184 research outputs found
Automatic epilepsy detection using fractal dimensions segmentation and GP-SVM classification
Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms.
Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs.
Results: The final application of GP SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector.
Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm's classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS.Web of Science142449243
A Replication Study on Code Comprehension and Expertise using Lightweight Biometric Sensors
Code comprehension has been recently investigated from physiological and
cognitive perspectives through the use of medical imaging. Floyd et al (i.e.,
the original study) used fMRI to classify the type of comprehension tasks
performed by developers and relate such results to their expertise. We
replicate the original study using lightweight biometrics sensors which
participants (28 undergrads in computer science) wore when performing
comprehension tasks on source code and natural language prose. We developed
machine learning models to automatically identify what kind of tasks developers
are working on leveraging their brain-, heart-, and skin-related signals. The
best improvement over the original study performance is achieved using solely
the heart signal obtained through a single device (BAC 87% vs. 79.1%).
Differently from the original study, we were not able to observe a correlation
between the participants' expertise and the classifier performance (tau = 0.16,
p = 0.31). Our findings show that lightweight biometric sensors can be used to
accurately recognize comprehension tasks opening interesting scenarios for
research and practice.Comment: Author version submitted to ICPC2019 (Replication track
An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers
This paper presents an epilepsy detection method based on discrete wavelet
transform (DWT) and Machine learning classifiers. Here DWT has been used for
feature extraction as it provides a better decomposition of the signals in
different frequency bands. At first, DWT has been applied to the EEG signal to
extract the detail and approximate coefficients or different sub-bands. After
the extraction of the coefficients, principal component analysis (PCA) has been
applied on different sub-bands and then a feature level fusion technique is
used to extract the important features in low dimensional feature space. Three
classifiers namely: Support Vector Machine (SVM) classifier, K-Nearest-Neighbor
(KNN) classifier, and Naive Bayes (NB) Classifiers have been used in the
proposed work for classifying the EEG signals. The proposed method is tested on
Bonn databases and provides a maximum of 100% recognition accuracy for KNN,
SVM, NB classifiers.Comment: Accepted in International Conference on Smart Technologies for
Sustainable Development (ICSTSD2021
User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features
In this study, the approach of combined features from two simultaneous Electroencephalogram (EEG) channels when a user is performing a certain mental task is discussed to increase the discrimination degree among subject classes, hence the visibility of using sets of features extracted from a single channel was investigated in previously published articles. The feature sets considered in previous studies is utilized to establish a combined set of features extracted from two channels. The first feature set is the energy density of power spectra of Discrete Fourier Transform (DFT) or Discrete Cosine Transform; the second one is the set of statistical moments of Discrete Wavelet Transform (DWT). Euclidean distance metric is used to accomplish feature set matching task. The combinations of features from two EEG channels showed high accuracy for the identification system, and competitive results for the verification system. The best achieved identification accuracy is (100%) for all proposed feature sets. For verification mode the best achieved Half Total Error Rate (HTER) is (0.88) with accuracy (99.12%) on Colorado State University (CSU) dataset, and (0.26) with accuracy (99.97%) on Motor Movement/Imagery (MMI) dataset
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