https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33906 x_a), signal energy (Ω^x), Fuzzy entropy (E^x_f), Kolmogorov Sinai entropy (E^x_ks), permutation entropy (E^x_p), Renyi entropy (E^x_r), sample entropy (E^x_s), Shannon entropy (E^x_sh), Tsallis entropy (E^x_t), wavelet entropy (E^x_w), fractal dimension (F^x_D), Kolmogorov complexity (C^x_k), largest Lyapunov exponent (E^x_LLE), recurrence quantification analysis (RQA) parameters (RQ^x_i), Hurst exponent (H^x), activity entropy (E^x_ac), Hjorth complexity (H^x_c), Hjorth mobility (H^x_m), modified multi scale entropy (E^x_mmsy) and higher order statistics (HOS) bispectrum (Bi^x_i) are obtained from the DWT coefficients. Later, these features are subjected to sequential forward feature selection (SFS) method and selected features are then ranked using seven ranking methods namely, Bhattacharyya distance, entropy, Fuzzy maximum relevancy and minimum redundancy (mRMR), receiver operating characteristic (ROC), Student’s t-test, Wilcoxon and ReliefF. These ranked features are supplied independently into the k-Nearest Neighbor (kNN) classifier. Our proposed system achieved maximum accuracy, sensitivity and specificity of (i) 97.72%, 94.79% and 98.74% for 5s, (ii) 98.34%, 95.49% and 99.14% for 8s and (iii) 98.32%, 95.16% and 99.20% for 10s of ECG segments using only ten features. The integration of the proposed algorithm with ECG acquisition systems in the intensive care units (ICUs) can help the clinicians to decipher the shockable and nonshockable life-threatening arrhythmias accurately. Hence, doctors can use the CPR or AED immediately and increase the chance of survival during shockable life-threatening arrhythmia intervals.]]> Wed 23 Jan 2019 10:40:16 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33891 four levels of wavelet packet decomposition (WPD) to obtain various coefficients. Using the fourth-level coefficients obtained for each lead ECG signal beat, new 2s. ECG signal beats are reconstructed. Later, the reconstructed signals are split into two-fold data sets, in which one set is used for acquiring common spatial pattern (CSP) filter and the other for obtaining features vector (vice versa). The obtained features are one by one fed into k-nearest neighbors (KNN) classifier for automated classification. The proposed system yielded maximum average classification results of 99.65% accuracy, 99.64% sensitivity and 99.7% specificity using 10 features. Our proposed algorithm is highly efficient and can be used by the clinicians as an aiding system in their CAD diagnosis, thus, assisting in faster treatment and avoiding the progression of CAD condition.]]> Tue 22 Jan 2019 14:22:16 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33827 six levels of Wavelet Packet Decomposition (WPD) to obtain seven wavebands (delta (d), theta (t), lower alpha (la), upper alpha (ua), lower beta (lb), upper beta (ub), lower gamma (lg)). From each wavebands (activity bands), 19 non-linear features such as Recurrence Quantification Analysis (RQA) (^x_y), Approximate Entropy (^x_ap), Energy (Ω^x), Fractal Dimension (FD) (F^x_D), Permutation Entropy (E^x_p), Detrended Fluctuation Analysis (α^x_y), Hurst Exponent (E^x_H), Largest Lyapunov Exponent (E^x_LLLE), Sample Entropy (E^x_s), Shannon's Entropy (E^x_sh), Renyi's entropy (E^x_r), Tsalli's entropy (E^x_ts), Fuzzy entropy (E^x_f), Wavelet entropy (E^x_w), Kolmogorov-Sinai entropy (E^x_ks), Modified Multiscale Entropy (E^x_mmsy), Hjorth's parameters (activity (S^x_a), mobility (H^x_m), and complexity (H^x_c)) are extracted. The extracted features are then ranked using Bhattacharyya, Entropy, Fuzzy entropy-based Max-Relevancy and Min-Redundancy (mRMR), Receiver Operating Characteristic (ROC), -test, and Wilcoxon. These ranked features are given to train Support Vector Machine (SVM) classifier. The SVM classifier with radial basis function (RBF) achieved 95.41% accuracy, 93.33% sensitivity and 97.50% specificity using four non-linear features ranked by Wilcoxon method. In addition, an integrated index called Alcoholic Index ( ALCOHOLI) is developed using highly ranked two features for identification of normal and alcoholic EEG signals using a single number. This system is rapid, efficient, and inexpensive and can be employed as an EEG analysis assisting system by clinicians in the detection of alcoholism. In addition, the proposed system can be used in rehabilitation centers to evaluate person with alcoholism over time and observe the outcome of treatment provided for reducing or reversing the impact of the condition on the brain.]]> Thu 28 Oct 2021 13:05:10 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33829 Thu 17 Jan 2019 13:58:10 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33828 Thu 17 Jan 2019 13:58:03 AEDT ]]>