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by Keyword: Wavelet Transform


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Alsaleh, S. M., Aviles, A. I., Sobrevilla, P., Casals, A., Hahn, J. K., (2015). Automatic and robust single-camera specular highlight removal in cardiac images Engineering in Medicine and Biology Society (EMBC) 37th Annual International Conference of the IEEE , IEEE (Milan, Italy) , 675-678

In computer-assisted beating heart surgeries, accurate tracking of the heart's motion is of huge importance and there is a continuous need to eliminate any source of error that might disturb the tracking process. One source of error is the specular reflection that appears on the glossy surface of the heart. In this paper, we propose a robust solution for the detection and removal of specular highlights. A hybrid color attributes and wavelet based edge projection approach is applied to accurately identify the affected regions. These regions are then recovered using a dynamic search-based inpainting with adaptive windowing. Experimental results demonstrate the precision and efficiency of the proposed method. Moreover, it has a real-time performance and can be generalized to various other applications.

Keywords: Heart, Image color analysis, Image edge detection, Surgery, Tracking, Wavelet transforms


Tellez, J. P., Herrera, S., Benito, S., Giraldo, B. F., (2014). Analysis of the breathing pattern in elderly patients using the hurst exponent applied to the respiratory flow signal Engineering in Medicine and Biology Society (EMBC) 36th Annual International Conference of the IEEE , IEEE (Chicago, USA) , 3422-3425

Due to the increasing elderly population and the extensive number of comorbidities that affect them, studies are required to determine future increments in admission to emergency departments. Some of these studies could focus on the relation between chronic diseases and breathing pattern in elderly patients. Variations in the fractal properties of respiratory signals can be associated with several diseases. To determine the relationship between these variations and breathing patterns, and to quantify the fractal properties of respiratory flow signals, we estimated the Hurst exponent (H). Detrended fluctuation analysis (DFA) and discrete wavelet transform-based estimation (DWTE) methods were applied. The estimation methods were analyzed using simulated data series generated by fractional Gaussian noise. 43 elderly patients (19 patients with a non-periodic breathing pattern - nPB, and 24 patients with a periodic breathing pattern - PB) were studied. The results were evaluated according to the length of data and the number of averaged data series used to obtain a good estimation. The DWTE method estimated the respiratory flow signals better than the DFA method, and obtained Hurst values clustered by group. We found significant differences in the H exponent (p = 0.002) between PB and nPB patients, which showed different behavior in the fractal properties.

Keywords: Discrete wavelet transforms, Diseases, Estimation, Fractals, Modulation, Senior citizens, Time series analysis


Gonzalez, H., Acevedo, H., Arizmendi, C., Giraldo, B. F., (2013). Methodology for determine the moment of disconnection of patients of the mechanical ventilation using discrete wavelet transform Complex Medical Engineering (CME) 2013 ICME International Conference , IEEE (Beijing, China) , 483-486

The process of weaning from mechanical ventilation is one of the challenges in intensive care units. 66 patients under extubation process (T-tube test) were studied: 33 patients with successful trials and 33 patients who failed to maintain spontaneous breathing and were reconnected. Each patient was characterized using 7 time series from respiratory signals, and for each serie was evaluated the discrete wavelet transform. It trains a neural network for discriminating between patients from the two groups.

Keywords: discrete wavelet transforms, neural nets, patient treatment, pneumodynamics, time series, ventilation, T-tube test, discrete wavelet transform, extubation process, intensive care units, mechanical ventilation, moment of disconnection, neural network, patients, respiratory signals, spontaneous breathing, time series, weaning, Mechanical Ventilation, Neural Networks, Time series from respiratory signals, Wavelet Transform