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Abnormal components in the QRS complex on the surface electrocardiogram have been used to predict sudden cardiac death in patients with heart disease. We propose a novel method to automate detection of abnormal peaks within the QRS complex. The approach involves identification of such peaks from consecutive unfiltered 10-beat QRS averages. A simulation using synthetic QRS peaks is conducted to assess...
The Electrocardiogram (ECG) describes the electrical activity of the heart and is an important tool to study the functioning of heart. The QRS Complex is the most important characteristic in ECG signal. The correct detection of QRS complex in ECG signal is extremely important for its further processing with regard to cardiac health monitoring and for heart rate variability (HRV) studies. This paper...
The purpose of this study is to determine the performance of the working memory in processing visual stimuli based on the single-trial Event-Related Potential (ERP) signal. This signal was further analyzed using the logistic regression method to investigate children's working memory ability. This study involved 54 children aged 10 to 12 years old who were given computer-based visual stimuli while...
In this paper we introduce a new framework called Med Cat to delineate and demonstrate an approach for projecting representations of concept-derived content in clinical notes into a new categorization space to reduce dimensionality and noise in the data. Constructing Med Cat framework required several steps including manual annotation, knowledge base expansion using MetaMap, concept category construction,...
This paper presents the ways we explored until now for detecting and dealing with the class noise found in large annotated datasets used for training the classifiers that we have previously designed for industrial-scale malware identification. First we established a number of distance-based filtering rules that allow us to identify different "levels'' of potential noise in the training data,...
Cardiac auscultation has proven to be an excellent diagnostic tool. Heart sound processing algorithms are not completely robust in the presence of noise, requiring clean segments of heart sounds to extract reliable diagnostic features. This paper presents a new approach to detect transient noises mixed with heart sound. The algorithm explores a single channel source separation algorithm and evaluates...
In this paper, we review the state-of-the-art in neural interface recording architectures. Through this we identify schemes which show the trade-off between data information quality (lossiness), computation (i.e. power and area requirements) and the number of channels. We further extend these tradeoffs by band-limiting the signal through reducing the front-end amplifier bandwidth. We therefore explore...
Context extraction for local fusion (CELF) is a local approach that combines multiple classifier outputs with the help of feature space information. CELF is based on an objective function that integrates context extraction and decision fusion. Context extraction divides the feature space into homogeneous regions; decision fusion combines multiple classifier outputs in each region or context. Although...
A novel multi-scale approach is presented for the purpose of robust keypoint extraction in high-noise environments. A multi-scale representation of the noisy scene is computed using quasi-random scale space theory. A gradient second-order moment analysis is employed at each quasi random scale to identify initial keypoint candidates. Final keypoints and their characteristic scales are selected based...
Spatial Independent Components Analysis (ICA) is increasingly used in the context of functional Magnetic Resonance Imaging (fMRI) to study cognition and brain pathologies. Salient features present in some of the extracted Independent Components (ICs) can be interpreted as brain networks, but the segmentation of the corresponding regions from ICs is still ill-controlled. Here we propose a new ICA-based...
A novel method for screening obstructive sleep apnea syndrome (OSAs) based on nocturnal acoustic signal is proposed. Full-night audio signals from sixty subjects were segmented into snore, noise and silence events using semi-automatic algorithm based on Gaussian mixture models which achieves more than 90% (92%) sensitivity (specificity) and produces an average of 2,000 snores per subject. A classification...
The (semi-) automatic detection of significant (feature) points is a key task in in vivo assessment of cancer staging and progression. However, this is a challenging task due to the relatively poor signal-to-noise, limited resolution and variable intensity of medical images. We propose to use phase congruence (PC), the Morrone and Owens (1987) feature model, to extract local descriptors. We overcome...
A noise tolerant template model technique for ECG feature extraction based on an individual-specific training approach is presented in this paper. Baseline wander, electrode motion artifacts, and electromyographic interference were added with varying signal-to-noise ratios (SNRs) to a dataset of approximately 3000 beats of different ECG recordings from the QT database to validate the performance of...
Features extraction is the steganographic images (stego- images) classification detection key. How to extract high susceptibility statistic features to noise jamming is a very important thing. A new effective features extraction method was proposed, aiming at the steganographic model with additive noise being the main focus. With the theoretic and experiment analysis, it revealed the difference of...
Significant clinical information can be obtained from the analysis of the dominant beat morphology. In such respect, the identification of the dominant beats and their averaging can be very helpful, allowing clinicians to perform the measurement of amplitudes and intervals on a beat much cleaner from noise than a generic beat selected from the entire ECG recording. In this paper an algorithm for the...
Precise load measurement is important and difficult for an industrial scale ball mill in thermal power plant. This paper presents a new approach which combines a novel feature extraction method and a hierarchical model to achieve this purpose. By analyzing the sensitivity distributions of working condition transitions and removing the ineffective information, the characteristic power spectrums (CPS)...
This paper presents a novel circular augmented rotational trajectory (CART) algorithm to compute an R-space based shape descriptors which allow efficient shape matching, generalization and classification. The rotation invariant R-space representation can be used to detect invariant geometric features despite the presence of considerable noise and quantization errors. Moreover, the CART method is corner...
In this paper we apply approximate and multiscale entropy metrics to spectrum occupancy data gathered during an extensive measurement campaign. We show that the presented methods can be successfully applied to search for and quantify structures of highly varying complexities and time scales. Although structures can be found they are unfortunately relatively complex and it does not appear to be easy...
This paper presents an eye-gaze tracking system based on the image processing. All the computations are performed in software and the system just needs a PC camera attached to the user's computer. We first extract the facial regions form the images using the skin-color model and connected-component analysis. Then the eye regions are detected by employing the rules and area segmentation. After the...
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