| 1. |
- Ahmed, Mobyen Uddin
(författare)
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A case-based multi-modal clinical system for stress management
- 2010
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often make their diagnosis and decision based on manual inspection of physiological signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. Today the diagnosis and decision is largely dependent on how experienced the clinician is interpreting the measurements. A computer-aided decision support system for diagnosis and treatment of stress would enable a more objective and consistent diagnosis and decisions. A challenge in the field of medicine is the accuracy of the system, it is essential that the clinician is able to judge the accuracy of the suggested solutions. Case-based reasoning systems for medical applications are increasingly multi-purpose and multi-modal, using a variety of different methods and techniques to meet the challenges of the medical domain. This research work covers the development of an intelligent clinical decision support system for diagnosis, classification and treatment in stress management. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques have been investigated to enable a more reliable and efficient diagnosis and treatment of stress such as case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic. Functionalities and the performance of the system have been validated by implementing a research prototype based on close collaboration with an expert in stress. The case base of the implemented system has been initiated with 53 reference cases classified by an experienced clinician. A case study also shows that the system provides results close to a human expert. The experimental results suggest that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option.
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| 2. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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Case-Based Reasoning for Medical and Industrial Decision Support Systems
- 2010
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Ingår i: Successful Case-based Reasoning Applications. - Berlin, Heidelberg : Springer. - 9783642140778 ; , s. 7-52
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The amount of medical and industrial experience and knowledge is rapidly growing and it is almost impossible to be up to date with everything. The demand of decision support system (DSS) is especially important in domains where experience and knowledge grow rapidly. However, traditional approaches to DSS are not always easy to adapt to a flow of new experience and knowledge and may also show a limitation in areas with a weak domain theory. This chapter explores the functionalities of Case-Based Reasoning (CBR) to facilitate experience reuse both in clinical and in industrial decision making tasks. Examples from the field of stress medicine and condition monitoring in industrial robots are presented here to demonstrate that the same approach assists both for clinical applications as well as for decision support for engineers. In the both domains, DSS deals with sensor signal data and integrate other artificial intelligence techniques into the CBR system to enhance the performance in a number of different aspects. Textual information retrieval, Rule-based Reasoning (RBR), and fuzzy logic are combined together with CBR to offer decision support to clinicians for a more reliable and efficient management of stress. Agent technology and wavelet transformations are applied with CBR to diagnose audible faults on industrial robots and to package such a system. The performance of the CBR systems have been validated and have shown to be useful in solving such problems in both of these domains.
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| 3. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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Heart Rate and Inter-beat Interval Computation to Diagnose Stress
- 2010
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Problem in diagnosing of stress is an important issue. The variations in beat-to-beat alteration in the heart rate (HR) can provide an identification of stress. HR can be determined from the Electrocardiogram (ECG) signal. However, accurate detection of HR and inter-beat interval (IBI) values from the ECG waveform is important. This report presents a way of measuring the ECG signal together with the ECG component analysis such as QRS peak detection and HR calculation to use it in a computer-based stress diagnosis system.
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| 4. |
- Begum, Shahina, et al.
(författare)
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Development of a Stress Questionnaire : A Tool for Diagnosing Mental Stress
- 2010
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Stress and its relation with health, behavioral and environmental factors are known today. The stress questionnaire is a scientific screening instrument to understand individual’s causes of stress in different parts of life e.g. in the work place and at home. The 38-item stress questionnaire (SQ) is developed to assess the appraisal of stress personally experienced in a patient’s life. This questionnaire cannot diagnose any illness or psychological disorder. However it can be a helpful tool for developing the individual stress management plan by assessing data about the current demands of individual’s life and work.
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| 5. |
- Begum, Shahina, et al.
(författare)
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ECG Sensor Signal Analysis to Represent Cases in a Case-based Stress Diagnosis System
- 2010
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Ingår i: Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB. - Corfu, Greece. - 9781424465606
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Konferensbidrag (refereegranskat)abstract
- This paper presents a signal pre-processing and feature extraction approach based on electrocardiogram (ECG) sensor signal. The extracted features are used to formulate cases in a case-based reasoning system to develop a personalized stress diagnosis system. The results obtained from the evaluation show a performance close to an expert in the domain in diagnosing stress using ECG sensor signal.
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| 6. |
- Begum, Shahina, et al.
(författare)
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Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management
- 2010
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Ingår i: Computational Intelligence in Healthcare 4. - : Springer Berlin/Heidelberg. - 9783642144639 ; , s. 159-189
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The complexity of modern lifestyle and society brings many advantages but also causes increased levels of stress for many people. It is known that increased exposure to stress may cause serious health problems if undiagnosed and untreated and a report from the Swedish government estimates that 1/3 of all long term sick leave is stress related. One of the physiological parameters for quantifying stress levels is the finger temperature that helps the clinician in diagnosis and treatment of stress. However, in practice, the complex and varying nature of signals makes it difficult and tedious to interpret and analyze the lengthy sequential measurements. A computer based system diagnosing stress would be valuable both for clinicians and for treatment. Since stress diagnosis has a week domain theory and there are large individual variations, Case-Based Reasoning is proposed as the main methodology. Feature extraction methods abstracting the original signals without losing key features are investigated. A fuzzy technique is also incorporated into the system to perform matching between the features derived from signals to better accommodate vagueness, uncertainty often present in clinical reasoning Validation of the approach is based on close collaboration with experts and measurements from twenty four persons. The system formulates a new problem case with 17 extracted features from the fifteen minutes (1800 samples) of biomedical sensor data. Thirty nine time series from twenty four persons have been used to evaluate the approach (matching algorithms) in which the system shows a level of performance close to an experienced expert. The system can be used as an expert for a less experienced clinician, as a second option for an experienced clinician or for treatment in home environment.
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