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This paper covers the development of a fetal heart monitor (fHM) on an ARM Cortex-M3 based platform, and its application in extracting fetal heart rate (fHR) non-invasively from the maternal abdomen. The fHM is a low-power, inexpensive ambulatory device connected to smart-phones via Bluetooth. This monitor uses active-electrodes for primary signal-conditioning, and has a digital back end for running...
Trend-filtering of physiological signals is one of the most challenging tasks, owing to the non-linear and non-stationary nature of the signals. In this regard, trend extraction from noisy fetal heart signals is a possible measure for diagnosis of a fetal pathological condition. The detrended fluctuation analysis (DFA) of non-invasive fetal-electrocardiogram (FECG) is principally influenced by the...
The segmentation algorithm, that separates the first heart sound Si and the second heart sound S2, is developed. The segmentation of phonocardiogram (PCG) signal is the first and the most important step for analyzing the PCG signal in the automatic diagnosis of heart sounds. The phonocardiogram signal is analyzed by employing variational mode decomposition (VMD) combined with Shannon Energy (SE) feature...
This paper presents a novel solution of convex and non-convex economic load dispatch (ELD) problem of small scale thermal power system using a hybrid soft computing approach. The solution method involves a combination of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms where the latter is used to tune the solution obtained by the former towards finding global optima....
Fetal electrocardiography (fECG) based assessment techniques are likely to be more effective in ensuring fetal well-being, than currently popular methods based on ultrasound echocardiography and pulse oximetry. Employing Non-invasive procedures for measurement of fECG not only ensures minimal patient discomfort, but also makes the product suitable for wearable applications. However, non-invasive fECG...
This article presents a general framework for automatic conversion of a piece of text into a diagram that is described in the given text. Such kind of text is often found in many branches of Science & Engineering. Secondary school-level geometric problems are considered as a reference in this study. A knowledge base or lexical resource named GeometryNet is used in interpreting geometric meaning...
Fetal electrocardiogram (FECG) monitoring has become essential due to the current increase in the relative number of cardiac patients worldwide. This paper proposes to use a deep learning approach to compress/recover FECG signals, improving the computation speed in a telemonitoring system. The problem is analogous to the reconstruction of a non-sparse signal in compressive sensing (CS) framework....
Telemonitoring is a potential solution for management of patients suffering from chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD), respiratory failure, and obstructive sleep apnea. However, the compression is a prime concern for designing telemonitoring systems via Wireless Body Area Networks (WBANs). In this regard, Compressed Sensing (CS) is a promising tool of...
The increasing cardiac diseases of people in recent years demand an early detection of heart diseases using electrocardiogram (ECG) signal processing techniques. In this work we present a semi automatic scheme to discriminate patient-specific ECG beats by using a kernel based feature extraction technique called kernel canonical correlation analysis (KCCA). The heartbeat classification scheme uses...
Economic load dispatch (ELD) is an operational planning of a power generation system whereby the demand load is optimally distributed among the generation units such that the total generation cost is minimized. This paper presents a new hybrid technique, Particle Swarm Optimization (PSO) combined with Ant Colony Optimization (ACO), to solve the ELD problem. The results of simulated test run of the...
A novel framework is proposed to classify biological sequences using a kernel. It considers the topological information along with the primary structural information. The widely used string kernel for sequence classification does not take into account the structural information which might be available for biological sequences. The proposed kernels incorporate the additional structural information...
Cellular auto-fluorescence along with morphological and cytoskeletal features were assessed in lung cancer cells undergoing induced epithelial mesenchymal transition (EMT). During EMT progression, significant increase was observed in cellular aspect ratio (AR), filamentous (F)-actin and green auto-fluorescence intensities while blue intensity decreased. These features were provided to a kernel classification...
This paper proposes a new approach for clustering English text documents, based on finding the pair wise correlation of documents in a given set of text documents. The correlation coefficient for each pair of documents is calculated on the basis of ranks given to the words in the documents. The ranking of the words occurring in a document is computed on the basis of weights of the words calculated...
Expectation Maximization (EM) based techniques are popular in motif finding applications. This work attempts to use a variant of Expectation Maximization Technique in order to reduce the complexity. The iterations of EM are performed in projected spaces to reduce the computation time.
Biochemical networks normally operate in the neighbourhood of its steady-state which may be of multiple in number. It may reach from one steady-state to other within a finite time. In this paper, it is shown how the biochemical network reaches to a desired steady-state within optimal time and energy, with positive control input. Control signals i.e., the independent state variables in the network,...
Big Data is a term applied to data sets whose size is beyond the ability of traditional software technologies to capture, store, manage and process within a tolerable elapsed time. The popular assumption around Big Data analytics is that it requires internet scale scalability: over hundreds of compute nodes with attached storage. In this paper., we debate on the need of a massively scalable distributed...
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