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Interactions among physiological mechanisms are abundant in biomedical signals, and they may exist to maintain efficient homeostasis. For example, sympathetic and parasympathetic neural activities interact to either elevate or depress the heart rate, to maintain homeostasis. There has been considerable effort devoted to developing algorithms that can detect interactions between various physiological...
A novel method is proposed here to determine whether a time series is deterministic even in the presence of noise. The method is the extension of an existing method based on smoothness analysis of the signal in state space with surrogate data testing. While classical measures fail to detect determinism when the time series is corrupted by noise, the proposed method can clearly distinguish between...
In the study of biological systems, it is often desirable to study the relationship between two simultaneously recorded signals and investigate whether one signal is causing the other. Correlation between signals can be revealed by spectral analysis techniques such as coherence. While coherence reveals the interaction strength between two signals, it does not provide directional information about...
The time interval between the aortic (A2) and pulmonary (P2) components of the second heart sound (S2) is an indicator of the presence and severity of several cardiac abnormalities. However, in many cases identification of the A2 and P2 components is difficult due to their temporal overlap and significant spectral similarity. In this work, we present a method to extract the A2 and P2 components from...
Kernel Principal Component analysis is a nonlinear generalization of the popular linear multivariate analysis method. However, this method assumes that the observed data is independent, a disadvantage for many practical applications. In order to overcome this difficulty, the authors propose a combination of Kernel Principal Component analysis and hidden Markov models. The novelty of the proposed method...
In the last years the home monitoring development is increased both for its capability as a real time tool to manage patients health and to reduce hospitalization costs. The home monitoring system is a complex structure that needs the collaboration of different disciplines, from medicine to engineering, and technologies. This project has been developed with the integration of different groups of research...
The set partitioning in hierarchical trees (SPIHT) algorithm is very effective and computationally simple technique for image and signal compression. Here the author modified the algorithm which provides even better performance than the SPIHT algorithm. The enhanced set partitioning in hierarchical trees (ESPIHT) algorithm has performance faster than the SPIHT algorithm. In addition, the proposed...
Auditory evoked potentials (AEPs) have been recorded at high stimulus rates during sleep using continuous loop averaging deconvolution (CLAD) sequences. AEP transient signals are obtained via frequency domain deconvolution of overlapped responses. Simultaneous acquisition of auditory brainstem response (ABR), middle latency response (MLR), and long latency response (LLR) is obtained at an average...
Localization of the cognitive activity in the brain is one of the major problems in neuroscience. Current techniques for neuro-imaging are based on functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and event related potential (ERP) recordings. The highest temporal resolution is achieved by ERP, which is crucial for temporal localization of activities. However, the spatial...
Atrial fibrillation (AF) and flutter are common following cardiac surgery, increasing costs and morbidity. Cardiologists need a method to discern those patients who are at high risk for this arrhythmia in order to attempt to treat them by either pharmacologic or non-pharmacologic means. We performed a retrospective analysis of 377 CABG patients, of which 94 developed AF post-operatively. Feature selection...
The mean of cardiovascular pressure signals is an important metric in patient monitoring applications for many types of diseases and injuries. It is typically calculated with a moving average of 3-8 s of the pulsatile signal. This method of calculating the mean introduces a delay of 1.5-4 s. We demonstrate that an FIR filter with coefficients calculated with a least squares error (LSE) estimator can...
Conventional analysis of EEG signals for sleep scoring is based on the time domain assessment of wave patterns. Human experts carry out this task relying on the direct visualization of EEG epochs. Techniques that enhance an intuitive visualization may encourage a wider use of more abstract descriptors, such as frequency domain features. This paper presents a feature extraction method for EEG signals...
Despite much progress and research, fully reliable computer based epileptic seizure detection in EEG recordings is still elusive. This paper outlines a new strategy toward seizure detection. It is proposed that it is not the precise nature of a statistic that is important, but rather its variance over time. Using this, algorithms are presented that are able to successfully identify 97.6% of seizures...
Computer-aided fetal monitoring is based on automated analysis of the fetal heart rate (FHR) variability. The first and the main step in the automated signal interpretation is the estimation of the so called FHR baseline. There are various algorithms for baseline estimation, of different efficiency. For its evaluation, the method of modeling of FHR signal based on the preset baseline component has...
Osteoarthritis (OA) of knee is the most commonly occurring non-fatal irreversible disease, mainly in the elderly population and particularly in female. Various invasive and non-invasive methods are reported for the diagnosis of this articular cartilage pathology. Well known techniques such as X-ray, computed tomography, magnetic resonance imaging, arthroscopy and arthrography are having their disadvantages,...
We introduce a blind identification technique to reconstruct the clinically more relevant central aortic pressure waveform from multiple less invasively measured peripheral arterial pressure waveforms. We conducted initial testing of the technique in two swine in which peripheral arterial pressure waveforms from the femoral and radial arteries and reference central aortic pressure were simultaneously...
In machine learning based Brain Computer Interfaces (BCIs), it is a challenge to use only a small amount of labelled data to build a classifier for a specific subject. This challenge was specifically addressed in BCI Competition 2005. Moreover, an effective BCI system should be adaptive to tackle the dynamic variations in brain signal. One of the solutions is to have its parameters adjustable while...
We introduce an adaptive space time frequency analysis to extract and classify subject specific brain oscillations induced by motor imagery in a brain computer interface task. The introduced method requires no prior knowledge of the reactive frequency bands, their temporal behavior or cortical locations. The algorithm implements an arbitrary time-frequency segmentation procedure by using a flexible...
Catheter-based epicardial mapping is possible with two access methods: transthoracic access and transvenous access. Transthoracic access requires lengthy sequential mapping procedures and stable arrhythmias. Transvenous access uses the multielectrode (4-to-20 electrodes) catheters placed in the coronary veins, however, leaves most of the epicardium inaccessible to direct measurement. The aim of this...
Deception detection has important clinical and legal implications. However, the reliability of methods for the discrimination between truthful and deceptive responses is still limited. Efforts to improve reliability have examined measures of central nervous system function such as EEG. However, EEG analyses based on either time- or frequency-domain parameters have had mixed results. Because EEG is...
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