DETECTION OF ARTIAL FIBRILLATION DISORDER BY ECG USING DISCRETE WAVELET TRANSFORMS

Atrial fibrillation (A-fib) is the most common cardiac disorder. To efficiently treat or inhibit, an automatic detection based on electrocardiograph (ECG)monitoring is significantly required. ECG is a key function in the analysis of the heart functioning and diagnostic of diseases. Currently, a computer basedsystem is used to analyze the ECG signal. The main aim of this project is to analyze a heart malfunctions named as A-fib, using discrete wavelet transforms(DWT). The ECG signals were decomposed into time-frequency representations using DWT, and the statistical features were calculated to describe theirdistribution. The DWT detailed coefficients are used to obtain various parameters of the ECG signal such as the mean, variance, standard deviation, andentropy of the signal. An analysis had been made with these parameters of various patients with normal heart functioning and A-fib to identify the disorder.Keywords: Atrial fibrillation, Electrocardiogram, Discrete wavelet transforms

Extended phase space thermodynamics for charged and rotating black holes and Born-Infeld vacuum polarization

We investigate the critical behaviour of charged and rotating AdS black holes in d spacetime dimensions, including effects from non-linear electrodynamics via the Born-Infeld action, in an extended phase space in which the cosmological constant is interpreted as thermodynamic pressure. For Reissner-Nordstrom black holes we find that the analogy with the Van der Walls liquid-gas system holds in any dimension greater than three, and that the critical exponents coincide with those of the Van der Waals system. We find that neutral slowly rotating black holes in four space-time dimensions also have the same qualitative behaviour. However charged and rotating black holes in three spacetime dimensions do not exhibit critical phenomena. For Born-Infeld black holes we define a new thermodynamic quantity B conjugate to the Born-Infeld parameter b that we call Born-Infeld vacuum polarization. We demonstrate that this quantity is required for consistency of both the first law of thermodynamics and the corresponding Smarr relation.Comment: 23 pages, 32 figures, v2: minor changes, upgraded reference

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

PURPOSE Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning:a retrospective observational study

BACKGROUND: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction.METHODS: Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction.FINDINGS: Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36).INTERPRETATION: Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results.FUNDING: This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].</p

Bortezomib-based immunosuppression after reduced-intensity conditioning hematopoietic stem cell transplantation: randomized phase II results

Aprior phase I/II trial of bortezomib/tacrolimus/methotrexate prophylaxis after human leukocyte antigen (HLA)-mismatched reduced intensity conditioning allogeneic hematopoietic stem cell transplantation documented low acute graft-versus-host disease incidence, with promising overall and progression-free survival. We performed an open-label three-arm 1:1:1 phase II randomized controlled trial comparing grade II–IV acute graft-versus-host disease between conventional tacrolimus/methotrexate (A) versus bortezomib/tacrolimus/methotrexate (B), and versus bortezomib/sirolimus/tacrolimus (C), in reduced intensity conditioning allogeneic transplantation recipients lacking HLA-matched related donors. The primary endpoint was grade II–IV acute graft-versus-host disease incidence rate by day +180. One hundred and thirty-eight patients (A 46, B 45, C 47) with a median age of 64 years (range: 24–75), varying malignant diagnoses and disease risk (low 14, intermediate 96, high/very high 28) received 7–8/8 HLA-mismatched (40) or matched unrelated donor (98) grafts. Median follow up in survivors was 30 months (range: 14–46). Despite early immune reconstitution differences, day +180 grade II-IV acute graft-versus-host disease rates were similar (A 32.6%, B 31.1%, C 21%; P=0.53 for A vs. B, P=0.16 for A vs. C). The 2-year non-relapse mortality incidence was similar (A 14%, B 16%, C 6.4%; P=0.62), as were relapse (A 32%, B 32%, C 38%; P=0.74), chronic graft-versus-host disease (A 59%, B 60% C 55%; P=0.66), progression-free survival (A 54%, B 52%, C 55%; P=0.95), and overall survival (A 61%, B 62%, C 62%; P=0.98). Overall, the bortezomib-based regimens evaluated did not improve outcomes compared with tacrolimus/methotrexate therapy. clinicaltrials.gov Identifier: 0175438

Extremes in June rainfall during Indian summer monsoons of 2013 and 2014: Observational Analysis and Extended range prediction

The onset/progression phase of theIndian summer monsoon (ISM) is very crucial for the agricultural sector of the country as it has strong bearing on the sowing of kharif crops, which in turn affects overall food grain production and hence food security. The recent ISMs of 2013 and 2014 exhibited quite distinct progression phases. While 2013 had one of the fastest advancement in the last 70 years, 2014 witnessed a comparatively lethargic progression phase. The major difference was felt in the early monsoon month of June, with 2013 (2014) monthly rainfall being +34% (−43%) of its long period average. Observational investigations reveal that, during June 2013, the monsoon trough was very active in its normal position favouring low-level positive vorticity generation and moisture convergence, whereas the absence of monsoon trough during June 2014 facilitated the prevalence of a strong low-level anticyclonic circulation over central India hampering the northward progression of the ISM. It is found that June 2013 (2014) was associated with (i) stronger (weaker) north-south tropospheric temperature (TT) gradient with positive (negative) TT anomalies over Eurasia and north of 60°N; (ii) negative (positive) SST anomalies over the equatorial Indian Ocean, northwestern Arabian Sea and equatorial eastern Pacific; (iii) stronger (weaker) monsoonal Hadley circulation; and (iv) stronger (weaker) Walker circulation in response to the negative (positive) SST anomalies over the equatorial Pacific. The study also examines the skill of an Ensemble Prediction System (EPS) in predicting the observed contrasting behaviour during June 2013/2014 on extended range (∼15–20 days in advance) in real time. The EPS not only forecasted the observed discrepancy, but also predicted the influential role of the large-scale meteorological conditions prevalent during June 2013 (2014), thus demonstrating the remarkable skill of the EPS in predicting June extremes

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Not AvailableThe genomes of a number of species of Bacillus which can thrive in the salinity gradients (0 to 35% NaCl) of the hypersaline habitats of the Great and Little Rann of Kutch, India, were sequenced recently with a view to understanding the mechanism(s) of osmotolerance (1–5). Bacillus megaterium strain MSP20.1 (16SrRNA GenBank accession number JF802191), isolated from a saltern of the Little Rann of Kutch, India, grows optimally at a concentration of 7.5% NaCl (range, 0 to 20%) in the growth medium, at pH 7.0 and 37°C. The genome of MSP20.1 was sequenced to gain understanding of its moderate halophilism. The genome of MSP20.1 was sequenced using the Illumina HiSeq 2000 platform at Macrogen, Inc., South Korea, through Sequencher Tech Pvt., Ltd., Ahmedabad, India. Originally, a total of 69,373,018 paired-end reads with lengths of 72 nucleotides of 7,006,674,818 bases were generated, which, after filtering and removal of PCR duplicates, resulted in 61,462,320 reads of 6,207,694,320 bases.Not Availabl