Newtonabernathy9842

This paper presents a novel heart sound segmentation algorithm based on Temporal-Framing Adaptive Network (TFAN), including state transition loss and dynamic inference.

In contrast to previous state-of-the-art approaches, TFAN does not require any prior knowledge of the state duration of heart sounds and is therefore likely to generalize to non sinus rhythm. TFAN was trained on 50 recordings randomly chosen from Training set A of the 2016 PhysioNet/Computer in Cardiology Challenge and tested on the other 12 independent databases (2,099 recordings and 52,180 beats). And further testing of performance was conducted on databases with three levels of increasing difficulty (LEVEL-I, -II and -III).

TFAN achieved a superior F

score for all 12 databases except for 'Test-B,' with an average of 96.72%, compared to 94.56% for logistic regression hidden semi-Markov model (LR-HSMM) and 94.18% for bidirectional gated recurrent neural network (BiGRNN). Moreover, TFAN achieved an overall F

score of 99.21%, 94.17%, 91.31% on LEVEL-I, -II and -III databases respectively, compared to 98.37%, 87.56%, 78.46% for LR-HSMM and 99.01%, 92.63%, 88.45% for BiGRNN.

TFAN therefore provides a substantial improvement on heart sound segmentation while using less parameters compared to BiGRNN.

The proposed method is highly flexible and likely to apply to other non-stationary time series. Further work is required to understand to what extent this approach will provide improved diagnostic performance, although it is logical to assume superior segmentation will lead to improved diagnostics.

The proposed method is highly flexible and likely to apply to other non-stationary time series. Further work is required to understand to what extent this approach will provide improved diagnostic performance, although it is logical to assume superior segmentation will lead to improved diagnostics.

We investigated the nature of interactions between the central nervous system (CNS) and the cardiorespiratory system during sleep.

Overnight polysomnography recordings were obtained from 33 healthy individuals. The relative spectral powers of five frequency bands, three ECG morphological features and respiratory rate were obtained from six EEG channels, ECG, and oronasal airflow, respectively. The synchronous feature series were interpolated to 1 Hz to retain the high time-resolution required to detect rapid physiological variations. CNS-cardiorespiratory interaction networks were built for each EEG channel and a directionality analysis was conducted using multivariate transfer entropy. Finally, the difference in interaction between Deep, Light, and REM sleep (DS, LS, and REM) was studied.

Bidirectional interactions existed in central-cardiorespiratory networks, and the dominant direction was from the cardiorespiratory system to the brain during all sleep stages. Sleep stages had evident influence on these interactions, with the strength of information transfer from heart rate and respiration rate to the brain gradually increasing with the sequence of REM, LS, and DS. Furthermore, the occipital lobe appeared to receive the most input from the cardiorespiratory system during LS. Finally, different ECG morphological features were found to be involved with various central-cardiac and cardiac-respiratory interactions.

These findings reveal detailed information regarding CNS-cardiorespiratory interactions during sleep and provide new insights into understanding of sleep control mechanisms.

Our approach may facilitate the investigation of the pathological cardiorespiratory complications of sleep disorders.

Our approach may facilitate the investigation of the pathological cardiorespiratory complications of sleep disorders.

Musculoskeletal models play an important role in surgical planning and clinical assessment of gait and movement. Faster and more accurate simulation of muscle paths in such models can result in better predictions of forces and facilitate real-time clinical applications, such as rehabilitation with real-time feedback. We propose a novel and efficient method for computing wrapping paths across arbitrary surfaces, such as those defined by bone geometry.

A muscle path is modeled as a massless, frictionless elastic strand that uses artificial forces, applied independently of the dynamic simulation, to wrap tightly around intervening obstacles. Contact with arbitrary surfaces is computed quickly using a distance grid, which is interpolated quadratically to provide smoother results.

Evaluation of the method demonstrates good accuracy, with mean relative errors of 0.002 or better when compared against simple cases with exact solutions. The method is also fast, with strand update times of around 0.5 msec for a variety of bone shaped obstacles.

Our method has been implemented in the open source simulation system ArtiSynth (www.artisynth.org) and helps solve the problem of muscle wrapping around bones and other structures.

Muscle wrapping on arbitrary surfaces opens up new possibilities for patient-specific musculoskeletal models where muscle paths can directly conform to shapes extracted from medical image data.

Muscle wrapping on arbitrary surfaces opens up new possibilities for patient-specific musculoskeletal models where muscle paths can directly conform to shapes extracted from medical image data.

To assess the effect of the electro-magnetic coupling of endovascular stents on their RF heating potential in MRI.

A custom-built electro-optic E-field probe is used to perform measurements of the scattered E-field at a distance of 2 mm to stent samples with submillimeter resolution. Various combinations of stent lengths are measured at 124 MHz (3T MRI Larmor frequency) with varying gap and overlap between the stents, with and without stent coating, and with distilled water and saline solution as surrounding media. The results are compared to theoretically derived E-field distributions.

At an overlap of 10 mm the E-field pattern of two stents collapses to a single dipole indicating excellent coupling between the stents. E-field intensities substantially increase/decrease up to 5-fold/2.5-fold if the total length of the two combined stents is closer/further away from the resonance length of the single stents. EPZ015666 clinical trial Stent coating and conductivity of the surrounding medium strongly influence the E-field patterns of overlapping stents.