filter and avg qrs, ~90% finished
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# QRS Complex Detection\n",
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"This notebook holds all the functions to detect a QRS-Complex in an ECG Signal. To be able to differentiate if the signal is from a \"healthy\" person or a person with a \"heartcondition\".\n",
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"First of all there must be defined some thresholds to what a \"healthy\" QRS-Complex is. To do so we normalize the Data and extract the normal peaks and lengths of the ECG-Signal in the QRS Region."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import necessary packages\n",
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"\n",
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"import neurokit2 as nk\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import json\n",
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"import sys\n",
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"import wfdb\n",
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"\n",
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"sys.path.append('../scripts')\n",
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"import data_helper"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Reading SB\n",
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"Reading AFIB\n",
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"Reading GSVT\n",
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"Reading SR\n",
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"Number of patients per category:\n",
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"SB: 16559\n",
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"AFIB: 9839\n",
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"GSVT: 948\n",
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"SR: 9720\n"
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]
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}
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],
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"source": [
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"data_org = data_helper.load_data(only_demographic=False)\n",
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"\n",
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"print(\"Number of patients per category:\")\n",
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"for cat_name in data_org.keys():\n",
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" print(f\"{cat_name}: {len(data_org[cat_name])}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Cleaning Data from Noice"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# seperating data by diagnose\n",
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"sb_data = data_org['SB']\n",
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"afib_data = data_org['AFIB']\n",
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"gsvt_data = data_org['GSVT']\n",
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"sr_data = data_org['SR']\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: total: 562 ms\n",
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"Wall time: 1.46 s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"with open('../filter_params.json','r') as f:\n",
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" filter_params = json.load(f)\n",
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"sig_channel = 0\n",
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"np.random.seed(501)\n",
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"sampling_rate = 500\n",
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"### Pick Random Samples from Set\n",
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"amount_sb_records = len(sb_data)\n",
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"sample_indizies = np.random.choice(amount_sb_records,1000,replace=False)\n",
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"sb_data_sample = [sb_data[x].p_signal[:,sig_channel] for x in sample_indizies]\n",
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"clean_sb = [nk.ecg_clean(elem,sampling_rate=sampling_rate) for elem in sb_data_sample]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: total: 375 ms\n",
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"Wall time: 1.77 s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"amount_afib_records = len(afib_data)\n",
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"sample_indizies = np.random.choice(amount_afib_records,1000,replace=False)\n",
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"afib_data_sample = [afib_data[x].p_signal[:,sig_channel] for x in sample_indizies]\n",
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"clean_afib = [nk.ecg_clean(elem,sampling_rate=sampling_rate) for elem in afib_data_sample]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: total: 547 ms\n",
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"Wall time: 1.8 s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"gsvt_data_sample= [gsvt_data[x].p_signal[:,sig_channel] for x in range(len(gsvt_data))]\n",
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"clean_gsvt = [nk.ecg_clean(elem,sampling_rate=sampling_rate) for elem in gsvt_data_sample]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: total: 391 ms\n",
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"Wall time: 1.72 s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"amount_sr_records = len(sr_data)\n",
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"sample_indizies = np.random.choice(amount_sr_records,1000,replace=False)\n",
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"sr_data_sample = [sr_data[x].p_signal[:,sig_channel] for x in sample_indizies] \n",
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"clean_sr = [nk.ecg_clean(elem,sampling_rate=sampling_rate) for elem in sr_data_sample]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Lokalisierung der QRS-Komplexe"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"avg_sb = np.mean(clean_sb,axis=1)\n",
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"\n",
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"avg_afib = np.mean(clean_afib,axis=1)\n",
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"\n",
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"avg_gsvt = np.mean(clean_gsvt,axis=1)\n",
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"\n",
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"avg_sr = np.mean(clean_sr,axis=1)\n",
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"\n",
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"def get_r_peaks(clean_ecg_data,sampling_rate)->list:\n",
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" return [nk.ecg_peaks(record,sampling_rate=sampling_rate)[1] for record in clean_ecg_data]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"sb_r_peaks = get_r_peaks(clean_sb,sampling_rate)\n",
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"afib_r_peaks = get_r_peaks(clean_afib,sampling_rate)\n",
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"# gsvt_r_peaks = get_r_peaks(clean_gsvt,sampling_rate)\n",
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"sr_r_peaks = get_r_peaks(clean_sr,sampling_rate)\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# sb_r_peaks = [x for x in sb_r_peaks if len(x['ECG_R_Peaks'])>3]\n",
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"def remove_with_to_few(signals,clean):\n",
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" index_for_removal = []\n",
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" for index, element in enumerate(signals):\n",
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" if len(element['ECG_R_Peaks']) < 3:\n",
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" index_for_removal.append(index)\n",
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" for i in index_for_removal:\n",
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" del clean[i]\n",
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" del signals[i]\n",
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" return signals,clean\n",
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"\n",
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"sb_r_peaks,clean_sb = remove_with_to_few(sb_r_peaks,clean_sb)\n",
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"afib_r_peaks,clean_afib = remove_with_to_few(afib_r_peaks,clean_afib)\n",
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"sr_r_peaks,clean_sr = remove_with_to_few(sr_r_peaks,clean_sr)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"sb done\n",
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"afib done\n",
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"sr done\n"
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]
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}
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],
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"source": [
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"def get_infos(ecg_data,r_peaks,sampling_rate:int)->list:\n",
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" return [nk.ecg_delineate(ecg_data[x],r_peaks[x]['ECG_R_Peaks'],sampling_rate=sampling_rate,method=\"dwt\")[1]for x in range(len(ecg_data))]\n",
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"\n",
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"sb_info = get_infos(clean_sb,sb_r_peaks,sampling_rate=sampling_rate)\n",
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"print('sb done')\n",
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"afib_info = get_infos(clean_afib,afib_r_peaks,sampling_rate=sampling_rate)\n",
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"print('afib done')\n",
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"sr_info = get_infos(clean_sr,sr_r_peaks,sampling_rate=sampling_rate)\n",
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"print('sr done')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"import math\n",
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"# Removes nan values from P and T peaks and removed corosponding 'base' signals\n",
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"def remove_nan(signal,clean_signal):\n",
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" nan_indizies = []\n",
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" for i in range(len(signal)):\n",
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" if math.isnan(signal[i]['ECG_P_Peaks'][0]):\n",
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" nan_indizies.append(i)\n",
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" for i in range(len(signal)):\n",
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" if math.isnan(signal[i]['ECG_T_Peaks'][0]):\n",
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" nan_indizies.append(i)\n",
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" nth_removed_index = 0\n",
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" for i in nan_indizies:\n",
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" del signal[i-nth_removed_index]\n",
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" del clean_signal[i-nth_removed_index]\n",
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" nth_removed_index+=1\n",
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" return signal,clean_signal\n",
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"\n",
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"sb_info,clean_sb = remove_nan(sb_info,clean_sb)\n",
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"afib_info,clean_afib = remove_nan(afib_info,clean_afib)\n",
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"sr_info,clean_sr = remove_nan(sr_info,clean_sr)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"sb_p 854 sb_t 854 sb_clean 854\n"
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]
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}
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],
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"source": [
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"# get first P Peaks in Signals\n",
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"first_sb_p_peak = [elem['ECG_P_Peaks'][0] for elem in sb_info]\n",
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"first_afib_p_peak = [elem['ECG_P_Peaks'][0] for elem in afib_info]\n",
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"first_sr_p_peak = [elem['ECG_P_Peaks'][0] for elem in sr_info]\n",
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"\n",
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"# get first T Peaks in Signals\n",
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"first_sb_t_peak = [elem['ECG_T_Peaks'][0] for elem in sb_info]\n",
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"first_afib_t_peak = [elem['ECG_T_Peaks'][0] for elem in afib_info]\n",
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"first_sr_t_peak = [elem['ECG_T_Peaks'][0] for elem in sr_info]\n",
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"\n",
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"print(\"sb_p\",len(first_sb_p_peak),'sb_t',len(first_sb_t_peak),'sb_clean',len(clean_sb))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"def cut_signals(signals,first_p,first_t):\n",
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" return [signals[i][first_p[i]:first_t[i]] for i in range(len(signals))]\n",
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" \n",
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"\n",
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"sliced_sb = cut_signals(clean_sb,first_sb_p_peak,first_sb_t_peak)\n",
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"sliced_afib = cut_signals(clean_afib,first_afib_p_peak,first_afib_t_peak)\n",
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"sliced_sr = cut_signals(clean_sr,first_sr_p_peak,first_sr_t_peak)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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