# HRV Advanced Parameters

*Heart Rate Variability* refers to the variability in the timing between one
heartbeat and the next. DeepAffex measures this with
`HRV_SDNN`

, which is the standard deviation of
the interval between normal heartbeats (in milliseconds).

ID | Acronym | Feature Domain | Unit | Available After (seconds) | Range | Also Needs |
---|---|---|---|---|---|---|

`HRV_ALPHA` | α | Nonlinear | - | 120 | 0 to 2 | - |

`HRV_ALPHA1` | α1 | Nonlinear | - | 120 | 0 to 3 | - |

`HRV_ALPHA2` | α2 | Nonlinear | - | 120 | -1 to 3 | - |

`HRV_HF` | HF | Frequency domain | ms² | 120 | 8 to 6319 | - |

`HRV_HFNU` | HFnu | Frequency domain | nu | 120 | 0 to 53 | - |

`HRV_LF` | LF | Frequency domain | ms² | 120 | 2 to 100 | - |

`HRV_LF_HF` | LF/HF | Frequency domain | - | 120 | 10 to 9935 | - |

`HRV_LFNU` | LFnu | Frequency domain | nu | 120 | 0 to 98 | - |

`HRV_MEAN_RRI` | Mean RRI | Time domain | ms | 30 | 128 to 946 | - |

`HRV_MHR` | Mean HR | Time domain | bpm | 30 | 64 to 529 | - |

`HRV_NN50` | NN50 | Time domain | - | 60 | 0 to 28 | - |

`HRV_PEAK_HF` | Peak HF | Frequency domain | peak | 120 | 0 to 0.4 | - |

`HRV_PEAK_HF_AR` | Peak HF (AR) | Frequency domain | peak | 120 | 0 to 0.15 | - |

`HRV_PEAK_LF` | Peak LF | Frequency domain | peak | 120 | 0 to 0.4 | - |

`HRV_PEAK_LF_AR` | Peak LF (AR) | Frequency domain | peak | 120 | 0 to 0.15 | - |

`HRV_PNN50` | pNN50 | Time domain | % | 60 | 0 to 76 | - |

`HRV_RMSSD` | RMSSD | Time domain | ms | 60 | 12 to 118 | - |

`HRV_SD1` | SD1 | Nonlinear | ms | 120 | 9 to 85 | - |

`HRV_SD2` | SD2 | Nonlinear | ms | 120 | 8 to 185 | - |

`HRV_SD1_DIV_SD2` | SD1/SD2 | Nonlinear | - | 120 | 0 to 2 | - |

`HRV_SDNN` | SDNN | Time domain | ms | 30 | 1 to 80 | - |

`HRV_TOTAL_POWER` | Total Power | Frequency domain | ms² | 30 | 0 to 3535 | - |

`HRV_VLF` | VLF | Frequency domain | ms² | 120 | 0 to 9086 | - |

## Definitions

The points in the table are defined below. Please note that in all cases, the definition is the DeepAffex Cloud's estimate.

`HRV_ALPHA`

: Scaling exponent of RR intervals over different time series based on Detrended Fluctuation Analysis.`HRV_ALPHA1`

: Short-term fluctuations from Detrended Fluctuation Analysis.`HRV_ALPHA2`

: Long-term fluctuations from Detrended Fluctuation Analysis.`HRV_HF`

: Power measured in high-frequency band.`HRV_HFNU`

: Proportion of power in the high-frequency band (0.15–0.40 Hz) to summation of powers in low- and high-frequency bands, expressed in normal units (nu)`HRV_LF_HF`

: Ratio of power in low-frequency band to power in high-frequency band.`HRV_LF`

: Power measured in low-frequency band.`HRV_LFNU`

: Proportion of power in the low-frequency band (0.04–0.15 Hz) to the summation of powers in low- and high-frequency bands, expressed in normal units (nu)`HRV_MEAN_RRI`

: Average RR interval.`HRV_MHR`

: Average heart rate.`HRV_NN50`

: Number of adjacent NN intervals that differ from each other by more than 50ms.`HRV_PEAK_HF_AR`

: Highest amplitude frequency in the high-frequency band using autoregressive power spectral density estimate using Burg's method.`HRV_PEAK_HF`

: Highest amplitude frequency in the high-frequency band using Welch's power spectral density estimate.`HRV_PEAK_LF_AR`

: Highest amplitude frequency in the very-low-frequency (VLF) band using autoregressive power spectral density estimate using Burg's method.`HRV_PEAK_LF`

: Highest amplitude frequency in the low-frequency band using Welch's power spectral density estimate.`HRV_PNN50`

: Percentage of adjacent NN intervals that differ more than 50ms.`HRV_RMSSD`

: Root mean square of successive RR interval differences.`HRV_SD1_DIV_SD2`

: Ratio of SD1 to SD2, both obtained from Poincaré Plot.`HRV_SD1`

: Standard deviation of points along the line of identity on the Poincaré Plot.`HRV_SD1`

: Standard deviation of points perpendicular to the line of identity on the Poincaré Plot.`HRV_SDNN`

: Standard deviation of NN intervals.`HRV_TOTAL_POWER`

: Summation of power measures in all frequency bands.`HRV_VLF`

: Power measured in the very-low-frequency (VLF) band.

## Background

Heart Rate Variability (HRV) is a measure of the variation in time between successive heartbeats and is influenced by various physiological factors that impact heart rate and variability, including breathing patterns, blood pressure regulation, and natural daily rhythms such as sleep-wake cycles, physical activity, and food intake. HRV can be analyzed in three main domains: time domain, frequency domain, and non-linear domain.

In the time domain, HRV is measured through various statistical measures of the inter-beat intervals, such as the standard deviation of the RR intervals (SDNN), root mean square of successive RR interval differences (RMSSD), and the percentage of successive RR intervals that differ by more than 50 ms (pNN50). In the frequency domain, HRV is analyzed by looking at the power of the ECG signal in different frequency bands, which are called VLF, LF, and HF. These bands represent different ranges of frequencies, with VLF being the lowest, and HF being the highest. In the non-linear domain, HRV is analyzed using more complex mathematical methods like DFA and entropy measures. These methods provide information on how the inter-beat intervals vary in complexity and randomness.

Overall, HRV analysis provides important information about cardiovascular function and overall health and can help guide interventions to improve overall health and well-being.

*There is no interpretation table for HRV Advanced Parameters.*

TOI is designed to provide quick HRV measurements in just 30 seconds, but it's important to recognize that HRV analysis is typically performed over much longer periods of time to obtain accurate results. In general, electrocardiograms of at least 240-300 seconds are needed to reliably measure HRV. While short-term HRV measurements such as SDNN and RMSSD can be a useful approximation of longer-term HRV, there are some limitations. RMSSD is generally considered to be a more reliable measure than SDNN and averaging multiple 10-second ECG recordings can help to improve accuracy. However, it's important to use caution when interpreting the results of TOI, as it is subject to limitations similar to other short-term HRV analysis methods.