| 1. |
- Barua, Shaibal, et al.
(författare)
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A Review on Machine Learning Algorithms in Handling EEG Artifacts
- 2014
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Ingår i: The Swedish AI Society (SAIS) Workshop SAIS, 14.
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Konferensbidrag (refereegranskat)abstract
- Brain waves obtained by Electroencephalograms (EEG) recording are an important research area in medical and health and brain computer interface (BCI). Due to the nature of EEG signal, noises and artifacts can contaminate it, which leads to a serious misinterpretation in EEG signal analysis. These contaminations are referred to as artifacts, which are signals of other than brain activity. Moreover, artifacts can cause significant miscalculation of the EEG measurements that reduces the clinical usefulness of EEG signals. Therefore, artifact handling is one of the cornerstones in EEG signal analysis. This paper provides a review of machine learning algorithms that have been applied in EEG artifacts handling such as artifacts identification and removal. In addition, an analysis of these methods has been reported based on their performance.
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| 2. |
- Begum, Shahina, et al.
(författare)
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A Fusion Based System for Physiological Sensor Signal Classification
- 2014
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Ingår i: Medicinteknikdagarna 2014 MTD10.
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Konferensbidrag (refereegranskat)abstract
- Today, usage of physiological sensor signals is essential in medical applications for diagnoses and classification of diseases. Clinicians often rely on information collected from several physiological sensor signals to diagnose a patient. However, sensor signals are mostly non-stationary and noisy, and single sensor signal could easily be contaminated by uncertain noises and interferences that could cause miscalculation of measurements and reduce clinical usefulness. Therefore, an apparent choice is to use multiple sensor signals that could provide more robust and reliable decision. Therefore, a physiological signal classification approach is presented based on sensor signal fusion and case-based reasoning. To classify Stressed and Relaxed individuals from physiological signals, data level and decision level fusion are performed and case-based reasoning is applied as classification algorithm. Five physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, data level fusion is performed using Multivariate Multiscale Entropy (MMSE) and extracted features are then used to build a case- library. Decision level fusion is performed on the features extracted using traditional time and frequency domain analysis. Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.
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| 3. |
- Begum, Shahina, et al.
(författare)
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Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning
- 2014
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Ingår i: Expert systems with applications. - : Elsevier BV. - 0957-4174 .- 1873-6793. ; 41:2, s. 295-305
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Tidskriftsartikel (refereegranskat)abstract
- Sensor signal fusion is becoming increasingly important in many areas including medical diagnosis and classification. Today, clinicians/experts often do the diagnosis of stress, sleepiness and tiredness on the basis of information collected from several physiological sensor signals. Since there are large individual variations when analyzing the sensor measurements and systems with single sensor, they could easily be vulnerable to uncertain noises/interferences in such domain; multiple sensors could provide more robust and reliable decision. Therefore, this paper presents a classification approach i.e. Multivariate Multiscale Entropy Analysis-Case-Based Reasoning (MMSE-CBR) that classifies physiological parameters of wheel loader operators by combining CBR approach with a data level fusion method named Multivariate Multiscale Entropy (MMSE). The MMSE algorithm supports complexity analysis of multivariate biological recordings by aggregating several sensor measurements e.g., Inter-beat-Interval (IBI) and Heart Rate (HR) from Electrocardiogram (ECG), Finger Temperature (FT), Skin Conductance (SC) and Respiration Rate (RR). Here, MMSE has been applied to extract features to formulate a case by fusing a number of physiological signals and the CBR approach is applied to classify the cases by retrieving most similar cases from the case library. Finally, the proposed approach i.e. MMSE-CBR has been evaluated with the data from professional drivers at Volvo Construction Equipment, Sweden. The results demonstrate that the proposed system that fuses information at data level could classify 'stressed' and 'healthy' subjects 83.33% correctly compare to an expert's classification. Furthermore, with another data set the achieved accuracy (83.3%) indicates that it could also classify two different conditions 'adapt' (training) and 'sharp' (real-life driving) for the wheel loader operators. Thus, the new approach of MMSE-CBR could support in classification of operators and may be of interest to researchers developing systems based on information collected from different sensor sources.
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| 4. |
- Begum, Shahina, et al.
(författare)
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EEG Sensor Based Classification for Assessing Psychological Stress
- 2013
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Ingår i: Studies in Health Technology and Informatics, Volume 189, 2013. - : IOS Press. - 9781614992677 ; , s. 83-88
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Konferensbidrag (refereegranskat)abstract
- Electroencephalogram (EEG) reflects the brain activity and is widely used in biomedical research. However, analysis of this signal is still a challenging issue. This paper presents a hybrid approach for assessing stress using the EEG signal. It applies Multivariate Multi-scale Entropy Analysis (MMSE) for the data level fusion. Case-based reasoning is used for the classification tasks. Our preliminary result indicates that EEG sensor based classification could be an efficient technique for evaluation of the psychological state of individuals. Thus, the system can be used for personal health monitoring in order to improve users health.
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| 5. |
- Begum, Shahina, et al.
(författare)
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Multi-Scale Entropy Analysis and Case-Based Reasoning to Classify Physiological Sensor Signals
- 2012
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Ingår i: Proceedings of the ICCBR 2012 Workshops. ; , s. 129-138
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Konferensbidrag (refereegranskat)abstract
- Sensor signal fusion is becoming increasingly important in many areas including medical diagnosis and classification. Clinicians/experts often do the diagnosis of stress, sleepiness, tiredness etc. based on several physiological sensor signals to achieve better accuracy in classification. This paper presents a case-based reasoning (CBR) system that offers an opportunity to classify healthy and stressed persons based on sensor signal fusion. Several sensor measurements for instance, i.e., heart rate, inter-beat-interval, finger temperature, skin conductance and respiration rate have been combined for the data level fusion using Multivariate Multiscale Entropy Analysis (MMSE) algorithm. This algorithm supports complexity analysis of multivariate biological recordings. Here, MMSE is used to formulate cases in the case-based classification system.
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| 6. |
- Begum, Shahina, et al.
(författare)
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Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning
- 2014
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Ingår i: Sensors (Switzerland). - : MDPI AG. - 1424-8220. ; 14:7, s. 11770-11785
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Tidskriftsartikel (refereegranskat)abstract
- Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i) decision-level fusion using features extracted through traditional approaches; and (ii) data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE). Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.
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