A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Enhanced deep CNNs improve cardiac abnormality diagnosis from ECGs.
We present a model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency. Such exams can provide a full evaluation of heart activity and have not been studied in previous end-to-end machine learning pap…
Deep neural network with attention detects multiple ECG abnormalities.
Early recognition of abnormal rhythms in ECG signals is crucial for monitoring and diagnosing patients' cardiac conditions, increasing the success rate of the treatment. Classifying abnormal rhythms into exact categories is very challenging due to the broad taxonomy of rhythms, noises and lack of large-scale real-world…
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are gr…
In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i.e., time varying spectrum) and deep convolutional networks. In the first step we use a Bayesian spectro-temporal representation based on the estimation of time-varying coeffic…
Study combines CNNs and LSTMs for ECG classification, improving performance with attention mechanisms.
Split learning preserves privacy in 1D CNN models for detecting heart abnormalities.
The high rate of false alarms in intensive care units (ICUs) is one of the top challenges of using medical technology in hospitals. These false alarms are often caused by patients' movements, detachment of monitoring sensors, or different sources of noise and interference that impact the collected signals from differen…
This paper presents an innovative and generic deep learning approach to monitor heart conditions from ECG signals.We focus our attention on both the detection and classification of abnormal heartbeats, known as arrhythmia. We strongly insist on generalization throughout the construction of a deep-learning model that tu…
Generative adversarial network system improves ECG arrhythmia classification.
LLT-ECG classifies ECG signals without backpropagation using linear laws.
Paper presents DL models for ECG signal denoising.
DDGM generates realistic ECG signals for clinical use.
Develops a method to denoise and analyze wearable ECGs.
Method generates natural ECGs with 25 interpretable features.
Recent introduction of wearable single-lead ECG devices of diverse configurations has caught the intrigue of the medical community. While these devices provide a highly affordable support tool for the caregivers for continuous monitoring and to detect acute conditions, such as arrhythmia, their utility for cardiac diag…
Self-supervised learning improves ECG classification performance.
LETS-GZSL tackles GZSL for time series classification, achieving high accuracy.
Method extracts time-localized clusters to explain deep learning models in ECG analysis.
Self-supervised ECG learning improves emotion recognition.
There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery. Based on the diffusion-based channel selection, here we present the mathematical formalism and clinical validation of an algorithm capable of accurate separation of maternal …
The multiple fundamental frequency detection problem and the source separation problem from a single-channel signal containing multiple oscillatory components and a nonstationary noise are both challenging tasks. To extract the fetal electrocardiogram (ECG) from a single-lead maternal abdominal ECG, we face both challe…
A deep learning algorithm for ECG segmentation.
Portable, Wearable and Wireless electrocardiogram (ECG) Systems have the potential to be used as point-of-care for cardiovascular disease diagnostic systems. Such wearable and wireless ECG systems require automatic detection of cardiovascular disease. Even in the primary care, automation of ECG diagnostic systems will …
Paper proposes a deep learning model for real-time ECG signal segmentation.
Paper establishes a comprehensive benchmark for ECG time-series analysis.
ECG-DelNet uses neural networks to accurately delineate ECGs, even with low-quality data.
Generates synthetic ECGs conditioned on clinical statements.
As the advancement of information security, human recognition as its core technology, has absorbed an increasing amount of attention in the past few years. A myriad of biometric features including fingerprint, face, iris, have been applied to security systems, which are occasionally considered vulnerable to forgery and…
We release a large ECG dataset for arrhythmia subtype discovery.
Structured state space models improve ECG classification and reveal new insights.
Deep learning benchmarks ECG analysis with strong performance.
A new model classifies multi-lead ECGs better than single-channel models.
We recently proposed a new ensemble clustering algorithm for graphs (ECG) based on the concept of consensus clustering. We validated our approach by replicating a study comparing graph clustering algorithms over benchmark graphs, showing that ECG outperforms the leading algorithms. In this paper, we extend our comparis…
Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems recently achieve promising progress to take over tedious examinations by cardiologists. However, their vulnerability to adversarial attacks still lack comprehensive investigation. The existing attacks in image domain could not be directly app…
We propose an ensemble clustering algorithm for graphs (ECG), which is based on the Louvain algorithm and the concept of consensus clustering. We validate our approach by replicating a recently published study comparing graph clustering algorithms over artificial networks, showing that ECG outperforms the leading algor…
Novel graph-based method detects R-peaks in noisy ECG signals without preprocessing.
Machine learning improves detection of Brugada Syndrome from ECGs.
Study finds AI can predict diverse cardiac and non-cardiac diagnoses from a single ECG.
1DCNN detects OSA from ECG signals with high accuracy.
Compact neural network for ECG classification reduces resource needs.
Traditional authentication systems use alphanumeric or graphical passwords, or token-based techniques that require "something you know and something you have". The disadvantages of these systems include the risks of forgetfulness, loss, and theft. To address these shortcomings, biometric authentication is rapidly repla…
This paper interprets neural network ECG models by breaking them into understandable components.
GeoECG augments ECG data to improve heart disease detection.
The electrocardiogram (ECG) is a widely-used medical test, typically consisting of 12 voltage versus time traces collected from surface recordings over the heart. Here we hypothesize that a deep neural network can predict an important future clinical event (one-year all-cause mortality) from ECG voltage-time traces. We…
The development of new technology such as wearables that record high-quality single channel ECG, provides an opportunity for ECG screening in a larger population, especially for atrial fibrillation screening. The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), …
AI detects heart disease from ECGs with improved interpretability and performance.