2 research outputs found
Abductive reasoning as the basis to reproduce expert criteria in ECG Atrial Fibrillation identification
Objective: This work aims at providing a new method for the automatic
detection of atrial fibrillation, other arrhythmia and noise on short single
lead ECG signals, emphasizing the importance of the interpretability of the
classification results.
Approach: A morphological and rhythm description of the cardiac behavior is
obtained by a knowledge-based interpretation of the signal using the
\textit{Construe} abductive framework. Then, a set of meaningful features are
extracted for each individual heartbeat and as a summary of the full record.
The feature distributions were used to elucidate the expert criteria underlying
the labeling of the 2017 Physionet/CinC Challenge dataset, enabling a manual
partial relabeling to improve the consistency of the classification rules.
Finally, state-of-the-art machine learning methods are combined to provide an
answer on the basis of the feature values.
Main results: The proposal tied for the first place in the official stage of
the Challenge, with a combined score of 0.83, and was even improved in
the follow-up stage to 0.85 with a significant simplification of the model.
Significance: This approach demonstrates the potential of \textit{Construe}
to provide robust and valuable descriptions of temporal data even with
significant amounts of noise and artifacts. Also, we discuss the importance of
a consistent classification criteria in manually labeled training datasets, and
the fundamental advantages of knowledge-based approaches to formalize and
validate that criteria.Comment: 15 pages, 6 figures, 6 table
Detecting and interpreting myocardial infarction using fully convolutional neural networks
Objective: We aim to provide an algorithm for the detection of myocardial
infarction that operates directly on ECG data without any preprocessing and to
investigate its decision criteria. Approach: We train an ensemble of fully
convolutional neural networks on the PTB ECG dataset and apply state-of-the-art
attribution methods. Main results: Our classifier reaches 93.3% sensitivity and
89.7% specificity evaluated using 10-fold cross-validation with sampling based
on patients. The presented method outperforms state-of-the-art approaches and
reaches the performance level of human cardiologists for detection of
myocardial infarction. We are able to discriminate channel-specific regions
that contribute most significantly to the neural network's decision.
Interestingly, the network's decision is influenced by signs also recognized by
human cardiologists as indicative of myocardial infarction. Significance: Our
results demonstrate the high prospects of algorithmic ECG analysis for future
clinical applications considering both its quantitative performance as well as
the possibility of assessing decision criteria on a per-example basis, which
enhances the comprehensibility of the approach.Comment: 11 pages, 4 figure