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.
Reservoir computing models classify ECG signals for patient-adaptive monitoring.
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 …
DDGM generates realistic ECG signals for clinical use.
Paper proposes a deep learning model for real-time ECG signal segmentation.
A deep learning algorithm for ECG segmentation.
Continuous monitoring of cardiac health under free living condition is crucial to provide effective care for patients undergoing post operative recovery and individuals with high cardiac risk like the elderly. Capacitive Electrocardiogram (cECG) is one such technology which allows comfortable and long term monitoring t…
In recent years, the electrocardiogram (ECG) has seen a large diffusion in both medical and commercial applications, fueled by the rise of single-lead versions. Single-lead ECG can be embedded in medical devices and wearable products such as the injectable Medtronic Linq monitor, the iRhythm Ziopatch wearable monitor, …
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…
With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs). However, it becomes increasingly challenging to model such data, due to …
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…
Deep learning model detects and classifies arrhythmia from ECG signals.
Mobile electrocardiogram (ECG) recording technologies represent a promising tool to fight the ongoing epidemic of cardiovascular diseases, which are responsible for more deaths globally than any other cause. While the ability to monitor one's heart activity at any time in any place is a crucial advantage of such techno…
Novel graph-based method detects R-peaks in noisy ECG signals without preprocessing.
Paper identifies resting positions using EGG, ECG, respiration rate, and SpO2.
A new model classifies multi-lead ECGs better than single-channel models.
Deep learning detects arrhythmia from RR-interval ECG data.
Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system. Recently, there has been a great attention towards accurate categorization of heartbeats. While there are many commonalities between different ECG conditions, the focus of most studies has been classifyi…
Study reviews machine learning techniques for stress monitoring.
UniPhyNet improves cognitive load classification accuracy using EEG, ECG, and EDA signals.
LLT-ECG classifies ECG signals without backpropagation using linear laws.
Paper presents DL models for ECG signal denoising.
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…
Develops a method to denoise and analyze wearable ECGs.
Method generates natural ECGs with 25 interpretable features.
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…
Self-supervised learning improves ECG classification performance.
Method extracts time-localized clusters to explain deep learning models in ECG analysis.
Self-supervised ECG learning improves emotion recognition.
Deep neural network with attention detects multiple ECG abnormalities.
Enhanced deep CNNs improve cardiac abnormality diagnosis from ECGs.
Smartphones can estimate heart rate from other sensor data.
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…
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 establishes a comprehensive benchmark for ECG time-series analysis.
Enhanced ECG biometric system improves authentication accuracy.
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.
Fluctuations in heart rate are intimately tied to changes in the physiological state of the organism. We examine and exploit this relationship by classifying a human subject's wake/sleep status using his instantaneous heart rate (IHR) series. We use a convolutional neural network (CNN) to build features from the IHR se…
Structured state space models improve ECG classification and reveal new insights.
Deep learning benchmarks ECG analysis with strong performance.
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…
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.