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相关概念视频

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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Discrete-time Fourier transform01:26

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The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
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Discrete Fourier Transform01:15

Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Continuous -time Fourier Transform01:11

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The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
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Electrocardiogram01:29

Electrocardiogram

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
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相关实验视频

Updated: Jul 8, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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DWT-CNNTRN:一个用于ECG分类的卷积变压器,具有离散波形变换.

Congyu Zou, Mikhael Djajapermana, Eimo Martens

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
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    概括

    一个新的深度学习模型使用混合CNN-变压器方法准确地分类心电图 (ECG). 这种高效的心血管疾病诊断工具以更少的参数提供高性能,提高了可访问性.

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    科学领域:

    • 心脏病学和人工智能研究
    • 生物医学信号处理

    背景情况:

    • 心血管疾病是全球主要的死亡原因.
    • 电心电图 (ECG) 对于诊断心脏疾病至关重要.
    • 准确和自动化的心电图分类对于临床实践至关重要.

    研究的目的:

    • 开发一种新的,高性能的自动分类器,用于12导电图记录.
    • 提高心血管疾病检测的诊断准确性和效率.
    • 创建一个计算效率高的ECG分类模型.

    主要方法:

    • 这是一个混合深度学习模型,结合了卷积神经网络 (CNN) 和变压器模块.
    • 使用ECG信号的离散波形变换作为输入特征.
    • 整合一个全球混合池层,用于特征凝结.

    主要成果:

    • 在2018年中国生理信号挑战数据集上获得了0.86的平均准确度和0.83的平均F1得分.
    • 在对特定疾病进行分类方面表现出强的表现,如左捆分支区块 (LBBB),右捆分支区块 (RBBB) 和不完整的心房膜区块 (I-AVB).
    • 该模型的参数数量与现有的单个和整体模型相比显著较小.

    结论:

    • 拟议的模型提供了一个高度准确和高效的ECG分类解决方案.
    • 它的缩小模型大小提高了自动ECG分析的可访问性,特别是在资源有限的环境中.
    • 这项工作有助于推进心血管疾病的自动诊断工具.