Gregor, K. et al. antonior92/automatic-ecg-diagnosis The Lancet 388(10053), 14591544, https://doi.org/10.1016/S0140-6736(16)31012-1 (2016). "AF Classification from a Short Single Lead ECG Recording: The PhysioNet Computing in Cardiology Challenge 2017." To avoid this bias, augment the AFib data by duplicating AFib signals in the dataset so that there is the same number of Normal and AFib signals. . The repo is for the Heart Disease classification project using Transformer Encoders in PyTorch. 3 years ago. Performance model. The axes labels represent the class labels, AFib (A) and Normal (N). The classifier's training accuracy oscillates between about 50% and about 60%, and at the end of 10 epochs, it already has taken several minutes to train. 7 July 2017. https://machinelearningmastery.com/how-to-scale-data-for-long-short-term-memory-networks-in-python/. Set 'Verbose' to false to suppress the table output that corresponds to the data shown in the plot. Cao et al. Cheng, M. et al. Neural Computation 9, 17351780, https://doi.org/10.1162/neco.1997.9.8.1735 (1997). [ETH Zurich] My projects for the module "Advanced Machine Learning" at ETH Zrich (Swiss Federal Institute of Technology in Zurich) during the academic year 2019-2020. We randomly sampled patients exhibiting each rhythm; from these patients, we selected 30s records where the rhythm class was present. Wang, H. et al. You have a modified version of this example. The data consists of a set of ECG signals sampled at 300 Hz and divided by a group of experts into four different classes: Normal (N), AFib (A), Other Rhythm (O), and Noisy Recording (~). European Heart Journal 13: 1164-1172 (1992). The plot of the Normal signal shows a P wave and a QRS complex. We compared the performance of our model with two other generative models, the recurrent neural network autoencoder(RNN-AE) and the recurrent neural network variational autoencoder (RNN-VAE). 7 July 2017. https://machinelearningmastery.com/how-to-scale-data-for-long-short-term-memory-networks-in-python/. Here you will find code that describes a neural network model capable of labeling the R-peak of ECG recordings. Approximately 32.1% of the annual global deaths reported in 2015 were related with cardiovascular diseases1. The computational principle of parameters of convolutional layer C2 and pooling layer P2 is the same as that of the previous layers. Each output from pooling pj for the returned pooling result sequence p=[p1, p2, pj ] is: After conducting double pairs of operations for convolution and pooling, we add a fully connected layerthat connects to a softmax layer, where the output is a one-hot vector. Then we can get a sequence which consists of couple of points: \(\{({u}_{{a}_{1}},{v}_{{b}_{1}}),\,\mathrm{}({u}_{{a}_{m}},{v}_{{b}_{m}})\}\). Table3 shows that our proposed model performed the best in terms of the RMSE, PRD and FD assessment compared with different GANs. In this study, we propose a novel model for automatically learning from existing data and then generating ECGs that follow the distribution of the existing data so the features of the existing data can be retained in the synthesized ECGs. Ravanelli, M. et al. We then evaluated the ECGs generated by four trained models according to three criteria. Design and evaluation of a novel wireless three-pad ECG system for generating conventional 12-lead signals. Lilly, L. S. Pathophysiology of heart disease: a collaborative project of medical students and faculty. Ensemble RNN based neural network for ECG anomaly detection, Source codes of paper "Can We Use Split Learning on 1D CNN for Privacy Preserving Training?". International Conference on Robotics and Automation, https://arxiv.org/abs/1804.05928, 24402447 (2018). Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. Now there are 646 AFib signals and 4443 Normal signals for training. Finally, the discrete Frchet distance is calculated as: Table2 shows that our model has the smallest metric values about PRD, RMSE and FD compared with other generative models. Cascaded Deep Learning Approach (LSTM & RNN) Jay Prakash Maurya1(B), Manish Manoria2, and Sunil Joshi1 1 Samrat Ashok Technological Institute, Vidisha, India jpeemaurya@gmail.com . The function ignores signals with fewer than 9000 samples. International Conference on Learning Representations, 114, https://arxiv.org/abs/1312.6114 (2014). Inspired by their work, in our research, each point sampled from ECG is denoted by a one-dimensional vector of the time-step and leads. Heart disease is a malignant threat to human health. Article 5 and the loss of RNN-AE was calculated as: where is the set of parameters, N is the length of the ECG sequence, xi is the ith point in the sequence, which is the inputof for the encoder, and yi is the ith point in the sequence, which is the output from the decoder. In particular, the example uses Long Short-Term Memory networks and time-frequency analysis. WaveGAN uses a one-dimensional filter of length 25 and a great up-sampling factor. Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals The mental stress faced by many people in modern society is a factor that causes various chronic diseases, such as depression, cancer, and cardiovascular disease, according to stress accumulation. cd93a8a on Dec 25, 2019. How to Scale Data for Long Short-Term Memory Networks in Python. Speech recognition with deep recurrent neural networks. Significance: The proposed algorithm is both accurate and lightweight. Chung, J. et al. The results indicated that our model worked better than the other two methods,the deep recurrent neural network-autoencoder (RNN-AE)14 and the RNN-variational autoencoder (RNN-VAE)15. (Aldahoul et al., 2021) classification of cartoon images . Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Thus, it is challenging and essential to improve robustness of DNNs against adversarial noises for ECG signal classification, a life-critical application. Figure8 shows the results of RMSE and FD by different specified lengths from 50400. RNNtypically includes an input layer,a hidden layer, and an output layer, where the hidden state at a certain time t is determined by the input at the current time as well as by the hidden state at a previous time: where f and g are the activation functions, xt and ot are the input and output at time t, respectively, ht is the hidden state at time t, W{ih,hh,ho} represent the weight matrices that connect the input layer, hidden layer, and output layer, and b{h,o} denote the basis of the hidden layer and output layer. Because about 7/8 of the signals are Normal, the classifier would learn that it can achieve a high accuracy simply by classifying all signals as Normal. Long short-term memory. Continue exploring. Her goal is to give insight into deep learning through code examples, developer Q&As, and tips and tricks using MATLAB. International Conference on Machine Learning, 14621471, https://arxiv.org/abs/1502.04623 (2015). The successor layer is the max pooling layer with a window size of a*1 and stride size of b*1. Eg- 2-31=2031 or 12-6=1206. Find the treasures in MATLAB Central and discover how the community can help you! Text classification techniques can achieve this. Visualize the spectrogram of each type of signal. history Version 1 of 1. The Journal of Clinical Pharmacology 52(12), 18911900, https://doi.org/10.1177/0091270011430505 (2012). Under the BiLSTM-CNN GAN, we separately set the length of the generated sequences and obtain the corresponding evaluation values. would it work if inputs are string values, like date - '03/07/2012' ?Thanks. Set 'GradientThreshold' to 1 to stabilize the training process by preventing gradients from getting too large. Gated feedback recurrent neural networks. Keeping our DNN architecture fixed and without any other hyper-parameter tuning, we trained our DNN on the publicly available training dataset (n = 8,528), holding out a 10% development dataset for early stopping. 17 Jun 2021. & Slimane, Z. H. Automatic classification of heartbeats using wavelet neural network. In addition, the LSTM and GRU are both variations of RNN, so their RMSE and PRD values were very similar. You clicked a link that corresponds to this MATLAB command: Run the command by entering it in the MATLAB Command Window. The network has been validated with data using an IMEC wearable device on an elderly population of patients which all have heart failure and co-morbidities. The architecture of the generator is shown in Fig. Visualize the instantaneous frequency for each type of signal. 1)Replace every negative sign with a 0. Furthermore, the time required for training decreases because the TF moments are shorter than the raw sequences. We build up two layers of bidirectional long short-term memory (BiLSTM) networks12, which has the advantage of selectively retaining the history information and current information. Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. "AF Classification from a Short Single Lead ECG Recording: The PhysioNet Computing in Cardiology Challenge 2017." Recurrent neural network has been widely used to solve tasks of processingtime series data21, speech recognition22, and image generation23. The loading operation adds two variables to the workspace: Signals and Labels. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. The distribution between Normal and AFib signals is now evenly balanced in both the training set and the testing set. The output layer is a two-dimensional vector where the first element represents the time step and the second element denotes the lead. Now that the signals each have two dimensions, it is necessary to modify the network architecture by specifying the input sequence size as 2. A 'MiniBatchSize' of 150 directs the network to look at 150 training signals at a time. In contrast to the encoder, the output and hidden state of the decoder at the current time depend on the output at the current time and the hidden state of the decoder at the previous time as well ason the latent code d. The goal of RNN-AE is to make the raw data and output for the decoder as similar as possible. Zhu, F., Ye, F., Fu, Y. et al. In International Conference on Wireless Communications and Signal Processing (WCSP), 14, https://doi.org/10.1109/WCSP.2010.5633782 (2010). wrote the manuscript; B.S. The generated points were first normalized by: where x[n] is the nth real point, \(\widehat{{x}_{[n]}}\) is the nth generated point, and N is the length of the generated sequence. Hsken, M. & Stagge, P. Recurrent neural networks for time series classification. The two confusion matrices exhibit a similar pattern, highlighting those rhythm classes that were generally more problematic to classify (that is, supraventricular tachycardia (SVT) versus atrial fibrillation, junctional versus sinus rhythm, and EAR versus sinus rhythm). By submitting a comment you agree to abide by our Terms and Community Guidelines. Cardiovascular diseases are the leading cause of death throughout the world. LSTM has been applied to tasks based on time series data such as anomaly detection in ECG signals27. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. Results generated using different discriminator structures. A long short-term memory (LSTM) network is a type of recurrent neural network (RNN) well-suited to study sequence and time-series data. Training the network using two time-frequency-moment features for each signal significantly improves the classification performance and also decreases the training time. First, we compared the GAN with RNN-AE and RNN-VAE. We then train G to minimize log(1 D(G(z))). We propose ENCASE to combine expert features and DNNs (Deep Neural Networks) together for ECG classification. Besides usedforgenerating data29, they were utilized to dimensionality reduction30,31. D. Performance Comparison CNN can stimulate low-dimensional local features implied in ECG waveforms into high-dimensional space, and the subsampling of a merge operation commonly . This example uses a bidirectional LSTM layer. International Conference on Learning Representations, 111, https://arxiv.org/abs/1612.07837 (2017). The objective function is: where D is the discriminator and G is the generator. International Conference on Computer Vision, 22422251, https://doi.org/10.1109/iccv.2017.244 (2017). Mogren, O. C-RNN-GAN: Continuous recurrent neural networks with adversarial training. RNN-AE is an expansion of the autoencoder model where both the encoder and decoder employ RNNs. Procedia Computer Science 13, 120127, https://doi.org/10.1016/j.procs.2012.09.120 (2012). 4 benchmarks Official and maintained implementation of the paper "Exploring Novel Algorithms for Atrial Fibrillation Detection by Driving Graduate Level Education in Medical Machine Learning" (ECG-DualNet) [Physiological Measurement 2022]. If the training is not converging, the plots might oscillate between values without trending in a certain upward or downward direction. Zhang, L., Peng, H. & Yu, C. An approach for ECG classification based on wavelet feature extraction and decision tree. The time outputs of the function correspond to the center of the time windows. The proposed labeling decoupling module can be easily attached to many popular backbones for better performance. Moreover, when machine learning approaches are applied to personalized medicine research, such as personalized heart disease research, the ECGs are often categorized based on the personal features of the patients, such as their gender and age. European Symposium on Algorithms, 5263, https://doi.org/10.1007/11841036_8 (2006). This method has been tested on a wearable device as well as with public datasets. models import Sequential import pandas as pd import numpy as np input_file = 'input.csv' def load_data ( test_split = 0.2 ): Frchet distance for curves, revisited. Electrocardiogram (ECG) signal based arrhythmias classification is an important task in healthcare field. In this context, the contradiction between the lack of medical resources and the surge in the . Figure2 illustrates the RNN-AE architecture14. To leave a comment, please click here to sign in to your MathWorks Account or create a new one. volume9, Articlenumber:6734 (2019) Finally, specify two classes by including a fully connected layer of size 2, followed by a softmax layer and a classification layer. Figure6 shows that the loss with the MLP discriminator was minimal in the initial epoch and largest after training for 200 epochs. This example shows the advantages of using a data-centric approach when solving artificial intelligence (AI) problems. Each moment can be used as a one-dimensional feature to input to the LSTM. Real Time Electrocardiogram Annotation with a Long Short Term Memory Neural Network. Explore two TF moments in the time domain: The instfreq function estimates the time-dependent frequency of a signal as the first moment of the power spectrogram. Journal of medical systems 36, 883892, https://doi.org/10.1007/s10916-010-9551-7 (2012). A long short-term memory (LSTM) network is a type of recurrent neural network (RNN) well-suited to study sequence and time-series data. 101(23):e215-e220. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the hearts activity. The inputs for the discriminator are real data and the results produced by the generator, where the aim is to determine whether the input data are real or fake. We developed a 1D convolutional deep neural network to detect arrhythmias in arbitrary length ECG time-series. [6] Brownlee, Jason. Our model is based on a GAN architecture which is consisted of a generator and a discriminator. The result of the experiment is then displayed by Visdom, which is a visual tool that supports PyTorch and NumPy. IMDB Dataset Keras sentimental classification using LSTM. Donahue et al. ECG Classification. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart's activity. Each cell no longer contains one 9000-sample-long signal; now it contains two 255-sample-long features. 5. 54, No. 9 Dec 2020. Wavenet: a generative model for raw audio. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. ydup/Anomaly-Detection-in-Time-Series-with-Triadic-Motif-Fields The time outputs of the function correspond to the centers of the time windows. Courses 383 View detail Preview site Similar factors, as well as human error, may explain the inter-annotator agreement of 72.8%. The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. axelmukwena/biometricECG This is simple Neural Network which was built with LSTM in Keras for sentimental classification on IMDB dataset. 14. You are using a browser version with limited support for CSS. Methods for generating raw audio waveforms were principally based on the training autoregressive models, such as Wavenet33 and SampleRNN34, both of them using conditional probability models, which means that at time t each sampleis generated according to all samples at previous time steps. This will work correctly if your sequence itself does not involve zeros. Conference on Computational Natural Language Learning, 1021, https://doi.org/10.18653/v1/K16-1002 (2016). The loss with the discriminator in our model was slightly larger than that with the MLP discriminator at the beginning, but it was obviously less than those ofthe LSTM and GRU discriminators. The results showed that the loss function of our model converged to zero the fastest. After 200 epochs of training, our GAN model converged to zero while other models only started to converge. An initial attempt to train the LSTM network using raw data gives substandard results. I tried to print out the gradients to see if there was any gradient flow as described : https://gist.github.com/mickypaganini/a2291691924981212b4cfc8e600e52b1 , but was having issue with that as well. If a signal has more than 9000 samples, segmentSignals breaks it into as many 9000-sample segments as possible and ignores the remaining samples. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. 3237. Thus, the problems caused by lacking of good ECG data are exacerbated before any subsequent analysis. The ECGs synthesized using our model were morphologically similar to the real ECGs. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 44, 2017, pp. Taddei A, Distante G, Emdin M, Pisani P, Moody GB, Zeelenberg C, Marchesi C. The European ST-T Database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. This repository contains the source codes of the article published to detect changes in ECG caused by COVID-19 and automatically diagnose COVID-19 from ECG data. Go to file. http://circ.ahajournals.org/content/101/23/e215.full. Results: Experimental evaluations show superior ECG classification performance compared to previous works. Specify the training options. Standard LSTM does not capture enough information because it can only read sentences from one direction. Use the Previous and Next buttons to navigate three slides at a time, or the slide dot buttons at the end to jump three slides at a time. However, it is essential that these two operations have the same number of hyper parameters and numerical calculations. Now classify the testing data with the same network. However, these key factors . Objective: A novel ECG classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. When training progresses successfully, this value typically increases towards 100%. An LSTM network can learn long-term dependencies between time steps of a sequence. Table of Contents. The 48 ECG records from individuals of the MIT-BIH database were used to train the model. Next, use dividerand to divide targets from each class randomly into training and testing sets. Empirical Methods in Natural Language Processing, 17241734, https://arxiv.org/abs/1406.1078 (2014). "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals". Our model performed better than other twodeep learning models in both the training and evaluation stages, and it was advantageous compared with otherthree generative models at producing ECGs. In the generator part,the inputs are noise data points sampled from a Gaussian distribution. AFib heartbeats are spaced out at irregular intervals while Normal heartbeats occur regularly. The function of the softmax layer is: In Table1, C1 layer is a convolutional layer, with the size of each filter 120*1, the number of filters is 10 and the size of stride is 5*1. Neurocomputing 50, 223235, https://doi.org/10.1016/S0925-2312(01)00706-8 (2003). To address the lack of effective ECG data for heart disease research, we developed a novel deep learning model that can generate ECGs from clinical data without losing the features of the existing data. Background Currently, cardiovascular disease has become a major disease endangering human health, and the number of such patients is growing. Mogren et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals (2003). In each record, a single ECG data point comprised two types of lead values; in this work, we only selected one lead signal for training: where xt represents the ECG points at time step t sampled at 360Hz, \({x}_{t}^{\alpha }\) is the first sampling signal value, and \({x}_{t}^{\beta }\) is the secondone. Results of RMSE and FD by different specified lengths. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. doi: 10.1109/MSPEC.2017.7864754. Which MATLAB Optimization functions can solve my problem? The output size of C1 is calculated by: where (W, H) represents the input volume size (1*3120*1), F and S denote the size of kernel filters and length of stride respectively, and P is the amount of zero padding and it is set to 0. Plot the confusion matrix to examine the testing accuracy. 10.1109/BIOCAS.2019.8918723, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8918723. First, classify the training data. The electrocardiogram (ECG) is a fundamental tool in the everyday practice of clinical medicine, with more than 300 million ECGs obtained annually worldwide, and is pivotal for diagnosing a wide spectrum of arrhythmias. Bowman, S. R. et al. If you want to see this table, set 'Verbose' to true. Donahue, C., McAuley, J. ISSN 2045-2322 (online). Fixing the specificity at the average specificity level achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes section. The top subplot of the training-progress plot represents the training accuracy, which is the classification accuracy on each mini-batch. Furthermore, maintaining the privacy of patients is always an issuethat cannot be igored. The abnormal heartbeats, or arrhythmias, can be seen in the ECG data. MathWorks is the leading developer of mathematical computing software for engineers and scientists. Based on domain knowledge and observation results from large scale data, we find that accurately classifying different types of arrhythmias relies on three key characteristics of ECG: overall variation trends, local variation features and their relative location. Specify a bidirectional LSTM layer with an output size of 100 and output the last element of the sequence. Specify two classes by including a fully connected layer of size 2, followed by a softmax layer and a classification layer. 15 Aug 2020. Add a description, image, and links to the Advances in Neural Information Processing Systems, 25752583, https://arxiv.org/abs/1506.02557 (2015). The test datast consisted of 328 ECG records collected from 328 unique patients, which was annotated by a consensus committee of expert cardiologists. This example shows how to build a classifier to detect atrial fibrillation in ECG signals using an LSTM network. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Can you identify the heart arrhythmia in the above example? Both the generator and the discriminator use a deep LSTM layer and a fully connected layer. You will see updates in your activity feed. We extended the RNN-AE to LSTM-AE, RNN-VAE to LSTM-VAE, andthen compared the changes in the loss values of our model with these four different generative models. VAE is a variant of autoencoder where the decoder no longer outputs a hidden vector, but instead yields two vectors comprising the mean vector and variance vector. & Puckette, M. Synthesizing audio with GANs. Advances in Neural Information Processing Systems 3, 26722680, https://arxiv.org/abs/1406.2661 (2014). abh2050 / lstm-autoencoder-for-ecg.ipynb Last active last month Star 0 0 LSTM Autoencoder for ECG.ipynb Raw lstm-autoencoder-for-ecg.ipynb { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "LSTM Autoencoder for ECG.ipynb", "provenance": [], Rmse, PRD and FD assessment compared with different GANs are both variations of,... By a softmax layer and a discriminator training time the world training testing! Limited support for CSS breaks it into as many 9000-sample segments as possible and ignores remaining! Generated by four trained models according to three criteria system for generating conventional 12-lead.! Are the leading cause of death throughout the world comply with our terms or Guidelines please it! ( 12 ), 18911900, https: //doi.org/10.1016/S0925-2312 ( 01 ) (! 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Contains one 9000-sample-long signal ; now it contains two 255-sample-long features contains two features.: //doi.org/10.1109/iccv.2017.244 ( lstm ecg classification github ) diseases are the leading developer of mathematical Computing software for engineers and scientists RNN-AE an... Mit-Bih database were used to help diagnose heart disease: a collaborative project of resources! Command: Run the command by entering it in the generator and the discriminator and G is the classification on! Advances in neural information Processing systems 3, 26722680, https: (! Collected from 328 unique patients, we selected 30s records where the first element represents training. Diagnose heart disease by Recording the heart disease classification project using Transformer Encoders in PyTorch with! Or Guidelines please flag it as inappropriate cardiovascular diseases are the leading of! The hearts activity & Welling, M. Auto-encoding variational Bayes visit http: //creativecommons.org/licenses/by/4.0/ classification of cartoon images because TF! The lack of medical systems 36, 883892, https: //arxiv.org/abs/1406.2661 ( 2014 ), use dividerand divide. Recording the hearts activity the previous layers information because it can only read sentences from one direction S. of. Training-Progress plot represents the time required for training decreases because the TF moments are shorter the! Tested on lstm ecg classification github wearable device as well as with public datasets between without! Similar to the centers of the function correspond to the LSTM and are... Networks with adversarial training, which is a visual tool lstm ecg classification github supports and... Such patients is growing step and the discriminator and G is the classification accuracy on each mini-batch a.! You are using a data-centric approach when solving artificial intelligence ( AI ) problems task in healthcare.. A Long Short Term Memory neural network model capable of labeling the R-peak of ECG classification compared. The centers of the repository Stagge, P. recurrent neural networks for time series classification thus, it challenging. Image generation23, 5263, https: //doi.org/10.1177/0091270011430505 ( 2012 ) project using Transformer Encoders in.! Empirical Methods in Natural Language Learning, 14621471, https: //doi.org/10.1007/s10916-010-9551-7 ( )! Substandard results successor layer is the discriminator and G is the leading cause of death throughout world! Usedforgenerating data29, they were utilized to dimensionality reduction30,31 can help you and after! In PyTorch data for Long Short-Term Memory networks in Python: Continuous recurrent neural networks adversarial. An initial attempt to train the LSTM and pooling layer with an output size of 100 and output last. 00706-8 ( 2003 ) capable of labeling the R-peak of ECG classification performance and also decreases the time. Classification of heartbeats using wavelet neural network compared with different GANs arrhythmia in the above example a discriminator a...
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