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Currently, the nonadaptive delay-and-sum (DAS) beamformer is used in medical ultrasound imaging. However, due to its data-independent nature, DAS leads to images with limited resolution and contrast. In this paper, an adaptive minimum variance (MV)-based beamformer that combines the MV and coherence factor (CF) weighting is introduced and adapted to medical ultrasound imaging. MV-adaptive beamformers can improve the image quality in terms of resolution and sidelobes by suppressing off-axis signals, while keeping onaxis ones. In addition, CF weighting can improve contrast and sidelobes by emphasizing the in-phase signals and reducing the out-of-phase ones. Combining MV and CF weighting results in simultaneous improvement of imaging resolution and contrast, outperforming both DAS and MV beamformers. In addition, because of the power of CF in reducing the focusing errors, the proposed method presents satisfactory robustness against sound velocity inhomogeneities, outperforming the regularized MV beamformer. The excellent performance of the proposed beamforming approach is demonstrated by several simulated examples.

Recently, adaptive beamforming methods have been successfully applied to medical ultrasound imaging, resulting in significant improvement in image quality compared with non-adaptive delay-and-sum (DAS) beamformers. Most of the adaptive beamformers presented in the ultrasound imaging literature are based on the minimum variance (MV) beamformer which can significantly improve the imaging resolution, although their success in enhancing the contrast has not yet been satisfactory. It is desirable for the beamformer to improve the resolution and contrast at the same time. To this end, in this paper, we have applied the eigenspace-based MV (EIBMV) beamformer to medical ultrasound imaging and have shown a simultaneous improvement in imaging resolution and contrast. EIBMV beamformer utilizes the eigenstructure of the covariance matrix to enhance the performance of the MV beamformer. The weight vector of the EIBMV is found by projecting the MV weight vector onto a vector subspace constructed from the eigenstructure of the covariance matrix. Using EIBMV weights instead of the MV ones leads to reduced sidelobes and improved contrast, without compromising the high resolution of the MV beamformer. In addition, the proposed EIBMV beamformer presents a satisfactory robustness against data misalignment resulting from steering vector errors, outperforming the regularized MV beamformer.

Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder, and highly affects the quality of human life. Currently, gold standard for OSA detection is Polysomnogram. Since this method is time consuming and cost inefficient, practical systems focus on the usage of electrocardiogram (ECG) signals for OSA detection. In this paper, a novel automatic OSA detection method using a single-lead ECG signal has been proposed. A non-linear feature extraction using Wavelet Transform (WT) coefficients obtained by an ECG signal decomposition is employed. In addition, different classification methods are investigated. ECG signals are decomposed into 8 levels using a Symlet function as a mother Wavelet function with third-order. Then, the entropy-based features including fuzzy/approximate/sample/correct conditional entropy as well as other non-linear features including interquartile range, mean absolute deviation, variance, Poincare plot and recurrence plot are extracted from WT coefficients. The best features are chosen using the automatic sequential forward feature selection algorithm. In order to assess the introduced method, 95 single-lead ECG recordings are used. SVM classifier having a RBF kernel leads to an accuracy of 94.63% (Sens: 94.43%, Spec: 94.77%) and 95.71% (Sens: 95.83%, Spec: 95.66%) for minute-by-minute and subject-by-subject classifications, respectively. The results show that applying entropy-based features for extracting hidden information of the ECG signals outperforms other available automatic OSA detection methods. The results indicate that a highly accurate OSA detection is attained by just exploiting the single-lead ECG signals. Furthermore, due to the low computational load in the proposed method, it can easily be applied to the home monitoring systems.

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