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matlab\output\one_vs_one_C-0.0001.txt (67, 2018-06-01)
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matlab\prepare_data_incartdb.m (8735, 2018-06-01)
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# ECG Classification NOTE: if this code is usefull for you, cite our work as: ## Description Code for training and test **MIT-BIH Arrhythmia Database** with: 1. [Support Vector Machine (**SVM**) on Python](matlab/README.md). 2. [Support Vector Machine (**SVM**) on MATLAB (*old*)](matlab/README.md). 3. [Artificial Neural Networks (**ANNs**) on TensorFlow (*old*)](tensorflow/README.md) The data is splited following the **inter-patient** scheme proposed by [Chazal *et al*](http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1306572)., i.e the training and eval set not contain any patient in common. ## Method This code classifies the signal at beat-level following the class labeling of the [AAMI recomendation](###aami-recomendation-for-mit). ### 1 Preprocess: 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. ```python 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. ```python # Remove Baseline for i in range(0, len(MLII)): MLII[i] = MLII[i] - baseline[i] ``` ### 2 Segmentation: Beat Detection 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](#software-references:-beat-detection)). ### 3 Feature Descriptor In order to describe the beats for classification purpose, we employ the following features: 1. **Morphological**: for this features a window of [-90, 90] is centred along the R-peak: 0. **RAW-Signal** (180): is the most simplier descriptor. Just employ the amplitude values from the signal delimited by the window. 1. **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. ```python import pywt ... db1 = pywt.Wavelet('db1') coeffs = pywt.wavedec(beat, db1, level=3) wavel = coeffs[0] ``` 2. **HOS** (10): extracted from 3-4th order cumulant, skewness and kurtosis. ```python 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) ``` 3. **U-LBP 1D (59)** 1D version of the popular LBP descriptor. Using the uniform patterns with neighbours = 8 ```python 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 ``` 4. **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]) ```python 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]) ``` 2. **Interval RR** (4): intervals computed from the time between consequent beats. There are the most common feature employed for ECG classification. 1. pre_RR 2. post_RR 3. local_RR 4. global_RR 3. **Normalized RR** (4): RR interval normalized by the division with the AVG value from each patient. 1. pre_RR / AVG(pre_RR) 2. post_RR / AVG(post_RR) 3. local_RR / AVG(local_Python (Scikit-learn) 4. 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*. Extracted from: Weighted Conditional Random Fields for Supervised Interpatient Heartbeat Classification* ### 4 Normalization of the features Before train the models. All the input data was standardized with [z-score](https://en.wikipedia.org/wiki/Standard_score), i.e., the values of each dimension are divided by its standard desviation and substracted by its mean. ```python 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) ``` ### 5 Training and Test In scikit-learn the multiclass SVM support is handled according to a [one-vs-one](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) 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. ```python 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](https:doi.org/10.1109/TBME.2011.2113395)) were employed as performance measure ```python 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) ``` ### 6 Combining Ensemble of SVM Several basic combination rules can be employed to combine the decision from different SVM model configurations in a single prediction (see basic_fusion.py) ### 7 Comparison with state-of-the-art on MITBIH database: | Classifier | Acc. | Sens. | jk index | |---------------------|------|-------|----------| | Our Ensemble of SVMs| **0.945**| 0.703 | **0.773** | | [Zhang et al.](https://doi.org/10.1016/j.compbiomed.2013.11.019) | 0.883| **0.868** | 0.663 | | Out Single SVM | 0.884| 0.696 | 0.***0 | | [Mar et al.](https://doi.org/10.1109/TBME.2011.2113395)| 0.899| 0.802 | 0.***9 | | [Chazal et al.](https://doi.org/10.1109/TBME.2004.827359) | 0.862| 0.832 | 0.612 | # About datasets: https://physionet.org/cgi-bin/atm/ATM #### 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/mitdb ``` ``` rsync -Cavz physionet.org::incartdb /home/mondejar/dataset/ECG/incartdb ``` Finally to convert the data as plain text files use [convert_wfdb_data_2_csv.py](https://github.com/mondejar/WFDB_utils_and_others/blob/master/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](https://github.com/mondejar/WFDB_utils_and_others) for more info about WFDB database conversion and the original site from [Physionet_tools](https://www.physionet.org/physiotools/wag/wag.htm). #### Download via Kaggle You can also download the raw signals (.csv) and annotations files from [kaggle.com/mondejar/mitbih-database](https://www.kaggle.com/mondejar/mitbih-database) # MIT-Arrythmia Database 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 | || | Isolated QRS-like artifact | [beats and rhythms](https://physionet.org/physiobank/database/html/mitdbdir/tables.htm#allrhythms) ||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 ### AAMI recomendation for MIT 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*](http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1306572)): 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| # INCART Database 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: # Software references: Beat Detection 1. [*Pan Tompkins*](https://es.mathworks.com/matlabcentral/fileexchange/45840-complete-pan-tompkins-implementation-ecg-qrs-detector) [third_party/Pan_Tompkins_ECG_v7/pan_tompkin.m](third_party/Pan_Tompkins_ECG_v7/pan_tompkin.m) 2. [*ecgpuwave*](third_party/README.md) 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.* 3. [ex_ecg_sigprocessing](https://es.mathworks.com/help/dsp/examples/real-time-ecg-qrs-detection.html) 4. osea

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