Electrical and Electronic Engineering - Research Publications
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ItemA comparative study on approximate entropy measure and poincare plot indexes of minimum foot clearance variability in the elderly during walking(BMC, 2008-02-02)BACKGROUND: Trip-related falls which is a major problem in the elderly population, might be linked to declines in the balance control function due to ageing. Minimum foot clearance (MFC) which provides a more sensitive measure of the motor function of the locomotor system, has been identified as a potential gait parameter associated with trip-related falls in older population. This paper proposes nonlinear indexes (approximate entropy (ApEn) and Poincaré plot indexes) of MFC variability and investigates the relationship of MFC with derived indexes of elderly gait patterns. The main aim is to find MFC variability indexes that well correlate with balance impairments. METHODS: MFC data during treadmill walking for 14 healthy elderly and 10 elderly participants with balance problems and a history of falls (falls risk) were analysed using a PEAK-2D motion analysis system. ApEn and Poincaré plot indexes of all MFC data sets were calculated and compared. RESULTS: Significant relationships of mean MFC with Poincaré plot indexes (SD1, SD2) and ApEn (r = 0.70, p < 0.05; r = 0.86, p < 0.01; r = 0.74, p < 0.05) were found in the falls-risk elderly group. On the other hand, such relationships were absent in the healthy elderly group. In contrast, the ApEn values of MFC data series were significantly (p < 0.05) correlated with Poincaré plot indexes of MFC in the healthy elderly group, whereas correlations were absent in the falls-risk group. The ApEn values in the falls-risk group (mean ApEn = 0.18 +/- 0.03) was significantly (p < 0.05) higher than that in the healthy group (mean ApEn = 0.13 +/- 0.13). The higher ApEn values in the falls-risk group might indicate increased irregularities and randomness in their gait patterns and an indication of loss of gait control mechanism. ApEn values of randomly shuffled MFC data of falls risk subjects did not show any significant relationship with mean MFC. CONCLUSION: Results have implication for quantifying gait dynamics in normal and pathological conditions, thus could be useful for the early diagnosis of at-risk gait. Further research should provide important information on whether falls prevention intervention can improve the gait performance of falls risk elderly by monitoring the change in MFC variability indexes.
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ItemJeeva: Enterprise Grid Enabled Web Portal for Protein Secondary Structure Prediction(IEEE, 2008)This paper presents a Grid portal for protein secondary structure prediction developed by using services of Aneka, a .NET-based enterprise Grid technology. The portal is used by research scientists to discover new prediction structures in a parallel manner. An SVM (Support Vector Machine)-based prediction algorithm is used with 64 sample protein sequences as a case study to demonstrate the potential of enterprise Grids.
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ItemA monomial ν-SV method for regression( 2007)In the present paper we describe a new formulation for Support Vector regression (SVR), namely monomial ν-SVR. Like the standard ν-SVR, the monomial ν-SVR method automatically adjusts the radius of insensitivity (the tube width, epsilon) to suit the training data. However, by replacing Vapnik’s epsilon-insensitive cost with a more general monomial epsilon-insensitive cost (and likewise replacing the linear tube shrinking term with a monomial tube shrinking term), the performance of the monomial ν-SVR is improved for data corrupted by a wider range of noise distributions. We focus on the quadric form of monomial ν-SVR and show that the dual form of this is simpler than the standard ν-SVR. We show that, like Suykens’ Least-Squares SVR (LS-SVR) method (and unlike standard ν-SVR), the quadric ν-SVR dual has a unique global solution. Comparisons are made between the asymptotic efficiency of our method and that of standard ν-SVR and LS-SVR which demonstrate the superiority of our method for the special case of higher order polynomial noise. These theoretical predictions are validated using experimental comparisons with the alternative approaches of standard ν-SVR, LS-SVR and weighted LS-SVR.
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ItemInteraction between sleep EEG and ECG signals during and after obstructive sleep apnea events with or without arousals(IEEE - Institute of Electrical and Electronic Engineers, 2008)
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ItemIdentification of onset, maximum and termination of obstructive sleep apnoea events in single lead ECG recordings(IEEE, 2008)Measuring the Apnoea Hypopnoea Index (AHI) is important for determining the severity of any apnoea patient. This study presents a method of screening each apnoea event separately based on the single lead Electrocardiogram (EGG) signal. The whole ECG of a subject was divided into Normal, Onset, OSA-maximum and Termination epochs with length of 5 seconds. PSD analysis was used for determining the features directly from the ECG. ROC area was calculated to determine the discrimination capability of each feature (or power in each frequency bin) found by PSD analysis. The maximum ROC area found between Normal vs. OSA-maximum was 0.81 in the frequency range of 52-72 Hz. The ROC area and significant frequency band for Normal vs. Onset and Normal vs. Termination were 0.78, 0.78 and 57-65 Hz, 52-66 Hz respectively.
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ItemAnalysis of coherence between sleep EEG and ECG signals during and after obstructive sleep apnea events(IEEE, 2008)This study presents the first successful preliminary attempt to directly investigate the interactions of power spectra of sleep EEG and ECG signals of patients with obstructive sleep apnea syndrome (OSAS) by coherence analysis. ECG and EEG signals were collected from 8 OSAS patients and 3 healthy subjects. Coherence between two signals over different frequency bands(0-128 Hz) were calculated for normal breathing events, obstructive sleep apnea (OSA) events and events following OSA terminations (with/without arousals) in non-REM as well as REM sleep. Overall coherence of ECG and EEG in REM sleep is higher than that in non-REM sleep. A significant (p=0.0164) difference of coherence in the range of 10-5 Hz was found among normal, OSA and termination events in REM sleep. The results could be useful in detecting OSA events or OSA related arousals to characterize sleep fragmentation from ECG and EEG signals.
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