deploy: 30fd32f

Author: DominiqueMakowski

.doctrees/environment.pickle

@@ -456,10 +456,10 @@ <h1>Source code for neurokit2.hrv.hrv_time</h1><div class="highlight"><pre>
 <span class="sd">          Frequency (LF/HF) Ratio (Sollers et al., 2007).</span>
 <span class="sd">        * **Prc20NN**: The 20th percentile of the RR intervals (Han, 2017; Hovsepian, 2015).</span>
 <span class="sd">        * **Prc80NN**: The 80th percentile of the RR intervals (Han, 2017; Hovsepian, 2015).</span>
-<span class="sd">        * **pNN50**: The proportion of RR intervals greater than 50ms, out of the total number of</span>
-<span class="sd">          RR intervals.</span>
-<span class="sd">        * **pNN20**: The proportion of RR intervals greater than 20ms, out of the total number of</span>
-<span class="sd">          RR intervals.</span>
+<span class="sd">        * **pNN50**: The percentage of absolute differences in successive RR intervals greater than</span>
+<span class="sd">          50 ms (Bigger et al., 1988; Mietus et al., 2002).</span>
+<span class="sd">        * **pNN20**: The percentage of absolute differences in successive RR intervals greater than</span>
+<span class="sd">          20 ms (Mietus et al., 2002).</span>
 <span class="sd">        * **MinNN**: The minimum of the RR intervals (Parent, 2019; Subramaniam, 2022).</span>
 <span class="sd">        * **MaxNN**: The maximum of the RR intervals (Parent, 2019; Subramaniam, 2022).</span>
 <span class="sd">        * **TINN**: A geometrical parameter of the HRV, or more specifically, the baseline width of</span>
@@ -493,6 +493,9 @@ <h1>Source code for neurokit2.hrv.hrv_time</h1><div class="highlight"><pre>
 
 <span class="sd">    References</span>
 <span class="sd">    ----------</span>
+<span class="sd">    * Bigger Jr, J. T., Kleiger, R. E., Fleiss, J. L., Rolnitzky, L. M., Steinman, R. C., &amp; Miller,</span>
+<span class="sd">      J. P. (1988). Components of heart rate variability measured during healing of acute myocardial</span>
+<span class="sd">      infarction. The American journal of cardiology, 61(4), 208-215.</span>
 <span class="sd">    * Pham, T., Lau, Z. J., Chen, S. H. A., &amp; Makowski, D. (2021). Heart Rate Variability in</span>
 <span class="sd">      Psychology: A Review of HRV Indices and an Analysis Tutorial. Sensors, 21(12), 3998.</span>
 <span class="sd">      https://doi.org/10.3390/s21123998</span>
@@ -505,6 +508,8 @@ <h1>Source code for neurokit2.hrv.hrv_time</h1><div class="highlight"><pre>
 <span class="sd">      towards a gold standard for continuous stress assessment in the mobile environment. In</span>
 <span class="sd">      Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous</span>
 <span class="sd">      computing (pp. 493-504).</span>
+<span class="sd">    * Mietus, J. E., Peng, C. K., Henry, I., Goldsmith, R. L., &amp; Goldberger, A. L. (2002). The pNNx</span>
+<span class="sd">      files: re-examining a widely used heart rate variability measure. Heart, 88(4), 378-380.</span>
 <span class="sd">    * Parent, M., Tiwari, A., Albuquerque, I., Gagnon, J. F., Lafond, D., Tremblay, S., &amp; Falk, T.</span>
 <span class="sd">      H. (2019). A multimodal approach to improve the robustness of physiological stress prediction</span>
 <span class="sd">      during physical activity. In 2019 IEEE International Conference on Systems, Man and</span>

.doctrees/examples/bio_custom/bio_custom.doctree

@@ -469,11 +469,6 @@ <h1>Customize your Processing Pipeline<a class="headerlink" href="#customize-you
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+<img alt="../../_images/0c08c76a7b07f3fe27cd7dd31c1fcaee0f2df13a07f8313b1ef6cbba448bcc01.png" src="../../_images/0c08c76a7b07f3fe27cd7dd31c1fcaee0f2df13a07f8313b1ef6cbba448bcc01.png" />
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+<img alt="../../_images/614ce7b0ea5b2bca1ca796a1b4e9e5d90bfca9d3a52c22a82b3acbcfe97b1689.png" src="../../_images/614ce7b0ea5b2bca1ca796a1b4e9e5d90bfca9d3a52c22a82b3acbcfe97b1689.png" />
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+<img alt="../../_images/e7711d23a2d4cb6f0c704eb903e02b9c8839904f6950e56c89838dd5ac467bab.png" src="../../_images/e7711d23a2d4cb6f0c704eb903e02b9c8839904f6950e56c89838dd5ac467bab.png" />
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 <p>This doesn’t look bad :) <strong>Can you do better?</strong></p>

.doctrees/examples/bio_eventrelated/bio_eventrelated.doctree

@@ -530,7 +530,7 @@ <h2>Find Events<a class="headerlink" href="#find-events" title="Link to this hea
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+<img alt="../../_images/4f6361f3a1bdac5a161b1f0f97065defa1514812a52eed9367a5a5a035e46181.png" src="../../_images/4f6361f3a1bdac5a161b1f0f97065defa1514812a52eed9367a5a5a035e46181.png" />
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 <p>The output of <a class="reference external" href="https://neuropsychology.github.io/NeuroKit/functions/functions_events.html#events-plot">events_plot()</a> shows the corresponding events in the signal, with the blue dashed line representing a Negative event and red dashed line representing a Neutral event.</p>
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7944\4246861920.py:12: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
   ecg_mean = epoch[&quot;ECG_Rate&quot;][0:4].mean()  # Mean heart rate in the 0-4 seconds
-C:\Users\runneradmin\AppData\Local\Temp\ipykernel_2548\4246861920.py:18: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
+C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7944\4246861920.py:18: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
   rsp_rate = epoch[&quot;RSP_Rate&quot;][0:6].mean()  # Longer window for RSP that has a slower dynamic
-C:\Users\runneradmin\AppData\Local\Temp\ipykernel_2548\4246861920.py:24: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
+C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7944\4246861920.py:24: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
   scr_max = epoch[&quot;SCR_Amplitude&quot;][0:6].max()  # Maximum SCR peak
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.doctrees/examples/ecg_generate_12leads/ecg_generate_12leads.doctree

@@ -481,7 +481,7 @@ <h2>Normal Multi-lead ECG<a class="headerlink" href="#normal-multi-lead-ecg" tit
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