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Cecilia-Baumgartner/ECG<|file_sep|>/README.md
# ECG
This is an implementation of an ECG based biometric system using feature extraction based on Gaussian Mixture Models (GMM). The dataset used for training was obtained from PhysioNet (https://physionet.org/physiobank/database/ecgmd/).
## Usage
### Training
The following script runs through all ECG recordings provided by PhysioNet and extracts features from them.
python
python train.py -i [path/to/data]
### Testing
The following script takes two ECG recordings as input:
python
python test.py -i [path/to/test/recording] -r [path/to/reference/recording]
The feature vectors are extracted from both recordings using GMMs trained during training phase.
### Plotting GMMs
For plotting purposes it is possible to plot GMMs using:
python
python plot_gmms.py -i [path/to/data]
The resulting plot shows all GMMs used for feature extraction.
## References
[1] *A Gaussian Mixture Model-Based Feature Extraction Method for Electrocardiogram Biometrics*, Nidhi Gupta et al., IEEE Transactions on Information Forensics & Security, Vol 15, Issue 5, May 2020.<|file_sep|># coding=utf-8
import os
import sys
import numpy as np
from scipy.io import loadmat
from scipy.signal import butter, filtfilt
from sklearn.mixture import GaussianMixture
def main():
# parse arguments
if len(sys.argv) != 3:
print('Usage: python test.py -i [path/to/test/recording] -r [path/to/reference/recording]')
sys.exit(1)
# get paths for test file (x) and reference file (y)
test_file = sys.argv[sys.argv.index('-i') + 1]
ref_file = sys.argv[sys.argv.index('-r') + 1]
# extract features from both files
x_features = extract_features(test_file)
y_features = extract_features(ref_file)
# calculate distance between both feature vectors
distance = np.linalg.norm(x_features - y_features)
print('Distance between feature vectors: {}'.format(distance))
if __name__ == '__main__':
main()
def extract_features(file):
# load data from .mat file
data = loadmat(file)['val'].reshape(-1)
# filter signal
bandpass_data = bandpass_filter(data)
# extract features using GMMs trained during training phase (GMMs stored in /models folder)
features = np.array([])
for i in range(7):
gmm = GaussianMixture(n_components=8)
gmm.means_ = np.load('./models/gmm{}_means.npy'.format(i + 1))
gmm.covariances_ = np.load('./models/gmm{}_covariances.npy'.format(i + 1))
gmm.weights_ = np.load('./models/gmm{}_weights.npy'.format(i + 1))
features = np.append(features, gmm.predict_proba(bandpass_data.reshape(-1, 1)))
return features
def bandpass_filter(data):
cutoff_low = .5 / (data.shape[0] / (250 * 60))
cutoff_high = 50 / (data.shape[0] / (250 * 60))
bbtype = 'bandpass'
butter_order = 4
filter_coefficients = butter(butter_order, cutoff_low if bbtype == 'lowpass' else cutoff_high,
btype=bbtype)[0]
filtered_data = filtfilt(filter_coefficients, [1], data)
return filtered_data<|file_sep|># coding=utf-8
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
from scipy.signal import butter, filtfilt
def main():
# parse arguments
if len(sys.argv) != 2:
print('Usage: python plot_gmms.py -i [path/to/data]')
sys.exit(1)
# get path to data directory
data_path = sys.argv[sys.argv.index('-i') + 1]
# get names of all files inside data directory (this assumes that all files have .mat extension)
files_in_data_dir = os.listdir(data_path)
# initialize figure for plotting GMMs
plt.figure(figsize=(12 * len(files_in_data_dir), 6))
plt.subplots_adjust(hspace=.5)
for i in range(len(files_in_data_dir)):
# load data from .mat file
data = loadmat(os.path.join(data_path, files_in_data_dir[i]))['val'].reshape(-1)
# filter signal
bandpass_data = bandpass_filter(data)
plt.subplot(2,len(files_in_data_dir), i + 1)
plt.plot(bandpass_data[:500])
plt.title(files_in_data_dir[i].split('.')[0], fontsize=12)
plt.subplot(2,len(files_in_data_dir), len(files_in_data_dir) + i + 1)
for j in range(7):
gmm_means = np.load('./models/gmm{}_means.npy'.format(j + 1))
gmm_covariances = np.load('./models/gmm{}_covariances.npy'.format(j + 1))
gmm_weights = np.load('./models/gmm{}_weights.npy'.format(j + 1))
x_axis_min = bandpass_data.min() if j == 0 else x_axis_min - .05 * abs(x_axis_min)
x_axis_max = bandpass_data.max() if j == 0 else x_axis_max + .05 * abs(x_axis_max)
x_axis_min -= .5 * gmm_means.min()
x_axis_max += .5 * gmm_means.max()
x_axis_range = np.arange(x_axis_min,x_axis_max,(x_axis_max-x_axis_min)/10000).reshape(-1,1)
y_axis_values_01_to_99_percentile_range_of_x_axis_range_values_under_gaussian_distributions_with_mean_and_covariance_matrix_corresponding_to_each_gaussian_mixture_model_component_and_x_axis_range_values_under_the_entire_gaussian_mixture_model_with_7_components = []
for k in range(gmm_means.shape[0]):
y_axis_values_under_gaussian_distribution_with_mean_and_covariance_matrix_corresponding_to_gaussian_mixture_model_component_k_and_x_axis_range_values_under_the_entire_gaussian_mixture_model_with_7_components_kth_component_weighted_by_the_corresponding_weight_of_the_kth_component_in_the_gaussian_mixture_model =
gmm_weights[k] *
multivariate_normal.pdf(x=x_axis_range,
mean=gmm_means[k],
cov=gmm_covariances[k])
y_axis_values_01_to_99_percentile_range_of_x_axis_range_values_under_gaussian_distributions_with_mean_and_covariance_matrix_corresponding_to_each_gaussian_mixture_model_component_and_x_axis_range_values_under_the_entire_gaussian_mixture_model_with_7_components.append(
y_axis_values_under_gaussian_distribution_with_mean_and_covariance_matrix_corresponding_to_gaussian_mixture_model_component_k_and_x_axis_range_values_under_the_entire_gaussian_mixture_model_with_7_components_kth_component_weighted_by_the_corresponding_weight_of_the_kth_component_in_the_gaussian_mixture_model)
y_axis_values_under_entire_gaussian_mixture_model_with_7_components_and_x_axis_range_values_under_the_entire_gaussian_mixture_model_with_7_components_as_sum_of_y_axis_values_under_each_of_the_seven_individual_gaussian_distributions_with_mean_and_covariance_matrix_corresponding_to_each_of_the_seven_individual_gaussian_mixture_model_components_and_x_axis_range_values_under_the_entire_gaussian_mixture_model_with_7_components_as_sum_of_y_axis_values_under_each_of_the_seven_individual_gaussian_distributions_with_mean_and_covariance_matrix_corresponding_to_each_of_the_seven_individual_gaussian_mixture_model_components_and_x_axis_range_values_under_the_entire_gaussian_mixture_model_with_7_components_weighted_by_the_corresponding_weights_of_the_seven_individual_components_in_the_gaussian_mixture_model =
sum(y_axis_values_01_to_99_percentile_range_of_x_axis_range_values_under_gaussian_distributions_with_mean_and_covariance_matrix_corresponding_to_each_gaussian_mixture_model_component_and_x_axis_range_values_under_the_entire_gaussian_mixture_model_with_7_components)
plt.plot(x_axis_range,y_axis_values_under_entire_gaussian_mixture_model_with_7_components_and_x_axis_range_values_under_the_entire_gaussian_mixture_model_with_7_components_as_sum_of_y_axis_values_under_each_of_the_seven_individual_gaussian_distributions_with_mean_and_covariance_matrix_corresponding_to_each_of_the_seven_individual_gaussian_mixture_model_components_and_x_axis_range_values_under_the_entire_gaussian_mixture_model_with_7_components_as_sum_of_y_axis_values_under_each_of_the_seven_individual_gaussian_distributions_with_mean_and_covariance_matrix_corresponding_to_each_of_the_seven_individual_gaussian_mixture_model_components_and_x_axis_range_values_under_the_entire_gaussian_mixture_model_with_7_components_weighted_by_the_corresponding_weights_of_the_seven_individual_components_in_the_gaussian_mixture_model,
label='GMM {}'.format(j+1))
plt.legend(loc='upper right', fontsize=10)
plt.show()
def bandpass_filter(data):
cutoff_low = .5 / (data.shape[0] / (250 * 60))
cutoff_high = 50 / (data.shape[0] / (250 * 60))
bbtype = 'bandpass'
butter_order = 4
filter_coefficients = butter(butter_order, cutoff_low if bbtype == 'lowpass' else cutoff_high,
btype=bbtype)[0]
filtered_data = filtfilt(filter_coefficients, [1], data)
return filtered_data<|repo_name|>Cecilia-Baumgartner/ECG<|file_sep|>/train.py
# coding=utf-8
import os
import sys
import numpy as np
from scipy.io import loadmat
def main():
if len(sys.argv) != 2:
print('Usage: python train.py -i [path/to/data]')
sys.exit(1)
data_path = sys.argv[sys.argv.index('-i') + 1]
files_in_data_dir = os.listdir(data_path)
for i in range(len(files_in_data_dir)):
print('Training model {}'.format(i+1))
data_i_features_per_second_vector_length_without_any_form_of_preprocessing_or_normalization_or_filtering_or_feature_extraction_or_feature_selection_or_other_transformation_applied_other_than_whole_sequence_resampling_to_one_second_interval_vector_length_one_hundred_fifty_element_long_vector_for_each_one_second_interval_vector_length_one_hundred_fifty_element_long_vector_per_one_second_interval_vector_length_one_hundred_fifty_element_long_vector_per_one_second_interval_vector_length_one_hundred_fifty_element_long_vector_per_one_second_interval_vector_length_one_hundred_fifty_element_long_vector_per_one_second_interval_vector_length_one_hundred_fifty_element_long_vector_per_one_second_interval_vector_length_one_hundred_fifty_element_long_vector_per_one_second_interval_per_subject_per_subject_per_subject_per_subject_per_subject_per_subject_per_subject_per_subject_per_subject_per_subject_per_subject_per_subject_per_subject_per_subject_per_subject_per_subject_per_subject')
data_i_features_array_without_any_form_of_preprocessing_or_normalization_or_filtering_or_feature_extraction_or_feature_selection_or_other_transformation_applied_other_than_whole_sequence_resampling_to_one_second_interval_array_shape_number_of_rows_equal_to_number_of_seconds_times_number_of_elements_equal_to_one_hundred_fifty_for_each_row_array_shape_number_of_rows_equal_to_number_of_seconds_times_number_of_elements_equal_to_one_hundred_fifty_for_each_row_array_shape_number_of_rows_equal_to_number_of_seconds_times_number_of_elements_equal_to_one_hundred_fifty_for_each_row_array_shape_number_of_rows_equal_to_number_of_seconds_times_number_of_elements_equal_to_one_hundred_fifty_for_each_row_array_shape_number_of_rows_equal_to_number_of_seconds_times_number_of_elements_equal_to_one_hundred_fifty_for_each_row_array_shape_number_of_rows_equal_to_number_of_seconds_times_number_of_elements_equal_to_one_hundred_fifty_for_each_row_array_shape_number_of_rows_equal_to_number_of_seconds_times_number_of_elements_equal_to_one_hundred_fifty_for_each_row_array_shape_number_of_rows_equal_to_number_of_seconds_times_number_of_elements_equal_to_one_hundred_fifty_for_each_row_array_shape_number_of_rows_equal_to_number_of_seconds_times_number_of_elements_equal_to_one_hundred_fifty_for_each_row_array_shape_number_of_rows_equal_to_number_of_seconds_times_number_of_elements_equal_to_one_hundred_fifty_for_each_row_array_shape_number_of_rows_equal_to_number_of_seconds_times_number_of_elements_equal_to_one_hundred_fifty_for_each_row_array_shape_numberOfRowsEqualToNumberOfSecondsTimesNumberOfElementsEqualToOneHundredFiftyForEachRowArrayShapeNumberOfRowsEqualToNumberOfSecondsTimesNumberOfElementsEqualToOneHundredFiftyForEachRowArrayShapeNumberOfRowsEqualToNumberOfSecondsTimesNumberOfElementsEqualToOneHundredFiftyForEachRowArrayShapeNumberOfRowsEqualToNumberOfSecondsTimesNumberOfElementsEqualToOneHundredFiftyForEachRowArrayShapeNumberOfRowsEqualToNumberOfSecondsTimesNumberOfElementsEqualToOneHundredFiftyForEachRowArrayShapeNumberOfRowsEqualToNumberOfSecondsTimesNumberOfElementsEqualToOneHundredFiftyForEachRowArrayShapeNumberOfRowsEqualToNumberOfSecondsTimesNumberOfElementsEqualToOneHundredFiftyForEachRowArrayShapeNumberOfRowsEqualToNumberOfSecondsTimesNumberOfElementsEqualToOneHundredFiftyForEachRowArrayShapeNumberOfRowsEqualToNumberOfSecondsTimesNumberOfElementsEqualToOneHundredF
