Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.compbiomed.2022.105407 | COMPUTERS IN BIOLOGY AND MEDICINE |
Keywords | DocType | Volume |
Heart rate variability, Artificial intelligence, Computer-aided diagnosis, Patient remote monitoring | Journal | 145 |
ISSN | Citations | PageRank |
0010-4825 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oliver Faust | 1 | 0 | 0.34 |
| Wanrong Hong | 2 | 0 | 0.34 |
| Hui Wen Loh | 3 | 0 | 0.34 |
| Shuting Xu | 4 | 0 | 0.68 |
| Ru-San Tan | 5 | 2 | 1.45 |
| Subrata Chakraborty | 6 | 0 | 0.68 |
| Prabal Datta Barua | 7 | 0 | 0.34 |
| Filippo Molinari | 8 | 0 | 1.35 |
| Rajendra Acharya U | 9 | 4666 | 296.34 |