178 lines
6.9 KiB
Python
178 lines
6.9 KiB
Python
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from matplotlib import pyplot as plt
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import wfdb.processing
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import sys
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import json
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import scipy
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import numpy as np
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import neurokit2 as nk
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import math
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import time
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def get_y_value(ecg_cleaned, indecies):
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"""
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Get the y value of the ECG signal at the given indices.
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Args:
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ecg_cleaned (list): The cleaned ECG signal.
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indecies (list): The list of indices.
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Returns:
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list: The list of y values at the given indices.
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"""
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return [ecg_cleaned[int(i)] for i in indecies if not math.isnan(i)]
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def calculate_axis(record, wave_peak, r_peak_idx, sampling_rate=500, aVF=5, I=0):
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"""
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Calculate the R and T axis of the ECG signal.
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Args:
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record (object): The record object containing the ECG signal.
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wave_peak (dict): The dictionary containing the wave peaks.
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r_peak_idx (list): The list containing the R peak indices.
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sampling_rate (int): The sampling rate of the ECG signal.
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aVF (int): The index of the aVF lead.
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I (int): The index of the I lead.
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Returns:
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tuple: The R and T axis of the ECG signal.
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"""
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# Calculate the net QRS in each lead
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ecg_signal_avf = record.p_signal[:, aVF]
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ecg_signal_avf_cleaned = nk.ecg_clean(ecg_signal_avf, sampling_rate=sampling_rate)
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ecg_signal_i = record.p_signal[:, I]
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ecg_signal_i_cleaned = nk.ecg_clean(ecg_signal_i, sampling_rate=sampling_rate)
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# r axis
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# get amplitude of peaks
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q_peaks_avf = get_y_value(ecg_signal_avf_cleaned, wave_peak['ECG_Q_Peaks'])
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s_peaks_avf = get_y_value(ecg_signal_avf_cleaned, wave_peak['ECG_S_Peaks'])
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r_peaks_avf = get_y_value(ecg_signal_avf_cleaned, r_peak_idx)
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q_peaks_i = get_y_value(ecg_signal_i_cleaned, wave_peak['ECG_Q_Peaks'])
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s_peaks_i = get_y_value(ecg_signal_i_cleaned, wave_peak['ECG_S_Peaks'])
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r_peaks_i = get_y_value(ecg_signal_i_cleaned, r_peak_idx)
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# calculate avg peal amplitude
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q_peaks_i_avg = np.mean(q_peaks_i)
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s_peaks_i_avg = np.mean(s_peaks_i)
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r_peaks_i_avg = np.mean(r_peaks_i)
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q_peaks_avf_avg = np.mean(q_peaks_avf)
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s_peaks_avf_avg = np.mean(s_peaks_avf)
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r_peaks_avf_avg = np.mean(r_peaks_avf)
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# Calculate net QRS in lead
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net_qrs_i = r_peaks_i_avg - (q_peaks_i_avg + s_peaks_i_avg)
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net_qrs_avf = r_peaks_avf_avg - (q_peaks_avf_avg + s_peaks_avf_avg)
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# t axis
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t_peaks_i = get_y_value(ecg_signal_avf_cleaned, wave_peak['ECG_T_Peaks'])
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t_peaks_avf = get_y_value(ecg_signal_i_cleaned, wave_peak['ECG_T_Peaks'])
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net_t_i = np.mean(t_peaks_i)
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net_t_avf = np.mean(t_peaks_avf)
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#print("amplitude I", net_qrs.get(I, 0))
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#print("amplitude aVF", net_qrs.get(aVF, 0))
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# Calculate the R axis (Convert to degrees)
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r_axis = np.arctan2(net_qrs_avf, net_qrs_i) * (180 / np.pi)
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# Calculate the T axis (Convert to degrees)
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t_axis = np.arctan2(net_t_avf, net_t_i) * (180 / np.pi)
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return r_axis, t_axis
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def extract_features(data_dict, sampling_rate=500, used_channels=[0, 1, 2, 3, 4, 5]):
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"""
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Extracts the features from the data.
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Args:
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data_dict (dict): The dictionary containing the data.
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Returns:
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dict: The dictionary containing the extracted features.
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"""
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start_time = time.time()
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feature_data = {}
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failed_records = []
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for label, data in data_dict.items():
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print(f"Extracting features for {label} with {len(data)} data entries.")
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for data_idx, record in enumerate(data):
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if data_idx % 100 == 0:
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stop_time = time.time()
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print(f"Extracted features for {data_idx} records. Time taken: {stop_time - start_time:.2f}s")
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start_time = time.time()
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age = record.comments[0].split(' ')[1]
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gender = record.comments[1].split(' ')[1]
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if age == 'NaN' or gender == 'NaN':
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continue
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features = {}
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# Extract the features
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features['y'] = label
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# Demographic features
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features['age'] = int(age)
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features['gender'] = True if gender == 'Male' else False
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# Signal features
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ecg_signal = record.p_signal[:, 0]
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ecg_cleaned = nk.ecg_clean(ecg_signal, sampling_rate=sampling_rate)
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_, rpeaks = nk.ecg_peaks(ecg_cleaned, sampling_rate=sampling_rate)
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r_peaks = rpeaks['ECG_R_Peaks']
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# Delineate the ECG signal
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try:
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_, waves_peak = nk.ecg_delineate(ecg_signal, r_peaks, sampling_rate=sampling_rate, method="peak")
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except:
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failed_records.append(record.record_name)
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print(f"Failed to extract features for record {record.record_name} Sum of failed records: {len(failed_records)}")
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continue
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# TODO Other features and check features
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atrial_rate = len(waves_peak['ECG_P_Peaks']) * 6
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ventricular_rate = np.mean(nk.ecg_rate(r_peaks, sampling_rate=sampling_rate, desired_length=len(ecg_cleaned)))
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features['artial_rate'] = atrial_rate
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features['ventricular_rate'] = ventricular_rate
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qrs_duration = np.nanmean(np.array(waves_peak['ECG_S_Peaks']) - np.array(waves_peak['ECG_Q_Peaks']))
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features['qrs_duration'] = qrs_duration
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qt_interval = np.nanmean(np.array(waves_peak['ECG_T_Offsets']) - np.array(waves_peak['ECG_Q_Peaks']))
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features['qt_length'] = qt_interval
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q_peak = waves_peak['ECG_Q_Peaks']
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s_peak = waves_peak['ECG_S_Peaks']
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# check if q_peak, r_peak, s_peak are not nan and therefore a solid qrs complex exists
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qrs_count = [any([math.isnan(q_peak[i]), math.isnan(r_peaks[i]), math.isnan(s_peak[i])]) for i in range(len(q_peak))].count(False)
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features['qrs_count'] = qrs_count
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features['q_peak'] = np.mean(get_y_value(ecg_cleaned, q_peak))
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r_axis, t_axis = calculate_axis(record, waves_peak, r_peaks, sampling_rate=500, aVF=5, I=0)
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features['r_axis'] = r_axis
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features['t_axis'] = t_axis
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# print the features
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#print(json.dumps(features, indent=4))
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feature_data[record.record_name] = features
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return feature_data
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def split_data(feature_data, split_ratio):
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print(f"Splitting data with ratio {split_ratio}")
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#flatten dictionary
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feature_data = {k: v for k, v in feature_data.items()}
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# print keys
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print("Keys:")
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print(len(feature_data.keys()))
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# shuffle the data
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keys = list(feature_data.keys())
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np.random.shuffle(keys)
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# split the data
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split_data = {}
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split_data['train'] = {k: feature_data[k] for k in keys[:int(len(keys) * split_ratio[0])]}
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split_data['test'] = {k: feature_data[k] for k in keys[int(len(keys) * split_ratio[0]):int(len(keys) * (split_ratio[0] + split_ratio[1]))]}
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split_data['validation'] = {k: feature_data[k] for k in keys[int(len(keys) * (split_ratio[0] + split_ratio[1])):]}
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return split_data
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