ECG waveform generation from radar signals: A deep learning perspective.

Cardiovascular diagnostics relies heavily on the ECG (ECG), which reveals significant information about heart rhythm and function. Despite their significance, traditional ECG measures employing electrodes have limitations. As a result of extended electrode attachments, patients may experience skin irritation or pain, and motion artifacts may interfere with signal accuracy.

MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals.

Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG's usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e.

A comparative study of the alpha-subdomains of bovine and human alpha-lactalbumin reveals key differences that correlate with molten globule stability.

The alpha-lactalbumins form stable molten globule states under a range of conditions, with the low pH form being the best characterized. The stability of the molten globule varies among different members of this family, but the origin of the stability difference is not clear. We compare the folding and stability of alpha-subdomain constructs of human and bovine alpha-lactalbumin.

Protein dissection experiments reveal key differences in the equilibrium folding of alpha-lactalbumin and the calcium binding lysozymes.

The alpha-lactalbumins and c-type lysozymes have virtually identical structure but exhibit very different folding behavior. All alpha-lactalbumins form a well populated molten globule state, while most of the lysozymes do not. alpha-Lactalbumin consists of two subdomains, and the alpha-subdomain is considerably more structured in the molten globule state than the beta-subdomain.