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:15150 Wed 11 Apr 2018 13:48:38 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:27487 Eucalyptus leucoxylon, E. microcarpa, E. polyanthemos and E. tricarpa. The DWT is used as a correlational method, and proposed as a new metric for synchronisation and visualisation for a multidimensional time series. The wavelet based results are compared to Moran's traditional synchronisation test and to a self organising map time series clustering approach. The Maximal Overlap DWT analysis successfully identify four subcomponents in each flowering series characterised as a (i) non-flowering phase (dl), (ii) duration (d2), (iii) annual (d3) and (iv) intensity (d4) cycles. The wavelet correlation (WCORR) signatures are shown to exhibit a common pattern for synchronous species pairs, for example d2, d3 and d4 WCORR values being significant and positive for E. leucoxylon and E. tricarpa whereas for species pairs that are asynchronous the d2 and d3 specific WCORR values are significant and negative for E. tricarpa and E. polyanthemos. The profiles of wavelet cross correlation (WCCORR) enable us to further understand how synchrony may relate to the subcomponents of flowering (peak annual cycles, start, cessation, duration, overlap) and climate. The positive periods of the WCCORR profiles for d2 reflect flowering overlap between species pairs. The d3 WCCORR's peak at the lag which is the difference between the species pair's peak flowering months. Distinctly different patterns for d3 WCCORR are associated with asynchronous and synchronous time series. The sinusoidal and cycling of the d3 specific WCCORR profiles mirror the cyclic 6 monthly climatic influence on species flowering, where for 6 months if temperature is positively correlated with flowering, rainfall is negative and vice versa.]]> Sat 24 Mar 2018 07:25:40 AEDT ]]>