Sheffield Hallam University, Howard St, Sheffield, S1 1WB, UK.
Cogninet Australia, Sydney, NSW, 2010, Australia.
Comput Biol Med. 2022 Jun;145:105407. doi: 10.1016/j.compbiomed.2022.105407. Epub 2022 Mar 23.
Heart Rate Variability (HRV) is a good predictor of human health because the heart rhythm is modulated by a wide range of physiological processes. This statement embodies both challenges to and opportunities for HRV analysis. Opportunities arise from the wide-ranging applicability of HRV analysis for disease detection. The availability of modern high-quality sensors and the low data rate of heart rate signals make HRV easy to measure, communicate, store, and process. However, there are also significant obstacles that prevent a wider use of this technology. HRV signals are both nonstationary and nonlinear and, to the human eye, they appear noise-like. This makes them difficult to analyze and indeed the analysis findings are difficult to explain. Moreover, it is difficult to discriminate between the influences of different complex physiological processes on the HRV. These difficulties are compounded by the effects of aging and the presence of comorbidities. In this review, we have looked at scientific studies that have addressed these challenges with advanced signal processing and Artificial Intelligence (AI) methods.
心率变异性（HRV）是人类健康的良好预测指标，因为心率节律受到广泛的生理过程的调节。这一说法既体现了 HRV 分析所面临的挑战，也体现了它所带来的机遇。机遇源于 HRV 分析在疾病检测方面的广泛适用性。现代高质量传感器的可用性和心率信号的低数据率使得 HRV 易于测量、通信、存储和处理。然而，也存在着一些重大的障碍，阻碍了这项技术的广泛应用。HRV 信号是非平稳的和非线性的，并且对人眼来说，它们看起来像是噪声。这使得它们难以分析，实际上，分析结果也很难解释。此外，很难区分不同复杂生理过程对 HRV 的影响。这些困难因衰老的影响和合并症的存在而变得更加复杂。在这篇综述中，我们研究了利用先进的信号处理和人工智能（AI）方法来应对这些挑战的科学研究。
