an original method for extracting medical information from a heart sound recording, so called Phonocardiographic (PCG) signal. The extracted information is employed by a binary classifier to distinguish between stenosis and regurgitation murmurs. The method is based on using our original neural network, the Time-Growing Neural Network (TGNN), in an innovative way. Children with an obstruction on their semilunar valve are considered as the patient group (PG) against a reference group (RG) of children with a regurgitation in their atrioventricular valve. PCG signals were collected from 55 children, 25/30 from the PG/RG, who referred to the Children Medical Center of Tehran University. The study was conducted according to the guidelines of Good Clinical Practices and the Declaration of Helsinki. Informed consents were obtained for all the patients prior to the data acquisition. The accuracy and sensitivity of the method was estimated to be 85% and 80% respectively, exhibiting a very good performance to be used as a part of decision support system. Such a decision support system can improve the screening accuracy in primary healthcare centers, thanks to the innovative use of TGNN.
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| 9. |
- Gharehbaghi, Arash, et al.
(författare)
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A hybrid model for diagnosing sever aortic stenosis in asymptomatic patients using phonocardiogram
- 2015
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Ingår i: IFMBE Proceedings. - Cham : Springer. - 9783319193878 - 9783319193861 ; , s. 1006-1009
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Konferensbidrag (refereegranskat)abstract
- This study presents a screening algorithm for severe aortic stenosis (AS), based on a processing method for phonocardiographic (PCG) signal. The processing method employs a hybrid model, constituted of a hidden Markov model and support vector machine. The method benefits from a preprocessing phase for an enhanced learning. The performance of the method is statistically evaluated using PCG signals recorded from 50 individuals who were referred to the echocardiography lab at Linköping University hospital. All the individuals were diagnosed as having a degree of AS, from mild to severe, according to the echocardiographic measurements. The patient group consists of 26 individuals with severe AS, and the rest of the 24 patients comprise the control group. Performance of the method is statistically evaluated using repeated random sub sampling. Results showed a 95% confidence interval of (80.5%-82.8%) /(77.8%- 80.8%) for the accuracy/sensitivity, exhibiting an acceptable performance to be used as decision support system in the primary healthcare center.
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| 10. |
- Gharehbaghi, Arash, et al.
(författare)
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A Novel Model for Screening Aortic Stenosis Using Phonocardiogram
- 2015
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Ingår i: 16th Nordic-Baltic Conference on Biomedical Engineering. - Cham : Springer Science Business Media. - 9783319129662 - 9783319129679 ; , s. 48-51
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Konferensbidrag (refereegranskat)abstract
- This study presents an algorithm for screening aortic stenosis, based on heart sound signal processing. It benefits from an artificial intelligent-based (AI-based) model using a multi-layer perceptron neural network. The AI-based model learns disease related murmurs using non-stationary features of the murmurs. Performance of the model is statistically evaluated using two different databases, one of children and the other of elderly volunteers with normal heart condition and aortic stenosis. Results showed a 95% confidence interval of the high accuracy/sensitivity (84.1%-86.0%)/(86.0%-88.4%) thus exhibiting a superior performance to a cardiologist who relies on the conventional auscultation. The study suggests including the heart sound signal in the clinical decision making due to its potential to improve the screening accuracy.
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