The code contains the implementation of a method for the automatic classification of electrocardiograms (ECG) based on the combination of multiple Support Vector Machines (SVMs). The method relies on the time intervals between consequent beats and their morphology for the ECG characterisation. Different descriptors based on wavelets, local binary patterns (LBP), higher order statistics (HOS) and several amplitude values were employed.
For a detailed explanation refer to the paper.
- Title: Heartbeat Classification Fusing Temporal and Morphological Information of ECGs via Ensemble of Classifiers.
- Authors: V. Mondéjar-Guerra, J. Novo, J. Rouco, M. G.Penedo, M. Ortega
- Journal: Biomedical Signal Processing and Control
- DOI: 10.1016/j.bspc.2018.08.007
If you use this code, please cite our work.
Python implementation is the most updated version of the repository. Matlab implementation is independent. Both implementations are tested under Ubuntu 16.04.
- Numpy
- Scikit learn
- Matplotlib (Optional)
Performed using Matlab 2016b 64 bits
Implementation for TensorFlow is in early stage and will not be maintained by the author.
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Download the dataset:
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a) Download via Kaggle:
The raw signals files (.csv) and annotations files can be downloaded from kaggle.com/mondejar/mitbih-database
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b) Download via WFDB:
https://www.physionet.org/faq.shtml#downloading-databases
Using the comand rsync you can check the datasets availability:
rsync physionet.org::The terminal will show all the available datasets:
physionet PhysioNet web site, volume 1 (about 23 GB) physionet-small PhysioNet web site, excluding databases (about 5 GB) ... ... umwdb Unconstrained and Metronomic Walking Database (1 MB) vfdb MIT-BIH Malignant Ventricular Ectopy Database (33 MB)Then select the desired dataset as:
rsync -Cavz physionet.org::mitdb /home/mondejar/dataset/ECG/mitdbrsync -Cavz physionet.org::incartdb /home/mondejar/dataset/ECG/incartdbFinally to convert the data as plain text files use convert_wfdb_data_2_csv.py. One file with the raw data and one file for annotations ground truth.
Also check the repo WFDB_utils_and_others for more info about WFDB database conversion and the original site from Physionet_tools.
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Run:
Run the file run_train_SVM.py and adapt the desired configuration to call train_SVM.py file. This call method will train the SVM model using the training set and evaluates the model on a different test set.
Check and adjust the path dirs on train_SVM.py file.
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Combining multiples classifiers:
Run the file basic_fusion.py to combine the decisions of previously trained SVM models.
The data is splited following the inter-patient scheme proposed by Chazal et al., i.e the training and eval set not contain any patient in common.
This code classifies the signal at beat-level following the class labeling of the AAMI recomendation.
First, the baseline of the signal is substracted. Additionally, some noise removal can be done.
Two median filters are applied for this purpose, of 200-ms and 600-ms. Note that this values depend on the frecuency sampling of the signal.
from scipy.signal import medfilt
...
# median_filter1D
baseline = medfilt(MLII, 71)
baseline = medfilt(baseline, 215) The signal resulting from the second filter operation contains the baseline wanderings and can be subtracted from the original signal.
# Remove Baseline
for i in range(0, len(MLII)):
MLII[i] = MLII[i] - baseline[i]In this work the annotations of the MIT-BIH arrhyhtmia was used in order to detect the R-peak positions. However, in practise they can be detected using the following software (see Software references: Beat Detection).
In order to describe the beats for classification purpose, we employ the following features:
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Morphological: for this features a window of [-90, 90] was centred along the R-peak:
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RAW-Signal (180): is the most simplier descriptor. Just employ the amplitude values from the signal delimited by the window.
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Wavelets (23): The wavelet transforms have the capability to allow information extraction from both frequency and time domains, which make them suitable for ECG description. The signal is decomposed using wave_decomposition function using family db1 and 3 levels.
import pywt ... db1 = pywt.Wavelet('db1') coeffs = pywt.wavedec(beat, db1, level=3) wavel = coeffs[0]
- HOS (10): extracted from 3-4th order cumulant, skewness and kurtosis.
import scipy.stats ... n_intervals = 6 lag = int(round( (winL + winR )/ n_intervals)) ... # For each beat for i in range(0, n_intervals-1): pose = (lag * (i+1)) interval = beat[(pose -(lag/2) ):(pose + (lag/2))] # Skewness hos_b[i] = scipy.stats.skew(interval, 0, True) # Kurtosis hos_b[5+i] = scipy.stats.kurtosis(interval, 0, False, True)
- U-LBP 1D (59) 1D version of the popular LBP descriptor. Using the uniform patterns with neighbours = 8
import numpy as np ... hist_u_lbp = np.zeros(59, dtype=float) for i in range(neigh/2, len(signal) - neigh/2): pattern = np.zeros(neigh) ind = 0 for n in range(-neigh/2,0) + range(1,neigh/2+1): if signal[i] > signal[i+n]: pattern[ind] = 1 ind += 1 # Convert pattern to id-int 0-255 (for neigh =8) pattern_id = int("".join(str(c) for c in pattern.astype(int)), 2) # Convert id to uniform LBP id 0-57 (uniform LBP) 58: (non uniform LBP) if pattern_id in uniform_pattern_list: pattern_uniform_id = int(np.argwhere(uniform_pattern_list == pattern_id)) else: pattern_uniform_id = 58 # Non uniforms patternsuse hist_u_lbp[pattern_uniform_id] += 1.0
- My Descriptor (4): computed from the Euclidean distance of the R-peak and four points extracted from the 4 following intervals:
- max([0, 40])
- min([75, 85])
- min([95, 105])
- max([150, 180])
import operator ... R_pos = int((winL + winR) / 2) R_value = beat[R_pos] my_morph = np.zeros((4)) y_values = np.zeros(4) x_values = np.zeros(4) # Obtain (max/min) values and index from the intervals [x_values[0], y_values[0]] = max(enumerate(beat[0:40]), key=operator.itemgetter(1)) [x_values[1], y_values[1]] = min(enumerate(beat[75:85]), key=operator.itemgetter(1)) [x_values[2], y_values[2]] = min(enumerate(beat[95:105]), key=operator.itemgetter(1)) [x_values[3], y_values[3]] = max(enumerate(beat[150:180]), key=operator.itemgetter(1)) x_values[1] = x_values[1] + 75 x_values[2] = x_values[2] + 95 x_values[3] = x_values[3] + 150 # Norm data before compute distance x_max = max(x_values) y_max = max(np.append(y_values, R_value)) x_min = min(x_values) y_min = min(np.append(y_values, R_value)) R_pos = (R_pos - x_min) / (x_max - x_min) R_value = (R_value - y_min) / (y_max - y_min) for n in range(0,4): x_values[n] = (x_values[n] - x_min) / (x_max - x_min) y_values[n] = (y_values[n] - y_min) / (y_max - y_min) x_diff = (R_pos - x_values[n]) y_diff = R_value - y_values[n] my_morph[n] = np.linalg.norm([x_diff, y_diff])
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Interval RR (4): intervals computed from the time between consequent beats. There are the most common feature employed for ECG classification.
- pre_RR
- post_RR
- local_RR
- global_RR
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Normalized RR (4): RR interval normalized by the division with the AVG value from each patient.
- pre_RR / AVG(pre_RR)
- post_RR / AVG(post_RR)
- local_RR / AVG(local_Python (Scikit-learn)
- global_RR / AVG(global_RR)
NOTE: Beats having a R–R interval smaller than 150 ms or higher than 2 s most probably involve segmentation errors and are discarded. "Weighted Conditional Random Fields for Supervised Interpatient Heartbeat Classification"*
Before train the models. All the input data was standardized with z-score, i.e., the values of each dimension are divided by its standard desviation and substracted by its mean.
import sklearn
from sklearn.externals import joblib
from sklearn.preprocessing import StandardScaler
from sklearn import svm
...
scaler = StandardScaler()
scaler.fit(tr_features)
tr_features_scaled = scaler.transform(tr_features)
# scaled: zero mean unit variance ( z-score )
eval_features_scaled = scaler.transform(eval_features)In scikit-learn the multiclass SVM support is handled according to a one-vs-one scheme.
Since the MIT-BIH database presents high imbalanced data, several weights equal to the ratio between the two classes of each model were employed to compensate this differences.
The Radial Basis Function (RBF) kernel was employed.
class_weights = {}
for c in range(4):
class_weights.update({c:len(tr_labels) / float(np.count_nonzero(tr_labels == c))})
svm_model = svm.SVC(C=C_value, kernel='rbf', degree=3, gamma='auto',
coef0=0.0, shrinking=True, probability=use_probability, tol=0.001,
cache_size=200, class_weight=class_weights, verbose=False,
max_iter=-1, decision_function_shape=multi_mode, random_state=None)
svm_model.fit(tr_features_scaled, tr_labels) For evaluating the model, the jk index Mar et. al) were employed as performance measure
decision_ovo = svm_model.decision_function(eval_features_scaled)
predict_ovo, counter = ovo_voting_exp(decision_ovo, 4)
perf_measures = compute_AAMI_performance_measures(predict_ovo, labels)Several basic combination rules can be employed to combine the decision from different SVM model configurations in a single prediction (see basic_fusion.py)
| Classifier | Acc. | Sens. | jk index |
|---|---|---|---|
| Our Ensemble of SVMs | 0.945 | 0.703 | 0.773 |
| Zhang et al. | 0.883 | 0.868 | 0.663 |
| Out Single SVM | 0.884 | 0.696 | 0.640 |
| Mar et al. | 0.899 | 0.802 | 0.649 |
| Chazal et al. | 0.862 | 0.832 | 0.612 |
https://physionet.org/cgi-bin/atm/ATM
360HZ
48 Samples of 30 minutes, 2 leads 47 Patients:
- 100 series: 23 samples
- 200 series: 25 samples. Contains uncommon but clinically important arrhythmias
| Symbol | Meaning |
|---|---|
| · or N | Normal beat |
| L | Left bundle branch block beat |
| R | Right bundle branch block beat |
| A | Atrial premature beat |
| a | Aberrated atrial premature beat |
| J | Nodal (junctional) premature beat |
| S | Supraventricular premature beat |
| V | Premature ventricular contraction |
| F | Fusion of ventricular and normal beat |
| [ | Start of ventricular flutter/fibrillation |
| ! | Ventricular flutter wave |
| ] | End of ventricular flutter/fibrillation |
| e | Atrial escape beat |
| j | Nodal (junctional) escape beat |
| E | Ventricular escape beat |
| / | Paced beat |
| f | Fusion of paced and normal beat |
| x | Non-conducted P-wave (blocked APB) |
| Q | Unclassifiable beat |
| Rhythm annotations appear below the level used for beat annotations | |
|---|---|
| (AB | Atrial bigeminy |
| (AFIB | Atrial fibrillation |
| (AFL | Atrial flutter |
| (B | Ventricular bigeminy |
| (BII | 2° heart block |
| (IVR | Idioventricular rhythm |
| (N | Normal sinus rhythm |
| (NOD | Nodal (A-V junctional) rhythm |
| (P | Paced rhythm |
| (PREX | Pre-excitation (WPW) |
| (SBR | Sinus bradycardia |
| (SVTA | Supraventricular tachyarrhythmia |
| (T | Ventricular trigeminy |
| (VFL | Ventricular flutter |
| (VT | Ventricular tachycardia |
There are 15 recommended classes for arrhythmia that are classified into 5 superclasses:
| SuperClass | ||||||
|---|---|---|---|---|---|---|
| N (Normal) | N | L | R | |||
| SVEB (Supraventricular ectopic beat) | A | a | J | S | e | j |
| VEB (Ventricular ectopic beat) | V | E | ||||
| F (Fusion beat) | F | |||||
| Q (Unknown beat) | P | / | f | u |
Inter-patient train/test split (Chazal et al):
DS_1 Train: 101, 106, 108, 109, 112, 114, 115, 116, 118, 119, 122, 124, 201, 203, 205, 207, 208, 209, 215, 220, 223, 230
| Class | N | SVEB | VEB | F | Q |
|---|---|---|---|---|---|
| instances | 45842 | 944 | 3788 | 414 | 0 |
DS_2 Test: = 100, 103, 105, 111, 113, 117, 121, 123, 200, 202, 210, 212, 213, 214, 219, 221, 222, 228, 231, 232, 233, 234
| Class | N | SVEB | VEB | F | Q |
|---|---|---|---|---|---|
| instances | 44743 | 1837 | 3447 | 388 | 8005 |
https://www.physionet.org/pn3/incartdb/
257HZ
75 records of 30 minutes, 12 leads [-4000, 4000]
Gains varying from 250 to 1100 analog-to-digital converter units per millivolt. Gains for each record are specified in its .hea file.
The reference annotation files contain over 175,000 beat annotations in all.
The original records were collected from patients undergoing tests for coronary artery disease (17 men and 15 women, aged 18-80; mean age: 58). None of the patients had pacemakers; most had ventricular ectopic beats. In selecting records to be included in the database, preference was given to subjects with ECGs consistent with ischemia, coronary artery disease, conduction abnormalities, and arrhythmias;observations of those selected included:
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ecgpuwave Also gives QRS onset, ofset, T-wave and P-wave NOTE:The beats whose Q and S points were not detected are considered as outliers and automatically rejected from our datasets.
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osea
The code of this repository is available under GNU GPLv3 license.