Converts detected PPG pulses to pulse-to-pulse intervals and delegates time-, frequency-, and optional nonlinear-domain metrics to the existing HRV engine with ECG rhythm gating disabled.
Usage
pulseRateVariability(
x,
peaks = NULL,
freq = TRUE,
nonlinear = FALSE,
assay_name = NULL,
...
)Arguments
- x
A PPG
PhysioExperiment.- peaks
Optional peak table from
ppgDetectPulses().- freq
Logical; calculate frequency-domain PRV.
- nonlinear
Logical; calculate nonlinear PRV.
- assay_name
Optional raw PPG assay name.
- ...
Detection arguments passed to
ppgDetectPulses()whenpeaksisNULL.
References
Schafer A, Vagedes J (2013). How accurate is pulse rate variability as an estimate of heart rate variability? International Journal of Cardiology, 166:15-29. doi:10.1016/j.ijcard.2012.03.119
Examples
simulation <- make_ppg(n_time = 15000)
pulseRateVariability(simulation$pe, freq = FALSE)
#> $ppi
#> channel ppi_ms time_sec
#> 1 1 832 0.976
#> 2 1 832 1.808
#> 3 1 832 2.640
#> 4 1 832 3.472
#> 5 1 832 4.304
#> 6 1 832 5.136
#> 7 1 824 5.968
#> 8 1 840 6.792
#> 9 1 832 7.632
#> 10 1 832 8.464
#> 11 1 840 9.296
#> 12 1 824 10.136
#> 13 1 832 10.960
#> 14 1 832 11.792
#> 15 1 832 12.624
#> 16 1 832 13.456
#> 17 1 832 14.288
#> 18 1 832 15.120
#> 19 1 832 15.952
#> 20 1 832 16.784
#> 21 1 832 17.616
#> 22 1 832 18.448
#> 23 1 832 19.280
#> 24 1 832 20.112
#> 25 1 840 20.944
#> 26 1 824 21.784
#> 27 1 832 22.608
#> 28 1 840 23.440
#> 29 1 824 24.280
#> 30 1 832 25.104
#> 31 1 832 25.936
#> 32 1 840 26.768
#> 33 1 824 27.608
#> 34 1 840 28.432
#> 35 1 824 29.272
#> 36 1 832 30.096
#> 37 1 832 30.928
#> 38 1 832 31.760
#> 39 1 832 32.592
#> 40 1 832 33.424
#> 41 1 824 34.256
#> 42 1 840 35.080
#> 43 1 832 35.920
#> 44 1 832 36.752
#> 45 1 824 37.584
#> 46 1 840 38.408
#> 47 1 832 39.248
#> 48 1 840 40.080
#> 49 1 824 40.920
#> 50 1 824 41.744
#> 51 1 840 42.568
#> 52 1 832 43.408
#> 53 1 832 44.240
#> 54 1 840 45.072
#> 55 1 824 45.912
#> 56 1 824 46.736
#> 57 1 840 47.560
#> 58 1 832 48.400
#> 59 1 832 49.232
#> 60 1 832 50.064
#> 61 1 824 50.896
#> 62 1 840 51.720
#> 63 1 832 52.560
#> 64 1 832 53.392
#> 65 1 832 54.224
#> 66 1 832 55.056
#> 67 1 832 55.888
#> 68 1 832 56.720
#> 69 1 832 57.552
#> 70 1 832 58.384
#> 71 1 832 59.216
#> 72 1 832 60.048
#> 73 1 832 60.880
#> 74 1 832 61.712
#> 75 1 840 62.544
#> 76 1 816 63.384
#> 77 1 840 64.200
#> 78 1 832 65.040
#> 79 1 832 65.872
#> 80 1 824 66.704
#> 81 1 840 67.528
#> 82 1 832 68.368
#> 83 1 832 69.200
#> 84 1 840 70.032
#> 85 1 824 70.872
#> 86 1 840 71.696
#> 87 1 824 72.536
#> 88 1 832 73.360
#> 89 1 832 74.192
#> 90 1 832 75.024
#> 91 1 824 75.856
#> 92 1 840 76.680
#> 93 1 832 77.520
#> 94 1 832 78.352
#> 95 1 832 79.184
#> 96 1 832 80.016
#> 97 1 824 80.848
#> 98 1 840 81.672
#> 99 1 824 82.512
#> 100 1 848 83.336
#> 101 1 824 84.184
#> 102 1 832 85.008
#> 103 1 824 85.840
#> 104 1 832 86.664
#> 105 1 840 87.496
#> 106 1 832 88.336
#> 107 1 832 89.168
#> 108 1 832 90.000
#> 109 1 840 90.832
#> 110 1 824 91.672
#> 111 1 824 92.496
#> 112 1 840 93.320
#> 113 1 832 94.160
#> 114 1 832 94.992
#> 115 1 832 95.824
#> 116 1 832 96.656
#> 117 1 824 97.488
#> 118 1 840 98.312
#> 119 1 840 99.152
#> 120 1 824 99.992
#> 121 1 832 100.816
#> 122 1 840 101.648
#> 123 1 824 102.488
#> 124 1 824 103.312
#> 125 1 848 104.136
#> 126 1 824 104.984
#> 127 1 832 105.808
#> 128 1 832 106.640
#> 129 1 824 107.472
#> 130 1 840 108.296
#> 131 1 832 109.136
#> 132 1 832 109.968
#> 133 1 832 110.800
#> 134 1 832 111.632
#> 135 1 832 112.464
#> 136 1 832 113.296
#> 137 1 832 114.128
#> 138 1 824 114.960
#> 139 1 840 115.784
#> 140 1 832 116.624
#> 141 1 832 117.456
#> 142 1 824 118.288
#>
#> $time
#> channel mean_pp sdpp rmssd pnn50 mean_pr
#> 1 1 831.9437 5.676597 9.786371 0 72.12027
#>
#> $freq
#> NULL
#>
#> $nonlinear
#> NULL
#>