Fiducial points extraction and charactericwaves detection in ECG signal using a model-based bayesian framework

International audienceThe automatic detection of Electrocardiogram (ECG) waves is important to cardiac disease diagnosis. A good perfor- mance of an automatic ECG analyzing system depends heavily upon the accurate and reliable detection of QRS complex, as well as P and T waves. In this paper, we propose an efficient method for extraction of characteristic points of ECG signal. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the new EKF structure was constructed. Quantitative and qualitative evaluations of the proposed method have been done on Physionet QT database (QTDB). This method is also compared with another EKF approach (EKF17). Results show that the proposed method can detect fiducial points of ECG precisely and mean and standard deviation of estimation error do not exceed two samples (8 msec)

ECG Fiducial Points Extraction by Extended Kalman Filtering

International audienceMost of the clinically useful information in Electrocardiogram (ECG) signal can be obtained from the intervals, amplitudes and wave shapes (morphologies). The automatic detection of ECG waves is important to cardiac disease diagnosis. In this paper, we propose an efficient method for extraction of characteristic points of ECG. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the new EKF structure was constructed. Quantitative and qualitative evaluations of the proposed method have been done on Physionet QT database (QTDB). This method is also compared with a method based on Partially Collapsed Gibbs Sampler (PCGS). Results show that the proposed method can detect fiducial points of ECG precisely and mean of estimation error of all FPs (except Ton) do not exceed five samples (20 msec)

T wave alternans detection in ECG using extended kalman filter and dualrate EKF

International audienceT Wave Alternans (TWA) is considered as an indicator of Sudden Cardiac Death (SCD). In this paper for TWA detection, a method based on a nonlinear dynamic model is presented. For estimating the model parameters, we use an Extended Kalman Filter (EKF). We propose EKF6 and dualrate EKF6 approaches. Dualrate EKF is suitable for modeling the states which are not updated in all time instances. Quantitative and qualitative evaluations of the proposed method have been done on TWA challenge database. We compare our method with that proposed by Sieed et al. in TWA challenge 2008. We also compare our method with our previousproposed approach (EKF25-4obs). Results show that the proposed method can detect peak position and amplitude of T waves in ECG precisely. Mean and standard deviation of estimation error of our method for finding position of T waves do not exceed four samples (8 msec)

T wave alternans detection in ECG using extended kalman filter and dualrate EKF

International audienceT Wave Alternans (TWA) is considered as an indicator of Sudden Cardiac Death (SCD). In this paper for TWA detection, a method based on a nonlinear dynamic model is presented. For estimating the model parameters, we use an Extended Kalman Filter (EKF). We propose EKF6 and dualrate EKF6 approaches. Dualrate EKF is suitable for modeling the states which are not updated in all time instances. Quantitative and qualitative evaluations of the proposed method have been done on TWA challenge database. We compare our method with that proposed by Sieed et al. in TWA challenge 2008. We also compare our method with our previousproposed approach (EKF25-4obs). Results show that the proposed method can detect peak position and amplitude of T waves in ECG precisely. Mean and standard deviation of estimation error of our method for finding position of T waves do not exceed four samples (8 msec)

Selection of Efficient Features for Discrimination of Hand Movements from MEG Using a BCI Competition IV Data Set

The aim of a brain–computer interface (BCI) system is to establish a new communication system that translates human intentions, reflected by measures of brain signals such as magnetoencephalogram (MEG), into a control signal for an output device. In this paper, an algorithm is proposed for discriminating MEG signals, which were recorded during hand movements in four directions. These signals were presented as data set 3 of BCI competition IV. The proposed algorithm has four main stages: pre-processing, primary feature extraction, the selection of efficient features, and classification. The classification stage was a combination of linear SVM and linear discriminant analysis classifiers. The proposed method was validated in the BCI competition IV, where it obtained the best result among BCI competitors: a classification accuracy of 59.5 and 34.3% for subject 1 and subject 2 on the test data respectively

Identification of Selective Signatures Associated with Resistance to Johne’s Disease (JD) in Goat Breeds

Introduction: Paratuberculosis, or Johne’s disease, is a chronic, granulomatous, gastrointestinal tract disease of goat and other ruminants caused by the bacterium Mycobacterium avium ssp. paratuberculosis (MAP). The clinical signs of disease in goat are pipestream diarrhea, weight loss, and edema due to hypoproteinemia caused by protein-losing enteropathy. Knowledge concerning genetics of susceptibility to MAP infection can contribute to disease control programs by facilitating genetic selection for a less susceptible population to reduce incidence of infection in the future. The opportunity for genetic improvement in susceptibility to infection is evidenced by estimates of heritability of MAP infection ranging from 0.03 to 0.28 (Kirkpatrick and Lett, 2018). Domestication and selection has significantly changed the behavioral and phenotypic traits in modern domestic animals. The selection of animals by humans left detectable signatures on the genome of modern goat. The identification of these signals can help us to improve the genetic characteristics of economically important traits in goat. Over the last decade, interest in detection of genes or genomic regions that are targeted by selection has been growing. Identifying signatures of selection can provide valuable insights about the genes or genomic regions that are or have been under selection pressure, which in turn leads to a better understanding of genotype-phenotype relationships. The aim of this study was to identify the selection signatures using the unbiased Theta method associated with resistance to Johne’s disease in two Italian goat breeds.Materials and Methods: The work described here is a case–control association study using the Illumina Caprine SNP50 BeadChip to unravel the genes involved in susceptibility of goats to Johne’s disease. Goats in herds with a high occurrence of Johne's disease were classified as healthy or infected based on the level of serum antibodies against MAP, and 331 animals were selected for the study. For the Siriana breed 174 samples (87 cases and 87 controls) were selected from 14 herds and for the Jonica breed 157 samples (77 cases and 80 controls) were selected from 10 herds. Cases were defined as animals serologically positive for MAP by ELISA with a sample to positive ratio (S/P) higher than 0.7 and MAP negative animals had a S/P lower than 0.6. Positive animals were tested twice with the ID Screen Paratuberculosis confirmation test. The 331 samples were genotyped using the Illumina GoatSNP50 BeadChip. SNP missing 5% of data, with MAF of <1% and Hardy–Weinberg equilibrium p-values <10−6 were removed. The genotyping efficiency for samples was also verified, and samples with more than 5% missing data were removed. Grouping was done to infer selection signatures based on FST statistic. Bioinformatics inquiries were conducted employing the Ensembl database (Cunningham et al., 2022), specifically for caprine genes (assembly ARS1). The aim was to pinpoint potential candidate genes that have either been previously reported in, or are situated within the genomic regions encompassing the peak of absolute extreme FST values. In this context, regions corresponding to the top and bottom 0.01% of acquired positive and negative FST scores were earmarked as areas undergoing selection. The identification of genes was executed through the application of a 250 Kb window both upstream and downstream of each core SNP.Results and Discussion: By applying a threshold at the 99.90 percentile of the obtained Theta (θ) values, a total of 13 distinct genomic regions were identified in the Jonica breed. These regions were situated across chromosomes 1, 5, 7 (in 2 regions), 8, 9 (in 2 regions), 11, 16, 17, 18, and 20 (in 2 regions). Similarly, in the Siriana breed, genomic regions were pinpointed on chromosomes 3, 5 (in 2 regions), 10, 12, 16, 17, 18, 23, 24, and 29. Further exploration through bioinformatics tools brought to light the overlap of these genomic regions with genes associated with the immune system, disease resistance, bacterial infection resilience, response to oxidative stress, and tumor suppression. The study population size is relatively modest, predominantly due to the intricacy of procuring a substantial volume of blood samples from goats within commercial herds that have been diagnosed with JD and are poised for culling. It's worth noting that JD diagnosis and culling procedures are not infallible preventive measures. The gradual progression of the disease often leads to late-stage diagnosis, allowing subclinical goats to intermittently excrete MAP in the environment. As the infection and disease progress, the fecal shedding of MAP increases and contributes to its horizontal transmission. In combination with genetic improvement (innate protection), vaccination (acquired protection) will support eradicating this incurable disease.Conclusions: To conclude, the findings of this study hold potential significance as they offer valuable insights for identifying genomic regions and subsequently, the genes that influence Johne's disease in goats. Nonetheless, additional research endeavors are essential to enhance and validate these outcomes. Utilizing a more extensive sample size, incorporating whole-genome sequencing, and implementing high-density genotyping are imperative steps to further refine and strengthen these findings

Unsupervised Cross-Subject BCI Learning and Classification using Riemannian Geometry

International audienceThe inter-subject variability poses a challenge in cross-subject Brain-Computer Interface learning and classification. As a matter of fact, in cross-subject learning not all available subjects may improve the performance on a test subject. In order to address this problem we propose a subject selection algorithm and we investigate the use of this algorithm in the Riemannian geometry classification framework. We demonstrate that this new approach can significantly improve cross-suject learning without the need of any labeled data from test subjects