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相关概念视频

ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

407
An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
407

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通过使用FFT-Based Scoring和浅层神经网络从部光电显微镜估计心率.

Rawan S Abdulsadig, Esther Rodriguez-Villegas

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    概括

    这项研究引入了一种新方法,使用部光多缩图 (PPG) 信号和人工智能来准确估计心率. 这种新的方法可以实现临床上可接受的准确性,用于持续监测心率.

    科学领域:

    • 生物医学工程 生物医学工程
    • 信号处理 信号处理
    • 医疗保健中的人工智能

    背景情况:

    • 持续监测心率对于评估生命体征至关重要.
    • 现有的监控设备可能由于传感模式或身体位置而受到信号采集的限制.
    • 对于准确和用户友好的心率估计系统的需求是显著的.

    研究的目的:

    • 开发和评估一种新的方法,用于估计使用部光电显微镜 (PPG) 信号的心率.
    • 用FT-Based评分和浅层神经网络来评估拟议方法的准确性.
    • 确定开发的心率监测系统的临床可接受性.

    主要方法:

    • 利用从部获取的光电显微镜 (PPG) 信号.
    • 实施了一种新的FFT-Based评分技术.
    • 采用浅层神经网络进行心率估计.
    • 使用根平均平方误差 (RMSE),平均绝对误差 (MAE) 和误差标准偏差 (STD) 评估性能.

    主要成果:

    • 在所有数据中,平均RMSE为3.13±4.66和MAE为1.96±3.38.
    • 在排除异常值数据后,已证明精度提高,平均RMSE为1.55±1.43和MAE为0.83±0.86.

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  • 新的FT-Based评分与浅层神经网络相结合,显示出具有竞争力的性能.

    • 结论:

    • 部PPG信号可以有效地用于心率估计.
    • 拟议的FT-Based评分和浅层神经网络方法为准确的心率监测提供了一个有前途的方法.
    • 这种技术有可能开发出易于使用和临床上可接受的HR监控系统.