Electrical Engineering, M.I.T.S, Gwalior, M.P., India.
J Healthc Eng. 2022 Apr 11;2022:9899899. doi: 10.1155/2022/9899899. eCollection 2022.
Cardiovascular health and training success can be assessed using electrocardiogram (ECG) data. For over a quarter of a century, an individual's resting heart rate is varying more. As a result, it has become the subject of inquiry and reveals the intricate relationship between the human body and its environment. The autonomic nervous system has impact on blood flow system based on the rate of heartbeats. However, heart rate variation (HRV) characteristics analysis throughout the time period has lack of physical activity information. In the presence of patient movement, ECG signal is suffering from hard artefacts. Time-varying HRV parameters can be derived from low-frequency (LF) and high-frequency (HF) domains of the correct frequency. However, sometimes it is critical to ensuring accurate detection of the R-peak position. The proposed ROIIR (reduced-order IIR) offers 8.8% improvement in peak-to-peak swing than earlier IIR filter. We present an advanced filtering algorithm that is used for R-peak detection.
心血管健康和训练效果可以通过心电图 (ECG) 数据来评估。二十五年来，个体的静息心率变化越来越大。因此，它已成为研究的主题，并揭示了人体与其环境之间的复杂关系。基于心跳的速率，自主神经系统会影响血流系统。然而，心率变异性 (HRV) 特征分析在整个时间段内缺乏身体活动信息。在患者运动时，心电图信号会受到严重的伪迹干扰。通过正确频率的低频 (LF) 和高频 (HF) 域，可以得出时变 HRV 参数。但是，有时确保准确检测 R 波峰值位置至关重要。与早期的 IIR 滤波器相比，所提出的 ROIIR（降阶 IIR）在峰峰值摆动方面提高了 8.8%。我们提出了一种用于 R 波峰值检测的先进滤波算法。
