Electrocardiogram (ECG) signals are among the significant physiological signals that indicate the essential properties of the human body. In recent years, the measurement of ECG signals has become more portable thanks to the increasing usage of wearable health testing technology. However, the enormous amount of signal data gathered over a long period of time does impose a heavy load on medical professionals. In addition, false alarms might occur due to the potential for the detected signal to become jumbled with noise and motion perturbations. Therefore, analyzing the quality of the measured raw ECG signal automatically is a valuable task. In this paper, we propose a new single-channel ECG signal quality assessment method that combines the Resnet network structure and the principle of self-attention to extract ECG signal features using the principle of similarity between individual QRS heartbeats within a time slice of ten seconds. In addition, an improved self-attention module is introduced into the deep neural network to learn the similarity between features. Finally, the network distinguishes between acceptable and unacceptable ECG segments. The model test results indicate that the F1-score can approach 0.954, which leads to a more accurate assessment of the ECG signal quality.
There is always a need for low-power, area-efficient VLSI (Very Large-Scale Integration) design and this need is increasing day by day. However, conventional design methods based on Metal-Oxide-Semiconductor Field Effect Transistor (MOSFET) devices and Complementary Metal-Oxide-Semiconductor Transistor (CMOS) technology cannot meet the performance requirements. The memristor, as a promising computing and memory integration device, offers a new research idea for conventional logic circuit structure and architecture innovation, given its non-volatility, scalability, low power consumption, fast switching speed, etc. This paper proposes a brief overview of the characteristics and current status of memristor-based logic circuits and analyzes their applications in numerical expression and memory. The benefits and drawbacks of various analog logic circuit structures are summarized and compared. In addition, some solution strategies for these issues are presented. Finally, this paper offers prospects for the applications of memristors in the logic implementation of large-scale memristor arrays, the novel structure of in-memory computing, and neural network computing.
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