Real Time QRS Detection Based on M-ary Likelihood Ratio Test on the DFT Coefficients

This paper shows an adaptive statistical test for QRS detection of electrocardiography (ECG) signals. The method is based on a M-ary generalized likelihood ratio test (LRT) defined over a multiple observation window in the Fourier domain. The motivations for proposing another detection algorithm based on maximum a posteriori (MAP) estimation are found in the high complexity of the signal model proposed in previous approaches which i) makes them computationally unfeasible or not intended for real time applications such as intensive care monitoring and (ii) in which the parameter selection conditions the overall performance. In this sense, we propose an alternative model based on the independent Gaussian properties of the Discrete Fourier Transform (DFT) coefficients, which allows to define a simplified MAP probability function. In addition, the proposed approach defines an adaptive MAP statistical test in which a global hypothesis is defined on particular hypotheses of the multiple observation window. In this sense, the observation interval is modeled as a discontinuous transmission discrete-time stochastic process avoiding the inclusion of parameters that constraint the morphology of the QRS complexes.This work has received research funding from the Spanish government (www.micinn.es) under project TEC2012 34306 (DiagnoSIS, Diagnosis by means of Statistical Intelligent Systems, 70K€) and projects P09-TIC-4530 (300K€) and P11-TIC-7103 (156K€) from the Andalusian government (http://www.juntadeandalucia.es/organismo​s/economiainnovacioncienciayempleo.html)

Individual identification via electrocardiogram analysis

Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations

Development and validation of an oligonucleotide microarray to characterise ectomycorrhizal fungal communities

<p>Abstract</p> <p>Background</p> <p>In forest ecosystems, communities of ectomycorrhizal fungi (ECM) are influenced by several biotic and abiotic factors. To understand their underlying dynamics, ECM communities have been surveyed with ribosomal DNA-based sequencing methods. However, most identification methods are both time-consuming and limited by the number of samples that can be treated in a realistic time frame. As a result of ongoing implementation, the array technique has gained throughput capacity in terms of the number of samples and the capacity for parallel identification of several species. Thus far, although phylochips (microarrays that are used to detect species) have been mostly developed to trace bacterial communities or groups of specific fungi, no phylochip has been developed to carry oligonucleotides for several ectomycorrhizal species that belong to different genera.</p> <p>Results</p> <p>We have constructed a custom ribosomal DNA phylochip to identify ECM fungi. Specific oligonucleotide probes were targeted to the nuclear internal transcribed spacer (ITS) regions from 95 fungal species belonging to 21 ECM fungal genera. The phylochip was first validated using PCR amplicons of reference species. Ninety-nine percent of the tested oligonucleotides generated positive hybridisation signals with their corresponding amplicons. Cross-hybridisation was mainly restricted at the genus level, particularly for <it>Cortinarius </it>and <it>Lactarius </it>species. The phylochip was subsequently tested with environmental samples that were composed of ECM fungal DNA from spruce and beech plantation fungal communities. The results were in concordance with the ITS sequencing of morphotypes and the ITS clone library sequencing results that were obtained using the same PCR products.</p> <p>Conclusion</p> <p>For the first time, we developed a custom phylochip that is specific for several ectomycorrhizal fungi. To overcome cross-hybridisation problems, specific filter and evaluation strategies that used spot signal intensity were applied. Evaluation of the phylochip by hybridising environmental samples confirmed the possible application of this technology for detecting and monitoring ectomycorrhizal fungi at specific sites in a routine and reproducible manner.</p

QRS detection based on morphological filter and energy envelope for applications in body sensor networks

10.1007/s11265-009-0430-8Journal of Signal Processing Systems642187-19