In this paper, a digital-measurement method for low-invasive clinical diagnosis of metallic prosthesis osseointegration is proposed. Electrical impedance spectroscopy is exploited to characterize the ...quality of the tissue at the interface between the bone and the prosthesis. The method overcomes current resolution limits of biological electrical-impedance analysis by means of several polarization levels. Electrical modeling through an equivalent circuit is used to define a quantitative index of osseointegration. In vitro experimental results of the proposed method validation show its sensitivity and its effectiveness in low-depth prostheses, such as in dentistry applications
Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented as a spatially fixed pattern ...of activation, which leads to the instantaneous mixing model. To allow modeling patterns of spatio-temporal dynamics, in particular, the flow of oxygenated blood, we have developed a convolutive ICA approach: spatial complex ICA applied to frequency-domain fMRI data. In several frequency-bands, we identify components pertaining to activity in primary visual cortex (V1) and blood supply vessels. One such component, obtained in the 0.10
Hz band, is analyzed in detail and found to likely reflect flow of oxygenated blood in V1.
This paper concerns the problem of recording and analyzing biomedical data that characterize the motor activities of the limbs in patients suffering from Parkinson's disease. The aim of this paper is ...the individuation of suitable parameters that are useful for detecting subjects affected by Parkinson's disease and revealing the state of the patient. Hence, a simple and inexpensive system that measures the force of palmar grip is presented and discussed, and a suitable software protocol that is able to record and manage the acquired data is developed. Finally, some biomedical parameters have been identified, characterizing the motor activities of limbs. The study of these parameters and the analysis of the correlations between the acquired data permit taking out useful information and details about the objective evaluation of Parkinson pathology, taking into account the metrological characteristics of the used measurement system
The classification of the electrocardiogram (ECG) signal has a vital impact on the identification of heart-related diseases. This can ensure the premature finding of heart disease and the proper ...selection of the patient's customized treatment. However, the detection of arrhythmia is a challenging task to perform manually. This justifies the necessity of a technique for automatic detection of abnormal heart signals. Therefore, our work is based on the classification of five classes of ECG arrhythmic signals from Physionet's MIT-BIH Arrhythmia Dataset. Artificial Neural Networks (ANN) have demonstrated significant success in ECG signal classification. Our proposed model is a Convolutional Neural Network (CNN) customized for the categorization of the ECG signals. Our result testifies that the planned CNN model can successfully categorize arrhythmia with an overall accuracy of 95.2%. The average precision and recall of the proposed model are 95.2% and 95.40% respectively. This model can effectively be used to detect irregularities of heart rhythm at an early stage.
A hardware and software solution is proposed for the development of a DSP-operated cell/particle counting and sizing system. LabVIEW, which is a graphical programming language, was adopted as the DSP ...programming platform. Special attention was paid to data reduction, noise elimination, baseline restoration, and pulsewidth discrimination. Global accuracy is verified based on externally generated test signals and using particle size standards. The hardware and software, with minor modifications, are also suitable as a detection system for cell/particle chromatography and flow cytometry.
The aim of this work is to draw the attention of the biophotonics community to a stochastic decomposition method (SDM) to potentially model 2-D scans of light scattering data from epithelium mucosa ...tissue. The emphasis in this work is on the proposed model and its theoretical pinning and foundation. Unlike previous works that analyze scattering signal at one spot as a function of wavelength or angle, our method statistically analyzes 2-D scans of light scattering data over an area. This allows for the extraction of texture parameters that correlate with changes in tissue morphology, and physical characteristics such as changes in absorption and scattering characteristics secondary to disease, information that could not be revealed otherwise. The method is tested on simulations, phantom data, and on a limited preliminary
animal experiment to track mucosal tissue inflammation over time, using the area
under receiver operating characteristics (ROC) curve as a performance measure. Combination of all the features results in an
value up to 1 for the simulated data, and
for the phantom data. For the tissue data, the best performances for differentiation between pairs of various levels of inflammation are 0.859, 0.983, and 0.999.
Human ECG sensing signals can be regarded as the observed variables of the human heart's nonlinear dynamic system, which can effectively reflect the state changes of the heart system. They can be ...used for heart health monitoring and related disease identification. Due to the robust chaos, nonlinearity, and complexity of ECG signals, it is challenging to express them by standard features. Therefore, this paper proposes a nonlinear topological data analysis method to model ECG signals and extract nonlinear features for ECG anomaly detection. Firstly, we use the time delay embedding approach to map the ECG time series to the topological space for phase space reconstruction to form the ECG point cloud. Then, based on the point cloud information in space, the persistent homology method was used to construct the topological imprint of ECG data. Finally, the persistence landscape in the topological impression was extracted as the topological feature of the ECG signal for ECG anomaly detection. With only 20% of the total training dataset, it achieves a 100% accuracy for normal heartbeats, 98.75% for ventricular beats, 95.88% for supra-ventricular moments, and 91.97% for fusion beats. Thus the method can be trained for a single individual, allowing for personalized analysis systems. With the present study, TDA could be a valuable tool for biomedical signal analysis, with potential application in customized data processing.
In this paper, the analysis of biomedical data characterizing motor activities of the limbs is carried out with the aim of extracting useful information for evaluating the state of subjects affected ...by Parkinson's disease. Initially, a simple and inexpensive system that measures the force of the palmar grip has been presented; then, a suitable software protocol that is able to record, manage, and interpret the acquired data has been realized. Afterward, a first analysis in the time domain has led to the identification of some biomedical parameters characterizing motor activities of the limbs; the study of these parameters and the analysis of the correlations between the acquired data have permitted the development of suitable models describing the time behavior of the palmar grip. In this paper, the authors propose a more detailed study that evaluates the filtering, phase noise effects, and performance of different methods used to estimate the characteristic parameters of the palmar grip.
We hypothesized that electrocardiogram (ECG) spatial phase analysis would define a spectrum of intracardiac organization from atrial fibrillation (AF), nonisthmus-dependent and isthmus-dependent ...atrial flutter (AFL) to supraventricular tachycardias (SVT), and similarly for ventricular arrhythmias. We analyzed arrhythmia ECGs of 33 patients with isthmus (n=9) and nonisthmus (n=5) dependent AFL and SVT: atrial (n=3), atrioventricular nodal (n=3), and orthodromic reciprocating (n=3) tachycardias, as well as AF (n=5), ventricular tachycardia (monomorphic, VT-MM; n=7), and fibrillation (VF; n=3). ECG spatial phase was considered coherent when the correlation coefficient of an atrial (or ventricular) template to its ECG over time maintained a constant relationship in XY, XZ, and YZ planes. Regularity was quantified spectrally from ECG and correlation series. Spatial coherence occurred in 9/9 cases of isthmus- but only 1/5 of cases of nonisthmus-dependent AFL (p<0.01; /spl chi//sup 2/). All showed one dominant spectral peak (temporal coherence). In AF, spatial phase was inconsistent in all planes and spectra were broad band. Temporal and spatial coherence occurred in other SVT. VT-MM maintained spatial phase and a single spectral peak, while VF displayed neither. Our conclusions are that temporal and spatial phase analysis from the ECG stratifies intra-atrial and intra-ventricular organization and reveals subtle variability lost on visual inspection.
This paper presents a Romanian telemedical project that has as goals researches, design and implementation of an electronic-informatics-telecom and scalable system, that allows the automatic and ...complex telemonitoring, everywhere and every time (at home, in hospitals, at work, of mobile subject, etc), in (almost) real time, of the vital signs of persons with chronic illnesses, of elderly people, of those having high medical risk and of those with neuro-locomotor disabilities. The main objective of this pilot project is to enable personalized medical teleservices delivery and patient safety enhancement based on an earlier diagnosis, and to act as basis for a public service for telemedical procedures.
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