Electrocardiography (ECG) is a key non-invasive diagnostic tool for cardiovascular diseases which is increasingly supported by algorithms based on machine learning. Major obstacles for the development of automatic ECG interpretation algorithms are both the lack of public datasets and well-defined benchmarking procedures to allow comparison s of different algorithms. To address these issues, we put forward PTB-XL, the to-date largest freely accessible clinical 12-lead ECG-waveform dataset comprising 21837 records from 18885 patients of 10 seconds length. The ECG-waveform data was annotated by up to two cardiologists as a multi-label dataset, where diagnostic labels were further aggregated into super and subclasses. The dataset covers a broad range of diagnostic classes including, in particular, a large fraction of healthy records. The combination with additional metadata on demographics, additional diagnostic statements, diagnosis likelihoods, manually annotated signal properties as well as suggested folds for splitting training and test sets turns the dataset into a rich resource for the development and the evaluation of automatic ECG interpretation algorithms. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12098055

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PTB-XL, a large publicly available electrocardiography dataset

Deep Learning (DL) has recently become a topic of study in different applications including healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a vital role in patient monitoring. This paper presents a comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes. This study considers various types of the DL methods such as Convolutional Neural Network (CNN), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). From the 75 studies reported within 2017 and 2018, CNN is dominantly observed as the suitable technique for feature extraction, seen in 52% of the studies. DL methods showed high accuracy in correct classification of Atrial Fibrillation (AF) (100%), Supraventricular Ectopic Beats (SVEB) (99.8%), and Ventricular Ectopic Beats (VEB) (99.7%) using the GRU/LSTM, CNN, and LSTM, respectively.

A Review on Deep Learning Methods for ECG Arrhythmia Classification

Objective: Vast 12-lead ECGs repositories provide opportunities to develop new machine learning approaches for creating accurate and automatic diagnostic systems for cardiac abnormalities. \However, most 12-lead ECG classification studies are trained, tested, or developed in single, small, or relatively homogeneous datasets. In addition, most algorithms focus on identifying small numbers of cardiac arrhythmias that do not represent the complexity and difficulty of ECG interpretation. This work addresses these issues by providing a standard, multi-institutional database and a novel scoring metric through a public competition: the PhysioNet/Computing in Cardiology Challenge 2020. Approach: A total of 66361 12-lead ECG recordings were sourced from six hospital systems from four countries across three continents. 43,101 recordings were posted publicly with a focus on 27 diagnoses. For the first time in a public competition, we required teams to publish open-source code for both training and testing their algorithms, ensuring full scientific reproducibility. Main results: A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry. As with previous Challenges, high-performing algorithms exhibited significant drops (≈ 10%) in performance on the hidden test data. Significance: Data from diverse institutions allowed us to assess algorithmic generalizability. A novel evaluation metric considered different misclassification errors for different cardiac abnormalities, capturing the outcomes and risks of different diagnoses. Requiring both trained models and code for training models improved the generalizability of submissions, setting a new bar in reproducibility for public data science competitions.

Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020.

Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review.

Electrocardiography (ECG) is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by algorithms. The progress in the field of automatic ECG analysis has up to now been hampered by a lack of appropriate datasets for training as well as a lack of well-defined evaluation procedures to ensure comparability of different algorithms. To alleviate these issues, we put forward first benchmarking results for the recently published, freely accessible clinical 12-lead ECG dataset PTB-XL, covering a variety of tasks from different ECG statement prediction tasks to age and sex prediction. Among the investigated deep-learning-based timeseries classification algorithms, we find that convolutional neural networks, in particular resnet- and inception-based architectures, show the strongest performance across all tasks. We find consistent results on the ICBEB2018 challenge ECG dataset and discuss prospects of transfer learning using classifiers pretrained on PTB-XL. These benchmarking results are complemented by deeper insights into the classification algorithm in terms of hidden stratification, model uncertainty and an exploratory interpretability analysis, which provide connecting points for future research on the dataset. Our results emphasize the prospects of deep-learning-based algorithms in the field of ECG analysis, not only in terms of quantitative accuracy but also in terms of clinically equally important further quality metrics such as uncertainty quantification and interpretability. With this resource, we aim to establish the PTB-XL dataset as a resource for structured benchmarking of ECG analysis algorithms and encourage other researchers in the field to join these efforts.

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Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL

An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection

Atrial fibrillation (AF) is a serious cardiovascular disease with the phenomenon of beating irregularly. It is the major cause of variety of heart diseases, such as myocardial infarction. Automatic AF beat detection is still a challenging task which needs further exploration. A new framework, which combines modified frequency slice wavelet transform (MFSWT) and convolutional neural networks (CNNs), was proposed for automatic AF beat identification. MFSWT was used to transform 1 s electrocardiogram (ECG) segments to time-frequency images, and then, the images were fed into a 12-layer CNN for feature extraction and AF/non-AF beat classification. The results on the MIT-BIH Atrial Fibrillation Database showed that a mean accuracy (Acc) of 81.07% from 5-fold cross validation is achieved for the test data. The corresponding sensitivity (Se), specificity (Sp), and the area under the ROC curve (AUC) results are 74.96%, 86.41%, and 0.88, respectively. When excluding an extremely poor signal quality ECG recording in the test data, a mean Acc of 84.85% is achieved, with the corresponding Se, Sp, and AUC values of 79.05%, 89.99%, and 0.92. This study indicates that it is possible to accurately identify AF or non-AF ECGs from a short-term signal episode.

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Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks.

Atrial fibrillation (AF) can cause a variety of heart diseases and its detection is insufficient in outside hospital. We proposed three methods for AF diagnosis in ambulatory settings. The first method is a convolutional neural network (CNN) trained on modified frequency slice wavelet transform (MFSWT) data. The second is a support vector machine (SVM) classifier trained on multiple AF features data. The third method is an SVM trained on the same feature set but extended by the predictive probability of the CNN. The proposed method (the third one) achieved the highest detection accuracy. MIT-BIH AF database was used as a training set with an accuracy of 97.87% for 30-s ECG episodes and 96.09% for 10-s ECG episodes from fivefold cross-validation. The trained model was tested on the PhysioNet/Computing in Cardiology (CinC) Challenge 2017 database, achieving an accuracy of 93.21% for 30-s episodes and 93.03% for 10-s ECG episodes. When tested on the China Physiological Signal Challenge (CPSC) 2018 database, the corresponding accuracies were 98.48% and 98.61%. The results on the wearable ECGs from a clinical AF patient were 99.21% and 97.04%. We retrained the model on the PhysioNet/CinC Challenge 2017 data set and tested on the other database to explore the generalization ability of the proposed method. Corresponding test results on the MIT-BIH AF database showed accuracies of 96.84% and 95.13%, on the CPSC 2018 database were 96.21% and 98.45%, on the wearable ECGs were 99.08% and 96.43%. The results proved that the proposed method could provide high accuracy and reliable recognition for AF events.

Integration of Results From Convolutional Neural Network in a Support Vector Machine for the Detection of Atrial Fibrillation

Atrial fibrillation (AF) is a progressive disease often initially manifested by intermittent episodes spontaneously terminating and is an insidious disease. The previous work trained the support vector machine (SVM) classifier on multiple RR interval features. The trained AF detector was tested on the fourth China Physiological Signal Challenge (CPSC 2021) database, achieving 97.59% and 89.83% for sensitivity and specificity on dataset 1, respectively. The test results were 96.46% and 78.64% on dataset 2, respectively. The results show that the AF detector based on rhythm is insufficient for the recognition of non-AF. Therefore, this work developed AF scanning algorithm integrating rhythm and P-wave information for long-term ECGs. The proposed algorithm is divided into three steps. First, utilize $a$ priori knowledge to locate suspected AF, and then, employ a trained rhythm-based AF detector to detect AF. Finally, adopt the dynamic time warping (DTW) and an autoencoding (AE) network to quantize the P-wave information to identify the non-AF signal from AF. The results on dataset 1 were 97.44% and 98.50% and on dataset 2 were 96.13% and 87.42%. This work scanned 11 patients with 24-h paroxysmal AF (PAF). The best result is that the detection accuracy is 99.33%, and the false detection is 0.01%, and the worst outcome is that the detection accuracy is 88.75%, and the false detection is 14.06%. The results proved that the proposed method could provide reliable scanning for PAF events.

A Multistep Paroxysmal Atrial Fibrillation Scanning Strategy in Long-Term ECGs

Atrial fibrillation (AF) is the most common and sustained heart rhythm disorder, increasing the risk of stroke and death, and its incidence is destined to increase as the population ages Current diagnostic methods are primarily through symptom or other indirect medical assessment methods The fast-developing wearable technologies significantly promote the progress in ambulatory electrocardiogram (ECG) monitoring This is a challenge to develop the devices that can detect AF in wearable electronic devices, with accessibility, sensitivity, ease of use, low-cost efficiency, and high computing power Here, we first give a brief introduction to physiological concepts for development of detection algorithms Then, we describe several kinds of AF features in dynamic signals These features are important part of the automatic detection of AF, and a thorough understanding of these concepts can help researchers gain better insight into AF detection Finally, seven AF features were extracted from the RR interval time series and were input into a SVM model to train AF/non-AF classifier The results on the wearable ECGs verified that the proposed model could provide good identification for AF events

Caiyun Ma

Papers

An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection

Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks.

Integration of Results From Convolutional Neural Network in a Support Vector Machine for the Detection of Atrial Fibrillation

A Multistep Paroxysmal Atrial Fibrillation Scanning Strategy in Long-Term ECGs

Atrial Fibrillation Detection in Dynamic Signals