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Felix Jan Michael Mucha 2024-06-03 21:08:46 +02:00
parent 2a8916acdd
commit 1404b91740
1 changed files with 33 additions and 22 deletions
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notebooks/features_bib_neurokit2.ipynb
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@ -697,21 +697,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 42, "execution_count": 44,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 lead R Axis: 56.83 degrees\n",
"2 lead T Axis: 30.78 degrees\n",
"\n",
"6 lead R Axis: 56.83 degrees\n",
"6 lead T Axis: 30.78 degrees\n"
]
}
],
"source": [ "source": [
"def calculate_axis(data, leads, sampling_rate=500, aVF=5, I=0):\n", "def calculate_axis(data, leads, sampling_rate=500, aVF=5, I=0):\n",
" # warnining if leads not include 0 and 5 or one lead is over 5\n", " # warnining if leads not include 0 and 5 or one lead is over 5\n",
@ -743,6 +731,7 @@
" t_peaks = [ecg_signal[int(i)] for i in info['ECG_T_Peaks'] if not math.isnan(i)]\n", " t_peaks = [ecg_signal[int(i)] for i in info['ECG_T_Peaks'] if not math.isnan(i)]\n",
" net_t[lead] = np.mean(t_peaks) if t_peaks else 0\n", " net_t[lead] = np.mean(t_peaks) if t_peaks else 0\n",
"\n", "\n",
" print(net_qrs.get(aVF, 0), net_qrs.get(I, 0))\n",
" # Calculate the R axis (Convert to degrees)\n", " # Calculate the R axis (Convert to degrees)\n",
" r_axis = np.arctan2(net_qrs.get(aVF, 0), net_qrs.get(I, 0)) * (180 / np.pi)\n", " r_axis = np.arctan2(net_qrs.get(aVF, 0), net_qrs.get(I, 0)) * (180 / np.pi)\n",
"\n", "\n",
@ -752,25 +741,47 @@
" return r_axis, t_axis\n", " return r_axis, t_axis\n",
"\n", "\n",
"\n", "\n",
"\n", "\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate R and T Axis for one dataset"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8047692058411333 0.5260930603345693\n",
"2 lead R Axis: 56.83 degrees\n",
"2 lead T Axis: 30.78 degrees\n",
"\n"
]
}
],
"source": [
"# print(data_org['SB'][0].__dict__) 'sig_name': ['I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']\n", "# print(data_org['SB'][0].__dict__) 'sig_name': ['I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']\n",
"\n", "\n",
"i_sig = 0\n", "i_sig = 0\n",
"avf = 5\n", "avf = 5\n",
"sampling_rate = 500\n", "sampling_rate = 500\n",
"\n", "\n",
"ecg_signal_i = filtered_data.p_signal[:,i_sig]\n", "example_data = data_org['SB'][0]\n",
"filtered_data = data_helper.filter_data(example_data, filter_params)\n",
"\n", "\n",
"ecg_cleaned_i = nk.ecg_clean(ecg_signal_i, sampling_rate=sampling_rate)\n",
"\n", "\n",
"r_ax, t_ax = calculate_axis(filtered_data, [0, 5], sampling_rate=500)\n", "r_ax, t_ax = calculate_axis(filtered_data, [0, 5], sampling_rate=500)\n",
"print(f\"2 lead R Axis: {r_ax:.2f} degrees\")\n", "print(f\"2 lead R Axis: {r_ax:.2f} degrees\")\n",
"print(f\"2 lead T Axis: {t_ax:.2f} degrees\")\n", "print(f\"2 lead T Axis: {t_ax:.2f} degrees\")\n",
"print(\"\")\n", "print(\"\")\n"
"\n",
"r_ax, t_ax = calculate_axis(filtered_data, [0, 1, 2, 3, 4, 5], sampling_rate=500)\n",
"print(f\"6 lead R Axis: {r_ax:.2f} degrees\")\n",
"print(f\"6 lead T Axis: {t_ax:.2f} degrees\")"
] ]
} }
], ],