@InProceedings{6952848,
  author = {M. A. Ahmad and W. Majeed and N. A. Khan},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {An alive electroencephalogram analysis system to assist the diagnosis of epilepsy},
  year = {2014},
  pages = {2340-2344},
  abstract = {Computer assisted electroencephalograph analysis tools are trained to classify the data based upon the “ground truth” provided by the clinicians. After development and delivery of these systems there is no simple mechanism for these clinicians to improve the system's classification while encountering any false classification by the system. So the improvement process of the system's classification after initial training (during development) can be termed as `dead'. We consider neurologist as the best available benchmark for system's learning. In this article, we propose an `alive' system, capable of improving its performance by taking clinician's feedback into consideration. The system is based on taking DWT transform which has been shown to be very effective for EEG signal analysis. PCA is applied on the statistical features which are extracted from DWT coefficients before classification by an SVM classifier. After corrective marking of few epochs the initial average accuracy of 94.8% raised to 95.12.},
  keywords = {discrete wavelet transforms;electroencephalography;medical signal processing;patient diagnosis;principal component analysis;signal classification;support vector machines;alive electroencephalogram analysis system;epilepsy diagnosis;computer assisted electroencephalograph analysis tools;ground truth;false classification;neurologist;alive system;clinician feedback;DWT transform;EEG signal analysis;PCA;statistical features;DWT coefficients;SVM classifier;Electroencephalography;Feature extraction;Training;Epilepsy;Discrete wavelet transforms;Support vector machines;Accuracy;Electroencephalography (EEG);Epilepsy;Computer Assisted Analysis;Machine Learning;Biomedical Signal Processing},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926707.pdf},
}

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After development and delivery of these systems there is no simple mechanism for these clinicians to improve the system's classification while encountering any false classification by the system. So the improvement process of the system's classification after initial training (during development) can be termed as `dead'. We consider neurologist as the best available benchmark for system's learning. In this article, we propose an `alive' system, capable of improving its performance by taking clinician's feedback into consideration. The system is based on taking DWT transform which has been shown to be very effective for EEG signal analysis. PCA is applied on the statistical features which are extracted from DWT coefficients before classification by an SVM classifier. 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A. Khan},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {An alive electroencephalogram analysis system to assist the diagnosis of epilepsy},\n year = {2014},\n pages = {2340-2344},\n abstract = {Computer assisted electroencephalograph analysis tools are trained to classify the data based upon the “ground truth” provided by the clinicians. After development and delivery of these systems there is no simple mechanism for these clinicians to improve the system's classification while encountering any false classification by the system. So the improvement process of the system's classification after initial training (during development) can be termed as `dead'. We consider neurologist as the best available benchmark for system's learning. In this article, we propose an `alive' system, capable of improving its performance by taking clinician's feedback into consideration. The system is based on taking DWT transform which has been shown to be very effective for EEG signal analysis. PCA is applied on the statistical features which are extracted from DWT coefficients before classification by an SVM classifier. After corrective marking of few epochs the initial average accuracy of 94.8% raised to 95.12.},\n keywords = {discrete wavelet transforms;electroencephalography;medical signal processing;patient diagnosis;principal component analysis;signal classification;support vector machines;alive electroencephalogram analysis system;epilepsy diagnosis;computer assisted electroencephalograph analysis tools;ground truth;false classification;neurologist;alive system;clinician feedback;DWT transform;EEG signal analysis;PCA;statistical features;DWT coefficients;SVM classifier;Electroencephalography;Feature extraction;Training;Epilepsy;Discrete wavelet transforms;Support vector machines;Accuracy;Electroencephalography (EEG);Epilepsy;Computer Assisted Analysis;Machine Learning;Biomedical Signal Processing},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926707.pdf},\n}\n\n","author_short":["Ahmad, M. A.","Majeed, W.","Khan, N. 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