Heart rate variability (HRV) is a widely used measure that reflects autonomic (parasympathetic) control of the heart. HRV has been linked with attentional performance, but it is unclear to what extent resting HRV is associated with both attention and attentional maintenance. In order to address this, we calculated HRV in seventy-four young and healthy volunteers (43 men, age: 21.6±2.4), who completed the D2 Test of Attention (D2), which was used to calculate an index of Concentration Performance (CP) and a measure of attention maintenance, the coefficient of variation (CV). After accounting for the effects of sex and age on HRV, there was no significant association between HRV and CP (p = .2), but a significant relationship between HRV and CV (p = .03). Overall, our study demonstrates that attention maintenance, but not attentional performance, is associated with higher resting state HRV which suggests that attentional performance from D2 subtest-to-subtest may reflect HRV's facilitation of behaviour flexibility.

Needle visualization in the ultrasound image is essential to successfully perform the ultrasound-guided core needle biopsy. Automatic needle detection can significantly reduce the procedure time, false-negative rate, and highly improve the diagnosis. In this paper, we present a CNN-based, fully automatic method for detection of core needle in 2D ultrasound images. Adaptive moment estimation optimizer is proposed as CNN architecture. Radon transform is applied to locate the needle. The network’s model was trained and tested on the total of 619 2D images from 91 cases of breast cancer. The model has achieved an average weighted intersection over union (the weighted Jaccard Index) of 0.986, F1 Score of 0.768, and angle RMSE of 3.73°. The obtained results exceed the other solutions by at least 0.27 and 7° in case of F1 score and angle RMSE, respectively. Finally, the needle is detected in a single frame averagely in 21.6 ms on a modern PC.

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