FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge
Detecting anomalies in electrocardiogram data is crucial to identifying deviations from normal heartbeat patterns and providing timely intervention to at-risk patients. Various AutoEncoder models (AE) have been proposed to tackle the anomaly detection task with ML. However, these models do not consider the specific patterns of ECG leads and are unexplainable black boxes. In contrast, we replace the decoding part of the AE with a reconstruction head (namely, FMM-Head) based on prior knowledge of the ECG shape. Our model consistently achieves higher anomaly detection capabilities than state-of-the-art models, up to 0.31 increase in area under the ROC curve (AUROC), with as little as half the original model size and explainable extracted features. The processing time of our model is four orders of magnitude lower than solving an optimization problem to obtain the same parameters, thus making it suitable for real-time ECG parameters extraction and anomaly detection.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Enhancing anomaly detection with topology-aware autoencoders
Anomaly detection in high-energy physics is essential for identifying new physics beyond the Standard Model. Autoencoders provide a signal-agnostic approach but are limited by the topology of their latent space. This wor…
Anomaly DetectionIterative Image Inpainting with Structural Similarity Mask for Anomaly Detection
Autoencoders have emerged as popular methods for unsupervised anomaly detection. Autoencoders trained on the normal data are expected to reconstruct only the normal features, allowing anomaly detection by thresholding re…
Anomaly DetectionImage InpaintingUnsupervised Anomaly DetectionModeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks
We propose a Quantum Federated Autoencoder for Anomaly Detection, a framework that leverages quantum federated learning for efficient, secure, and distributed processing in IoT networks. By harnessing quantum autoencoder…
Federated LearningAnomaly DetectionAnomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning
A novel approach to detecting anomalies in time series data is presented in this paper. This approach is pivotal in domains such as data centers, sensor networks, and finance. Traditional methods often struggle with manu…
Active LearningAnomaly DetectionDeep Reinforcement LearningTime Series+1An Attention-Augmented VAE-BiLSTM Framework for Anomaly Detection in 12-Lead ECG Signals
Anomaly detection in 12-lead electrocardiograms (ECGs) is critical for identifying deviations associated with cardiovascular disease. This work presents a comparative analysis of three autoencoder-based architectures: co…
Unsupervised Anomaly Detection