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On-chip implementation of Hilbert-Huang transform (HHT) has great impact to analyze the non-linear and non-stationary biomedical signals on wearable or implantable sensors for the real-time applications. Cubic spline interpolation (CSI) consumes the most computation in HHT, and is the key component for the HHT processor. In tradition, CSI in HHT is usually performed after the collection of a large...
Wireless capsule endoscopy (WCE) has been validated to be an important tool in the evaluation of gastrointestinal (GI) tract. Compared with traditional endoscope technologies, its non-invasiveness property meets with great favor of patients. However, from physician's point of view, WCE video suffers from low resolution, limited illumination, irregular movement, more importantly, imbalanced rate of...
Several algorithms have been recently proposed for the analysis of dermoscopy images and the detection of melanomas. However, the pigment network is not considered in most of these works, although this cue plays a major role in clinical diagnosis routines. This paper proposes an algorithm for the detection of the pigment network. The algorithm is based on a bank of directional filters (difference...
‘OMICS’ techniques have deeply changed the drug discovery process. The availability of many different potential druggable genes, generated by these new techniques, have exploited the complexity of new lead compounds screening. ‘Virtual screening’, based on the integration of different analytical tools on high performance hardware platforms, has speeded up the search for new chemical entities suitable...
Compressed Sensing (CS) takes advantage of the sparsity of MR images in certain bases or dictionaries to obtain accurate reconstructions from undersampled k-space data. The (pseudo) random sampling schemes used most often for CS may have good theoretical asymptotic properties; however, with limited data they may be far from optimal. In this paper, we propose a novel framework for improved adaptive...
Clinical assessment scales to evaluate motor abilities in stroke survivors could be used to individualize rehabilitation interventions thus maximizing motor gains. Unfortunately, these scales are not widely utilized in clinical practice because their administration is excessively time-consuming. Wearable sensors could be relied upon to address this issue. Sensor data could be unobtrusively gathered...
Optical imaging in vivo is an important tool for allowing researchers to understand neural ensemble interactions during awake behavior, sleep, anesthesia and during seizure activity. A major bottleneck in the overall efficiency of neural imaging experiments is the need for post-hoc analysis of imaging data. Computational capabilities are now at the point where real- or near-real-time multivariate...
Iterative Closest Point (ICP) is a widely exploited method for point registration that is based on binary point-to-point assignments, whereas the Expectation Conditional Maximization (ECM) algorithm tries to solve the problem of point registration within the framework of maximum likelihood with point-to-cluster matching. In this paper, by fulfilling the implementation of both algorithms as well as...
CHRONIOUS system is an integrated platform aiming at the management of chronic disease patients. One of the most important components of the system is a Decision Support System (DSS) that has been developed in a Smart Device (SD). This component decides on patient's current health status by combining several data, which are acquired either by wearable sensors or manually inputted by the patient or...
In regions of the world where tuberculosis (TB) poses the greatest disease burden, the lack of access to skilled laboratories is a significant problem. A lab-free method for assessing patient recovery during treatment would be of great benefit, particularly for identifying patients who may have drug-resistant tuberculosis. We hypothesize that cough analysis may provide such a test. In this paper we...
Extracting physiological signals to control external devices such as prosthetics is a field of research that offers great hope for patients suffering from disabilities. In this paper, a novel source signal extraction algorithm, based on the source localization method Champagne, is presented. The algorithm constructs spatial filters that not only maximizes the signal to noise ratio (SNR > 13dB)...
Stimulation artifacts are short-duration, high-amplitude spikes which can be observed in electroencephalogram (EEG) recordings whenever surface functional electrical stimulation (FES) is applied during recordings. Stimulation artifacts are of non-physiologic origin and hence have to be removed before analysis of the EEG can take place. In this paper, algorithms for the detection and removal of stimulation...
The least squares fitting algorithm is the most commonly used algorithm in Raman spectroscopy. In this paper, however, we show that it is sensitive to variations in the background signal when the signal of interest is weak. To address this problem, we propose a novel algorithm to analyze measured spectra in Raman spectroscopy. The method is a hybrid least squares and principal component analysis algorithm...
To investigate the neural activity corresponding to different cognitive states, it is of great importance to localize the cortical areas that are associated with task-related modulation. In this paper, we propose a novel discriminant pattern source localization (DPSL) method to analyze MEG data. Unlike most traditional source localization methods that aim to find “dominant” sources, DPSL is developed...
An algorithm based only on the impedance cardiogram (ICG) recorded through two defibrillation pads, using the strongest frequency component and amplitude, incorporated into a defibrillator could determine circulatory arrest and reduce delays in starting cardiopulmonary resuscitation (CPR). Frequency analysis of the ICG signal is carried out by integer filters on a sample by sample basis. They are...
This article presents a new simulation tool for designing and testing blood glucose control algorithms in patients with type 1 diabetes. The control algorithms can be designed and implemented either with textual or graphical programming languages or by importing them from several frameworks. Realistic scenarios and protocols can be customized and built through graphical user interfaces, where several...
In automated ECG monitoring, QRS detection performance is dependent on noise measurements on individual leads. A new signal quality measurement based on stability analysis of QRS complex features has been developed to assess individual ECG lead quality. The new method was evaluated on the records of the MIT-BIH arrhythmia and NST databases. Results showed that the new signal quality measurement can...
In this paper, a time domain algorithm architecture is presented and implemented on a smart-phone for ECG signal analysis. Using the QRS detection algorithm suggested by Pan-Tompkins and the beat classification method, the heart beats are detected and classified as normal beats and premature ventricular contractions (PVCs). Subsequently, a computationally efficient method is presented to separate...
In this paper, we present a new seizure detection algorithm and the associated CMOS circuitry implementation. The proposed low-power seizure detector is a good candidate for an implantable epilepsy prosthesis. The device is designed for patient-specific seizure detection with a one variable parameter. The parameter value is extracted from a single seizure that is subsequently excluded from the validation...
Accurate segmentation of cell nuclei in microscope images of tissue sections is a key step in a number of biological and clinical applications. Often such applications require analysis of large image datasets for which manual segmentation becomes subjective and time consuming. Hence automation of the segmentation steps using fast, robust and accurate image analysis and pattern classification techniques...
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