Multi-Label ECG Classification using Temporal Convolutional Neural Network

Automated analysis of 12-lead electrocardiogram (ECG) plays a crucial role in the early screening and management of cardiovascular diseases (CVDs). In practice, it is common to see multiple co-occurring cardiac disorders, i.e., multi-label or multimorbidity in patients with CVDs, which increases the risk for mortality. Most current research focuses on the single-label ECG classification, i.e., each ECG record corresponds to one cardiac disorder, ignoring ECG records with multi-label phenomenon. In this paper, we propose an ensemble of attention-based temporal convolutional neural network (ATCNN) models for the multi-label classification of 12-lead ECG records. Specifically, a set of ATCNN-based single-lead binary classifiers are trained one for each cardiac disorder, and the predictions from these classifiers with simple thresholding generate the final multi-label decisions. The ATCNN contains a stack of TCNN layers followed by temporal and spatial attention layers. The TCNN layers operate at different dilation rates to represent the multi-scaled pathological ECG features dynamics, and attention layers emphasize/reduce the diagnostically relevant/redundant 12-lead ECG information. The proposed framework is evaluated on the PTBXL-2020 dataset and achieved an average F1-score of 76.51%. Moreover, the spatial and temporal attention weights visualization provides the optimal ECG-lead subset selection for each disease and model interpretability results, respectively. The improved performance and interpretability of the proposed approach demonstrate its ability to screen multimorbidity patients and help clinicians initiate timely treatment.Comment: Under review for publication in the IEEE Journal (8 pages, 6 figures

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function

Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abnormalities. Early and accurate diagnosis will allow early treatment and intervention to prevent severe complications of cardiovascular disease. In the PhysioNet/Computing in Cardiology Challenge 2020, our objective is to develop an algorithm that automatically identifies 27 ECG abnormalities from 12-lead ECG recordings

Uncertainty-Aware AI for ECG arrhythmia multi-label classification

Machine Learning (ML) models are able to predict a variety of diseases, with performances that can be superior to those achieved by healthcare professionals. However, when implemented in clinical settings as decision support systems, their generalisation capabilities are often compromised, rendering healthcare professionals more susceptible into delivering erroneous diagnostics. This research focuses on uncertainty measures as a key method to abstain from classifying samples with high uncertainty as well as a selection criterion for active learning strategies. For this purpose, it was employed four large public multi-label Electrocardiogram (ECG) databases for the classification of cardiac arrhythmias. Regarding the uncertainty measures, single distribution uncertainty and classical information-theoretic measures of entropy were tested and compared. Thus, three Deep Learning models were developed: a single convolutional neural network and two multiple-models using Monte-Carlo Dropout and Deep Ensemble techniques. When tested with samples from the same database used for training, all models achieved performances higher than 95% for F1-score. However, when tested on an external dataset, their performances dropped to approximately 70%, indicating a probable scenario of dataset shift. The Deep Ensemble model obtained the highest F1-score in both test sets with a maximum difference of 3% from the others. The classification withrejection option increased from a rejection of10% to a range between 30% to 50% depending on the model or uncertainty measure, with the highest rejection rates being obtained on external data. This reveals that external dataset’s classifications have higher uncertainty, also an indication of dataset shift. For the active learning approach, 10% of the highest uncertainty sampleswere used to retrain the models. The performances results increased by almost 5%, suggesting uncertainty as a good selection method. Although there are still challenges to the implementation of ML models, the preliminary studies show that uncertainty quantification is a valuable method for classification with rejection option and active learning approaches under dataset shift conditions.Modelos de aprendizagem automática conseguem prever um leque de doenças, muitas vezes com desempenhos superiores aos obtidos pelos profissionais de saúde. Contudo, quando integrados em ambientes clínicos como sistemas de apoio à decisão, a generalização destes fica comprometida, o que leva a que profissionais de saúde fiquem mais suscetíveis de fornecer diagnósticos incorretos. Deste modo, este projeto foca-se no papel da incerteza na rejeição de classificações com elevada incerteza e na aprendizagem ativa. Quatro bases de dados públicas de sinais ECG multi-label foram utilizadas na classificação de arritmias cardíacas. Relativamente à quantificação da incerteza, foram testadas e comparadas incertezas provenientes das distribuições e da teoria de informação clássica da entropia. Para tal, foram desenvolvidos três tipos de redes neurais convolucionais: um modelo único e dois modelos obtidos através das técnicas de Monte-Carlo Dropout e Deep Ensemble. Quando testados com dados da mesma base de dados de treino, os modelos alcançaram desempenhos superiores a 95% de F1-score. No entanto, quando testados com dados externos, os desempenhos desceram para cerca de 70%, revelando a possibilidade de dataset shift. O modelo Deep Ensemble obteve os melhores resultados em ambos os dados de teste, com uma diferença máxima de 3% em relação aos outros modelos. O threshold de rejeição de 10% em treino aumentou para valores entre 30% a 50%, dependendo do modelo e da medida de incerteza, sendo que as rejeições mais elevadas são obtidas nos dados externos. Isto revela que estes dados têm maior incerteza nas suas classificações, confirmando a presença de dataset shift. Para a abordagem de aprendizagem ativa, 10% de dados com elevada incerteza foram utilizados para retreinar os modelos. O desempenho destes aumentou quase 5%, sugerindo a incerteza como um bom critério de seleção. Apesar de ainda existirem desafios na implementação de modelos de aprendizagem automática, os resultados preliminares revelam que a quantificação da incerteza é um método valioso na classificação com rejeição e na aprendizagem ativa, em condições de dataset shift