| 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. |
- 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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| 3. |
- 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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| 4. |
- 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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| 5. |
- 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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| 6. |
- Högberg, Cecilia, et al.
(författare)
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Immunochemical faecal occult blood tests in primary care and the risk of delay in the diagnosis of colorectal cancer
- 2013
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Ingår i: Scandinavian Journal of Primary Health Care. - : Informa UK Limited. - 0281-3432 .- 1502-7724. ; 31:4, s. 209-214
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Tidskriftsartikel (refereegranskat)abstract
- Objective. To evaluate the value, risks, and shortcomings of immunochemical faecal occult blood tests (iFOBTs) in the diagnosis of colorectal cancer (CRC) and adenomas with high-grade dysplasia (HGD) in patients initially presenting to primary care. Design. A retrospective population-based study. Setting and subjects. All 495 cases of CRC and adenomas with HGD diagnosed in the county of Jamtland, Sweden from 2005 to 2009. Results. Of 495 patients 323 (65%) initially presented to primary care. IFOBTs were performed in 215 of 323 (67%) patients. The sensitivity of iFOBT for CRC and adenomas with HGD was 88% (83% when patients with a history of rectal bleeding were excluded). Of 34 patients with anaemia found en passant, 10 had negative iFOBTs. Time to diagnosis was longer for patients with negative iFOBTs (p < 0.0005). Conclusion. IFOBT might be helpful in selecting which patients to refer for colonoscopy. However, iFOBT has a limited sensitivity as a diagnostic test for CRC and adenomas with HGD. Relying only on iFOBT for colonoscopy referral could delay diagnosis, especially for patients with anaemia found en passant.
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| 7. |
- Tidare, Jonatan
(författare)
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Temporal representation of Motor Imagery : towards improved Brain-Computer Interface-based strokerehabilitation
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Practicing Motor Imagery (MI) with a Brain-Computer Interface (BCI) has shown promise in promoting motor recovery in stroke patients. A BCI records a person’s brain activity and provides feedback to the person in real time, which allows the person to practice his or her brain activity. By imagining a movement (performing MI) such as gripping with their hand, cortical areas in the brain are activated that largely overlaps with those activated during the actual hand movement. A BCI can provide positive feedback when the hand-related cortical areas are activated during MI, which helps a person to learn how to perform MI. Despite evidence that stroke patients may recover some motor function from practicing MI with BCI feedback thanks to the feedback provided from a BCI, the effectiveness and reliability of BCI-based rehabilitation are still poor. A BCI can detect MI by analyzing patterns of features from the brain activity. The most common features are extracted from the oscillatory activity in the brain. In BCI research, MI is often treated as a static pattern of features, which is detected by using machine learning algorithms to assign activity into a binary state. However, this model of MI may be inaccurate. Analyzing brain activity as dynamically varying over time and with a continuous measure of strength could better represent the cortical activity related to MI. In this Licentiate thesis, I explore a method for analyzing the temporal dynamic of MI-activity with a continuous measure of strength. Brain activity was recorded with electroencephalography (EEG) and subject-specific feature patterns were extracted from a group of healthy subjects while they performed MI of two opposing hand movements: opening and closing the hand. Although MI of the two same-hand movements could not be discriminated, the continuous output from a machine learning algorithm was shown to correlate well with MI-related feature patterns. The temporal analysis also revealed that MI is dynamically encoded early, but later stabilizes into a more static pattern of brain activity. Last, to accommodate for higher temporal resolution of MI, I designed and evaluated a BCI framework by its feedback delay and uncertainty as a function of the stress on the system and found a non-linear correlation. These results could be essential for developing a BCI with time-critical feedback.To summarize, in this Licentiate thesis I propose a promising method for analyzing and extracting a temporal representation of MI, enabling relevant and continuous neurofeedback which may contribute to clinical advances in BCI-based stroke rehabilitation.
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| 8. |
- Trobec, R., et al.
(författare)
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Detection and Treatment of Atrial Irregular Rhythm with Body Gadgets and 35-channel ECG
- 2019
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Ingår i: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9789532330984 ; , s. 301-308
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Konferensbidrag (refereegranskat)abstract
- The atrial irregular rhythm, often reflected in atrial fibrillation, undulation or flutter, is recognized as one of the major causes of brain stroke and entails an increased risk of thromboembolic events because it increases the likelihood of blood clots formation. Its early detection is becoming an increasingly important preventive measure. The paper presents a simple methodology for the detection of atrial irregular rhythm by ECG body gadget that can perform long-term measurements, e.g. several weeks or more. Multichannel ECG, on the body surface, gives a more detailed insight into the atrial activity in comparison to standard 12-lead ECG. The information from MECG is compared with single-channel patch ECG. The obtained results suggest that the proposed methodology could be useful in treatments of atrial irregular rhythm. One can obtain a reliable information about the time and duration of fibrillation events, or determine arrhythmic focuses and conductive pathways in heart atria, or study the effects of antiarrhythmic drugs on existing arrhythmias and on an eventual development of new types of arrhythmias.
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