ACQuA: Anomaly Classification with Quasi-Attractors
Rudman, W.; Merullo, J.; Mercurio, L.; Eickhoff, C.
Show abstract
In recent years, deep learning has redefined algorithms for detecting cardiac abnormalities. However, many state of the art algorithms still rely on calculating handcrafted features from a given heart signal that are then fed into shallow 1D convolutional networks or transformer architectures. We propose ACQuA (Anomaly Classification with Quasi Attractors), a task agnostic algorithm that can be used in a wide variety of cardiac settings, from classifying cardiac arrhythmias from ECG signals to detecting heart murmurs from PCG signals. Using theorems from dynamical analysis and topological data analysis, we create informative attractor images that 1) are human distinguishable and 2) can be used to train small, off the shelf deep neural networks for anomaly classification. In the George B. Moody 2022 Challenge, we receive an official score of 0.433 (263/305) for murmur classification and a score of 12616 (208/305) for outcome classification. Additionally, we evaluate our model on the CinC 2017 Challenge data that tasks practitioners to classify cardiac arrhythmias from ECG signals. On the CinC 2017 Challenge data, we improve upon the winning F1 scores by approximately 14% on the hidden validation data.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- STAMP: Simultaneous Training and Model Pruning for Low Data Regimes in Medical Image Segmentation 94%
- Strain estimation in aortic roots from 4D echocardiographic images using medial modeling and deformable registration 93%
- A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in resting-state functional MRI Data 92%
Similar papers in this journal
- Self-Supervised Electrocardiograph De-noising 95%
- HeartNet: Self Multi-Head Attention Mechanism via Convolutional Network with Adversarial Data Synthesis for ECG-based Arrhythmia Classification 94%
- A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning 92%
Similar papers in this journal
Similar papers in this journal
- BRAVEHEART: Open-source software for automated electrocardiographic and vectorcardiographic analysis 93%
- Digitizing ECG image: new fully automated method and open-source software code 92%
- An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.