Strength of ensemble learning in automatic sleep stages classification using single-channel EEG and ECG signals.

Healthy sleep plays an essential role in human daily life. Classification of sleep stages is a crucial tool for assisting physicians in diagnosing and treating sleep disorders. In this study, a strong ensemble learning model is proposed to enhance the ability of classification models in accurate sleep staging, particularly in multi-class classification.

Frame rate improvement in ultrafast coherent plane wave compounding.

Coherent plane wave compounding (CPWC), as an ultrafast ultrasound imaging technique, makes a significant breakthrough in frame rate enhancement. However, there exists a compromise between the quality of the final image and the frame rate in CPWC. In this paper, we propose an efficient method to minimize the number of required emissions, and consequently, improve the frame rate, while maintaining the image quality.

Ultrafast Plane Wave Imaging Using Tensor Completion-Based Minimum Variance Algorithm.

Objective: Coherent plane wave compounding (CPWC) imaging is an efficient technique in high-frame-rate ultrasound imaging. To improve the image quality obtained from the CPWC, the adaptive minimum variance (MV) algorithm can be used. However, the high computational complexity of this algorithm negatively affects the frame rate.

Fast Beamforming Method for Plane Wave Compounding Based on Beamspace Adaptive Beamformer and Delay-Multiply-and-Sum.

Objective: Although the use of coherent plane wave compounding is a promising technique for enabling the attainment of very high frame rate imaging, it achieves relatively low image quality because of data-independent reconstruction. Adaptive beamformers rather than delay-and-sum (DAS) conventional techniques have been proposed to improve the imaging quality. The minimum variance (MV) and delay-multiply-and-sum (DMAS) beamformers have been validated as effective in improving image quality.