In free-living subjects with uncomplicated obesity, chronic hyperinsulinemia is associated with a high-output, low-resistance hemodynamic state, persistent baroreflex downregulation, and episodic (postprandial) sympathetic dominance. Reversal of these changes by weight loss suggests a causal role for insulin.

Non-invasive fetal heart rate is of great relevance in clinical practice to monitor fetal health state during pregnancy. To date, however, despite significant advances in the field of electrocardiography, the analysis of abdominal fetal ECG is considered a challenging problem for biomedical and signal processing communities. This is mainly due to the low signal-to-noise ratio of fetal ECG and difficulties in cancellation of maternal QRS complexes, motion and electromyographic artefacts. In this paper we present an efficient unsupervised algorithm for fetal QRS complex detection from abdominal multichannel signal recordings combining ICA and maternal ECG cancelling, which outperforms each single method. The signal is first pre-processed to remove impulsive artefacts, baseline wandering and power line interference. The following steps are then applied: maternal ECG extraction through independent component analysis (ICA); maternal QRS detection; maternal ECG cancelling through weighted singular value decomposition; enhancing of fetal ECG through ICA and fetal QRS detection. We participated in the Physionet/Computing in Cardiology Challenge 2013, obtaining the top official scores of the challenge (among 53 teams of participants) of event 1 and event 2 concerning fetal heart rate and fetal interbeat intervals estimation section. The developed algorithms are released as open-source on the Physionet website.

Spectral analysis of cardiovascular series has been proposed as a noninvasive tool for investigating the autonomic control of the cardiovascular system. The analysis of such series during autonomic tests requires high resolution estimators that are capable to track the transients of the tests. A comparative evaluation has been made among classical (FFT based), autoregressive (both block and sequential mode) and time-frequency representation (TFR) based power spectral estimators. The evaluation has been performed on artificial data that have typical patterns of the nonstationary series. The results documented the superiority of the TFR approach when a sharp time resolution is required. Moreover, the test on a RR-like series has shown that the smoothing operation is effective for rejecting TFR cross-terms when a simple, two-three components series is concerned. Finally, the preliminary application of the selected methods to real RR interval time series obtained during some autonomic tests has shown that the TFR are capable to correctly represent the transient of the series in the joint time-frequency domain.

Anxiety and emotional sensitivity were significant predictors of 8-year cardiac mortality after AMI. Reduced HF power, a recognized marker of vagal withdrawal, increased the risk.