Computer methods and programs in biomedicine
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Comput Methods Programs Biomed · Feb 2010
Performance evaluation and enhancement of lung sound recognition system in two real noisy environments.
This study investigates the problems associated with lung sound recognition under noisy conditions. Firstly, the effects of noise on the lung sound feature representation and the classification performance are analyzed. Two kinds of feature representations, autoregressive and mel-frequency cepstral coefficients, are used to characterize the lung sound signals. ⋯ Our experimental results indicate that additive noise produces a mismatch between training and recognition environments and deteriorates the classification performance with a decrease in the SNR levels. In order to compensate the degrading effect of noise on the lung sound recognition, a dual sensor spectral subtraction algorithm is applied to the lung sound signals before the extraction of lung sound features. It is observed that the proposed algorithm is capable of providing adequate performance in terms of noise suppression and lung sound signal enhancement.
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Comput Methods Programs Biomed · Nov 2009
A multidimensional segmentation evaluation for medical image data.
Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. ⋯ Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.
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Comput Methods Programs Biomed · Sep 2009
Randomized Controlled TrialEEG under anesthesia--feature extraction with TESPAR.
We investigated the problem of automatic depth of anesthesia (DOA) estimation from electroencephalogram (EEG) recordings. We employed Time Encoded Signal Processing And Recognition (TESPAR), a time-domain signal processing technique, in combination with multi-layer perceptrons to identify DOA levels. ⋯ We found that our system closely mimicked the behavior of the human expert, thus proving the utility of the method. Further analyses on the features extracted by our technique indicated that information related to DOA is mostly distributed across frequency bands and that the presence of high frequencies (> 80 Hz), which reflect mostly muscle activity, is beneficial for DOA detection.
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Comput Methods Programs Biomed · Aug 2009
Intelligent analysis in predicting outcome of out-of-hospital cardiac arrest.
The prognosis among patients who suffer out-of-hospital cardiac arrest is poor. Higher survival rates have been observed only in patients with ventricular fibrillation who were fortunate enough to have basic and advanced life support initiated early after cardiac arrest. ⋯ Six different supervised learning classification techniques were used and evaluated. It has been shown that machine learning methods can provide an efficient way to detect important prognostic factors upon which further emergency unit actions are based.
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Comput Methods Programs Biomed · Aug 2009
A minimal model of lung mechanics and model-based markers for optimizing ventilator treatment in ARDS patients.
A majority of patients admitted to the Intensive Care Unit (ICU) require some form of respiratory support. In the case of Acute Respiratory Distress Syndrome (ARDS), the patient often requires full intervention from a mechanical ventilator. ARDS is also associated with mortality rate as high as 70%. ⋯ The ability to use this identified patient specific model to optimize ventilator management is demonstrated by its ability to predict the patient specific response of PEEP changes before clinically applying them. Predictions of recruited lung volume change with change in PEEP have a median absolute error of 1.87% (IQR: 0.93-4.80%; 90% CI: 0.16-11.98%) for inflation and a median of 5.76% (IQR: 2.71-10.50%; 90% CI: 0.43-17.04%) for deflation, across all data sets and PEEP values (N=34predictions). This minimal model thus provides a clinically useful and relatively simple platform for continuous patient specific monitoring of lung unit recruitment for a patient.