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I 3 Abstract This paper presents a novel algorithm to detect onset and duration of QRS complexes. After low-pass filtering, the ECG signal is converted to a curve length signal by a transform in which a nonlinear scaling factor is introduced to enhance the QRS complex and to suppress unwanted noise. Adoptive thresholds are applied to the length signal to(More)
The paper presents an algorithm for reducing false alarms related to changes in arterial blood pressure (ABP) in intensive care unit (ICU) monitoring. The algorithm assesses the ABP signal quality, analyses the relationship between the electrocardiogram and ABP using a fuzzy logic approach and post-processes (accepts or rejects) ABP alarms produced by a(More)
This paper presents an approach to detection and segmentation of liver tumors in 3D computed tomography (CT) images. The automatic detection of tumor can be formulized as novelty detection or two-class classification issue. The method can also be used for tumor segmentation, where each voxel is to be assigned with a correct label, either a tumor class or(More)
Relevance ranking has been a popular and interesting topic over the years, which has a large variety of applications. A number of machine learning techniques were successfully applied as the learning algorithms for relevance ranking, including neural network, regularized least square, support vector machine and so on. From machine learning point of view,(More)
Intensive Care Unit (ICU) false alarm rates can be as high as 86%, leading to a desensitization of the clinical attending staff, slowing of response times and even ignoring true alarms. False alarms are commonly caused by single channel artifacts and could be avoided if information from other independent signals were fused to form a more robust hypothesis(More)