{ "cells": [ { "cell_type": "markdown", "id": "af36102a", "metadata": {}, "source": [ "(hrv)=\n", "# Heart rate variability\n", "Author: Nicolas Legrand " ] }, { "cell_type": "code", "execution_count": 1, "id": "b6e4e605", "metadata": { "tags": [ "hide-input" ] }, "outputs": [], "source": [ "%%capture\n", "import sys\n", "if 'google.colab' in sys.modules:\n", " !pip install systole" ] }, { "cell_type": "code", "execution_count": 2, "id": "0b2009aa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " \n", " Loading BokehJS ...\n", "
\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " const force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", "const JS_MIME_TYPE = 'application/javascript';\n", " const HTML_MIME_TYPE = 'text/html';\n", " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " const CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " const script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " const cell = handle.cell;\n", "\n", " const id = cell.output_area._bokeh_element_id;\n", " const server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd_clean, {\n", " iopub: {\n", " output: function(msg) {\n", " const id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd_destroy);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " const output_area = handle.output_area;\n", " const output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " const bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " const script_attrs = bk_div.children[0].attributes;\n", " for (let i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " const toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? 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\\n\"+\n \"

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\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"1002\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "from systole.detection import ecg_peaks\n", "from systole import import_dataset1\n", "\n", "from bokeh.io import output_notebook\n", "from bokeh.plotting import show\n", "from bokeh.layouts import row, column\n", "\n", "output_notebook()" ] }, { "cell_type": "markdown", "id": "050d89d5", "metadata": {}, "source": [ "After having preprocessed the ECG or PPG signal, performed peaks detection and artefacts correction, we discuss here the extraction of heart rate variability (**HRV**) indices.\n", "\n", "A healthy heart, and a healthy brain-heart connection, does not result in a stable and never-changing cardiac activity. On the contrary, the heart rate variability (the amount of change from one RR interval to the next) can be largely different across individuals and across time for the same individual. A considerable literature has documented the robust relations between various HRV indices and both physical and mental health (see {cite:p}`2009:thayer`). Extracting these indices can therefore be one of the first steps in the process of analysing and quantifying the cardiac signal. \n", "\n", "The heart rate variability indices are often split into three different sub-domain: *1.* the time domain, *2.* the frequency domain and *3.* the nonlinear domain (see {cite:p}`2021:pham` for review). The rationale of this repartition consist mostly in the nature of the statistical and mathematical operations performed to compute the indices, and the interpretation you can make of it. While the time domain looks at the distribution of the RR intervals as a whole and will examine different aspects of its spread and variance, the frequency domain interpolate the RR time-series and will extract the power of different frequency bands of interest. The nonlinear approach capitalizes on the notion that the variations found in RR time series can often be nonlinear and uses mathematical derivation from probability and chaos theories to quantify its chaotic/flatness dimension.\n", "\n", "[Systole](https://LegrandNico.github.io/systole/#) only implement a small number of all the HRV indices that have been proposed in the literature. They are, however, the most widely used, the most often reported and, for the most part, the most interpretable ones. It is also worth noting that high colinearity exists among all the HRV variables, and they can often be reduced to a few significant dimensions.\n", "\n", "HRV analysis quantifies the cardiac variability for a given recording taken as a whole. The recommendation for this kind of analysis is that the signal should be at least 5 minutes long ideally, and not shorter than 2 minutes (even if recording as short as 10 seconds, aka ultra short term HRV, can give reliable results for some domains (e.g. {cite:p}`2015:munoz`). On the other side, some indices from the frequency domain focusing on very slow fluctuation can require recording of 24 hours or more to be estimated reliably." ] }, { "cell_type": "code", "execution_count": 3, "id": "2b8d3caa", "metadata": { "tags": [ "hide-output" ] }, "outputs": [], "source": [ "# Import ECg recording\n", "signal = import_dataset1(modalities=['ECG'], disable=True).ecg.to_numpy()\n", "\n", "# R peaks detection\n", "signal, peaks = ecg_peaks(signal=signal, method='pan-tompkins', sfreq=1000)" ] }, { "cell_type": "markdown", "id": "ded41bee", "metadata": {}, "source": [ "In Systole, the HRV indices can be found in the `systole.hrv` submodule. For most of the indices, it can be imported individually, such as:" ] }, { "cell_type": "code", "execution_count": 4, "id": "ebc8166d", "metadata": {}, "outputs": [], "source": [ "from systole.hrv import rmssd" ] }, { "cell_type": "markdown", "id": "e89eba1f", "metadata": {}, "source": [ "This will import the RMSSD function that will compute the *Root Mean Square of the Successive Differences*, which is an often-used indice from the time domain dimension that is known to reflect the short-term heart rate variability, which evolves under the influence of the parasympathetic nervous system. Following [Systole](https://LegrandNico.github.io/systole/#)'s API, all the HRV functions have an `input_type` parameter that will let you compute the indices using either boolean `peaks` vectors, integer `peaks_idx` vectors, or the float RR interval vectors itself in milliseconds (`rr_ms`) or seconds (`rr_s`)." ] }, { "cell_type": "code", "execution_count": 5, "id": "50bd3082", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "59.21645574070569" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rmssd(peaks, input_type=\"peaks\")" ] }, { "cell_type": "markdown", "id": "68de0079", "metadata": {}, "source": [ "This approach will be more computationally efficient when working on a small number of indices as it allows to only retrieve some of them from the RR intervals. Other uses more focused on feature extraction can require to access all the possible indices or all of them in a specific domain. The `time_domain()`, `frequency_domain()` and `nonlinear_domain()` functions will return a data frame containing indices from the time, frequency or nonlinear domain respectively. The `all_domain()` function can be used to wrap all of this method in a single call and compute all the HRV metrics currently implemented in Systole." ] }, { "cell_type": "markdown", "id": "fed1bedf", "metadata": {}, "source": [ "## Time domain" ] }, { "cell_type": "code", "execution_count": 6, "id": "a4c54c14", "metadata": {}, "outputs": [], "source": [ "from systole.hrv import time_domain\n", "\n", "# Extract time domain heart rate variability\n", "hrv_df = time_domain(peaks, input_type='peaks')" ] }, { "cell_type": "markdown", "id": "4da914d5", "metadata": {}, "source": [ "The data frame output is in the long format as it is more convenient to append it in a larger summary variable for a large number of groups/session/participants. Here we simply pivot it so it renders more nicely in the notebook:" ] }, { "cell_type": "code", "execution_count": 7, "id": "35095aaf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MetricMaxBPMMaxRRMeanBPMMeanRRMedianBPMMedianRRMinBPMMinRRRMSSDSDNNSDSDnn50pnn50
Values184.6153851040.076.107736793.3243875.471698795.057.692308325.059.21645660.85582759.231183636.032.868217
\n", "
" ], "text/plain": [ "Metric MaxBPM MaxRR MeanBPM MeanRR MedianBPM MedianRR \\\n", "Values 184.615385 1040.0 76.107736 793.32438 75.471698 795.0 \n", "\n", "Metric MinBPM MinRR RMSSD SDNN SDSD nn50 pnn50 \n", "Values 57.692308 325.0 59.216456 60.855827 59.231183 636.0 32.868217 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.pivot_table(hrv_df, values='Values', columns='Metric')" ] }, { "cell_type": "markdown", "id": "1f4292c8", "metadata": {}, "source": [ "## Frequency domain" ] }, { "cell_type": "markdown", "id": "3a940f7d", "metadata": {}, "source": [ "Using a similar approach, indices from the frequency domains can be extracted from RR intervals time-series. Here, we demonstrate how to plot and render an output table for frequency domain indices. Systole includes tables functions that can print the table of results using either [tabulate](https://github.com/astanin/python-tabulate) or [Bokeh](https://docs.bokeh.org/en/latest/index.html) for better rendering in the notebook. Those are located in the `systole.plots` submodule. Here we use [tabulate](https://github.com/astanin/python-tabulate) (note that it is called inside a print function)." ] }, { "cell_type": "code", "execution_count": 8, "id": "67e0fa3b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================= ============ ============= =========== ==============\n", "Frequency Band Peaks (Hz) Power (ms²) Power (%) Power (n.u.)\n", "================= ============ ============= =========== ==============\n", "VLF (0-0.04 Hz) 0.0078 1441.6998 56.7621\n", "LF (0.04-0.15 Hz) 0.0938 522.1761 20.5589 47.5484\n", "HF (0.15-0.4 Hz) 0.3555 576.0226 22.6790 52.4516\n", "Total 2539.8985\n", "LF/HF 0.9065\n", "================= ============ ============= =========== ==============\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from systole.plots import plot_frequency\n", "from systole.reports import frequency_table\n", "\n", "# Print frequency table\n", "print(\n", " frequency_table(peaks, input_type='peaks', backend=\"tabulate\")\n", ")\n", "\n", "# Plot PDS using Matplotlib\n", "plot_frequency(peaks, input_type='peaks');" ] }, { "cell_type": "markdown", "id": "af78e820", "metadata": {}, "source": [ "## Nonlinear domain\n", "The nonlinear domain indices are accesible using the same API. Here, we demonstrate how to report it using [Bokeh](https://docs.bokeh.org/en/latest/index.html) only." ] }, { "cell_type": "code", "execution_count": 9, "id": "50f4b841", "metadata": {}, "outputs": [], "source": [ "from systole.plots import plot_poincare\n", "from systole.reports import nonlinear_table" ] }, { "cell_type": "code", "execution_count": 10, "id": "c7e8c9c9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function embed_document(root) {\n", " const docs_json = 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input_type='peaks', backend='bokeh'),\n", " nonlinear_table(peaks, input_type='peaks', backend='bokeh')\n", " )\n", ")" ] }, { "cell_type": "markdown", "id": "e6f5b7d8", "metadata": {}, "source": [ "## Creating HRV summary" ] }, { "cell_type": "markdown", "id": "46fbd306", "metadata": {}, "source": [ "These function can be modularily concatenated to render HRV dashboard. Here is an exampe for an HTML report using [Bokeh](https://docs.bokeh.org/en/latest/index.html) functions." ] }, { "cell_type": "code", "execution_count": 11, "id": "ddec034e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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Application\",\"version\":\"2.4.3\"}};\n", " const render_items = [{\"docid\":\"d39de23e-936f-4d61-af8e-0c216a0cd7b6\",\"root_ids\":[\"1481\"],\"roots\":{\"1481\":\"432e6ebd-2b02-4809-bc65-3948e0be8ad0\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " let attempts = 0;\n", " const timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " clearInterval(timer);\n", " embed_document(root);\n", " } else {\n", " attempts++;\n", " if (attempts > 100) {\n", " clearInterval(timer);\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", " }\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "1481" } }, "output_type": "display_data" } ], "source": [ "from systole.plots import plot_poincare, plot_frequency, plot_rr\n", "from systole.reports import nonlinear_table, frequency_table\n", "\n", "show(\n", " column(\n", " plot_rr(peaks, input_type=\"peaks\", backend=\"bokeh\"),\n", " row(\n", " plot_frequency(peaks, input_type=\"peaks\", backend=\"bokeh\", figsize=(400, 400)),\n", " frequency_table(peaks, input_type=\"peaks\", backend=\"bokeh\"),\n", " ),\n", " row(\n", " plot_poincare(peaks, input_type=\"peaks\", backend=\"bokeh\", figsize=(400, 400)),\n", " nonlinear_table(peaks, input_type=\"peaks\", backend=\"bokeh\"),\n", " )\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "84f107bc", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "ipynb,md:myst" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 5 }