No abstract

One of the important tasks in the operating room is monitoring the depth of anesthesia (DoA) during surgery, and noninvasive techniques are very popular. Hence, we propose a new scheme for DoA monitoring considering the time-frequency analysis of electroencephalography (EEG) signals and GLCM features extracted from them. To this end, at first, the time-frequency map (TFM) of each channel of each EEG is computed by smoothed pseudo-Wigner–Ville distribution (SPWVD), where the EEG signal used in this paper is recorded in 15 channels. After that, we consider the gray-level co-occurrence matrix (GLCM) to obtain the content of TFM, and after that, four features such as homogeneity, correlation, energy, and contrast are obtained for each GLCM. Finally, after the selection of efficient features using the minimum redundancy maximum relevance (MRMR) method, the K-nearest neighbor (KNN) classifier is utilized to determine the DoA. Here, we consider the three states, namely, deep hypnotic, surgical anesthesia, and sedation and awake states according to bispectral index (BIS), and each EEG epoch is classified to these states. We also employ data augmentation to enhance the training phase and increase accuracy. We obtain the accuracy and confusion matrix of the proposed method. We also analyze the effects of a number of gray levels of GLCM, distance measure in KNN classifier, and parameters of data augmentation on the performance of the proposed method. Results indicate the efficiency of the proposed method to determine the DoA during surgery.

A single-inductor multiple-input multiple-output converter is proposed in this paper that can be used in low-power systems due to low output current and voltage. This converter is implemented discretely, and only one microcontroller is employed to control the system. The unique zero-current switching (ZCS) technique considered in this paper is such that only by reading the inductor’s left-side voltage the optimal value of the inductor discharge duty cycle is determined. This method can be generalized to low-power and high-power converters, whether implemented and designed as discrete or integrated. This converter works in discontinuous conduction mode. It uses pulse width modulation control and the time-multiplexing control method, which makes the system have high efficiency and makes the cross-regulation problem between the converter’s outputs tiny. The control algorithm considered in this converter is digital, which determines the optimal charge and discharge duty cycles. Also, the switching frequency of this converter is constant, relatively low, and equal to 5[Formula: see text]kHz. The efficiency of this converter has reached 91.6% by using the ZCS technique and other mentioned control methods.