SummaryBackgroundLarge-scale and contemporary population-based studies of heart failure incidence are needed to inform resource planning and research prioritisation but current evidence is scarce. We aimed to assess temporal trends in incidence and prevalence of heart failure in a large general population cohort from the UK, between 2002 and 2014.MethodsFor this population-based study, we used linked primary and secondary electronic health records of 4 million individuals from the Clinical Practice Research Datalink (CPRD), a cohort that is representative of the UK population in terms of age and sex. Eligible patients were aged 16 years and older, had contributed data between Jan 1, 2002, and Dec 31, 2014, had an acceptable record according to CPRD quality control, were approved for CPRD and Hospital Episodes Statistics linkage, and were registered with their general practice for at least 12 months. For patients with incident heart failure, we extracted the most recent measurement of baseline characteristics (within 2 years of diagnosis) from electronic health records, as well as information about comorbidities, socioeconomic status, ethnicity, and region. We calculated standardised rates by applying direct age and sex standardisation to the 2013 European Standard Population, and we inferred crude rates by applying year-specific, age-specific, and sex-specific incidence to UK census mid-year population estimates. We assumed no heart failure for patients aged 15 years or younger and report total incidence and prevalence for all ages (>0 years).FindingsFrom 2002 to 2014, heart failure incidence (standardised by age and sex) decreased, similarly for men and women, by 7% (from 358 to 332 per 100 000 person-years; adjusted incidence ratio 0·93, 95% CI 0·91–0·94). However, the estimated absolute number of individuals with newly diagnosed heart failure in the UK increased by 12% (from 170 727 in 2002 to 190 798 in 2014), largely due to an increase in population size and age. The estimated absolute number of prevalent heart failure cases in the UK increased even more, by 23% (from 750 127 to 920 616). Over the study period, patient age and multi-morbidity at first presentation of heart failure increased (mean age 76·5 years [SD 12·0] to 77·0 years [12·9], adjusted difference 0·79 years, 95% CI 0·37–1·20; mean number of comorbidities 3·4 [SD 1·9] vs 5·4 [2·5]; adjusted difference 2·0, 95% CI 1·9–2·1). Socioeconomically deprived individuals were more likely to develop heart failure than were affluent individuals (incidence rate ratio 1·61, 95% CI 1·58–1·64), and did so earlier in life than those from the most affluent group (adjusted difference −3·51 years, 95% CI −3·77 to −3·25). From 2002 to 2014, the socioeconomic gradient in age at first presentation with heart failure widened. Socioeconomically deprived individuals also had more comorbidities, despite their younger age.InterpretationDespite a moderate decline in standardised incidence of heart failure, the burden of heart failure in the UK is increasing, and is now si...

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Infant faces elicit early, specific activity in the orbitofrontal cortex (OFC), a key cortical region for reward and affective processing. A test of the causal relationship between infant facial configuration and OFC activity is provided by naturally occurring disruptions to the face structure. One such disruption is cleft lip, a small change to one facial feature, shown to disrupt parenting. Using magnetoencephalography, we investigated neural responses to infant faces with cleft lip compared with typical infant and adult faces. We found activity in the right OFC at 140 ms in response to typical infant faces but diminished activity to infant faces with cleft lip or adult faces. Activity in the right fusiform face area was of similar magnitude for typical adult and infant faces but was significantly lower for infant faces with cleft lip. This is the first evidence that a minor change to the infant face can disrupt neural activity potentially implicated in caregiving.

In this paper, the performance of traditional variance-based method for detection of epileptic seizures in EEG signals are compared with various methods based on nonlinear time series analysis, entropies, logistic regression,discrete wavelet transform and time frequency distributions.We noted that variance-based method in compare to the mentioned methods had the best result (100%) applied on the same database.

Respiratory sounds are always contaminated by heart sound interference. An essential preprocessing step in some of the heart sound cancellation methods is localizing primary heart sound components. Singular spectrum analysis (SSA), a powerful time series analysis technique, is used in this paper. Despite the frequency overlap of the heart and lung sound components, two different trends in the eigenvalue spectra are recognizable, which leads to find a subspace that contains more information about the underlying heart sound. Artificially mixed and real respiratory signals are used for evaluating the performance of the method. Selecting the appropriate length for the SSA window results in good decomposition quality and low computational cost for the algorithm. The results of the proposed method are compared with those of well-established methods, which use the wavelet transform and entropy of the signal to detect the heart sound components. The proposed method outperforms the wavelet-based method in terms of false detection and also correlation with the underlying heart sounds. Performance of the proposed method is slightly better than that of the entropy-based method. Moreover, the execution time of the former is significantly lower than that of the latter.

In recent years the study of the intrinsic brain dynamics in a relaxed awake state in the absence of any specific task has gained increasing attention, as spontaneous neural activity has been found to be highly structured at a large scale. This so called resting-state activity has been found to be comprised by nonrandom spatiotemporal patterns and fluctuations, and several Resting-State Networks (RSN) have been found in BOLD-fMRI as well as in MEG signal power envelope correlations. The underlying anatomical connectivity structure between areas of the brain has been identified as being a key to the observed functional network connectivity, but the mechanisms behind this are still underdetermined. Theoretical large-scale brain models for fMRI data have corroborated the importance of the connectome in shaping network dynamics, while the importance of delays and noise differ between studies and depend on the models' specific dynamics. In the current study, we present a spiking neuron network model that is able to produce noisy, distributed alpha-oscillations, matching the power peak in the spectrum of group resting-state MEG recordings. We studied how well the model captured the inter-node correlation structure of the alpha-band power envelopes for different delays between brain areas, and found that the model performs best for propagation delays inside the physiological range (5-10 m/s). Delays also shift the transition from noisy to bursting oscillations to higher global coupling values in the model. Thus, in contrast to the asynchronous fMRI state, delays are important to consider in the presence of oscillation.

Objectives To determine the subgroup specific associations between usual blood pressure and risk of peripheral arterial disease, and to examine the relation between peripheral arterial disease and a range of other types of vascular disease in a large contemporary cohort. Design Cohort study. Setting Linked electronic health records from 1990 to 2013 in the United Kingdom. Participants 4 222 459 people aged 30-90 years, registered at a primary care practice for at least one year and with a blood pressure measurement. Main outcome measures Time to first diagnosis of new onset peripheral arterial disease and time to first diagnosis of 12 different vascular events. Results A 20 mm Hg higher than usual systolic blood pressure was associated with a 63% higher risk of peripheral arterial disease (hazard ratio 1.63, 95% confidence interval 1.59 to 1.66). The strength of the association declined with increasing age and body mass index (P<0.001 for interaction) but was not modified by sex or smoking status. Peripheral arterial disease was associated with an increased risk of 11 different vascular events, including ischaemic heart disease (1.68, 1.58 to 1.79), heart failure (1.63, 1.52 to 1.75), aortic aneurysm (2.10, 1.79 to 2.45), and chronic kidney disease (1.31, 1.25 to 1.38), but not haemorrhagic stroke. The most common initial vascular event among those with peripheral arterial disease was chronic kidney disease (24.4% of initial events), followed by ischaemic heart disease (18.5% of initial events), heart failure (14.7%), and atrial fibrillation (13.2%). Overall estimates from this cohort were consistent with those derived from traditional studies when we pooled the findings in two meta-analyses. Conclusions Raised blood pressure is a strong risk factor for peripheral arterial disease in a range of patient subgroups. Furthermore, clinicians should be aware that those with established peripheral arterial disease are at an increased risk of a range of other vascular events, including chronic kidney disease, ischaemic heart disease, heart failure, atrial fibrillation, and stroke.