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.
Researchers use VAEs to create understandable heart beat representations.
A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a "heartbea…
Study shows DL models trained on healthy subjects perform worse on patients' ECG data.
This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional time-frequency fea…
We developed a new approach for the analysis of physiological time series. An iterative convolution filter is used to decompose the time series into various components. Statistics of these components are extracted as features to characterize the mechanisms underlying the time series. Motivated by the studies that show …
We describe a novel neural network architecture for the prediction of ventricular tachyarrhythmias. The model receives input features that capture the change in RR intervals and ectopic beats, along with features based on heart rate variability and frequency analysis. Patient age is also included as a trainable embeddi…
Heart rate estimation from electrocardiogram signals is very important for the early detection of cardiovascular diseases. However, due to large individual differences and varying electrocardiogram signal quality, there does not exist a single reliable estimation algorithm that works well on all subjects. Every algorit…
Develops a method to denoise and analyze wearable ECGs.
Language models are at the heart of numerous works, notably in the text mining and information retrieval communities. These statistical models aim at extracting word distributions, from simple unigram models to recurrent approaches with latent variables that capture subtle dependencies in texts. However, those models a…
N-BEATS-MOE improves time series forecasting by adapting to series characteristics.
Bayesian nonparametric method segments multi-sequence time series data.
Generative adversarial network system improves ECG arrhythmia classification.
Paper optimizes portfolio selection with ICX order constraints.
Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal heart sounds. Existing machine learning-based systems achieve accurate classificatio…
Framework for imputing missing heart data to simulate brain-heart interactions.
Smartphones can estimate heart rate from other sensor data.
A new model classifies multi-lead ECGs better than single-channel models.
Python models predict stock sentiment for market-beating returns.
We consider the problem of locating a point-source heart arrhythmia using data from a standard diagnostic procedure, where a reference catheter is placed in the heart, and arrival times from a second diagnostic catheter are recorded as the diagnostic catheter moves around within the heart. We model this situation as a …
Improved heart rate and activity recognition with low-power wrist sensors.
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 novel algorithm for actively trading stocks is presented. While traditional expert advice and "universal" algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirica…
Heart disease is the leading cause of death, and experts estimate that approximately half of all heart attacks and strokes occur in people who have not been flagged as "at risk." Thus, there is an urgent need to improve the accuracy of heart disease diagnosis. To this end, we investigate the potential of using data ana…
Photoplethysmogram (PPG) is increasingly used to provide monitoring of the cardiovascular system under ambulatory conditions. Wearable devices like smartwatches use PPG to allow long term unobtrusive monitoring of heart rate in free living conditions. PPG based heart rate measurement is unfortunately highly susceptible…
Proposes a novel anomaly detection method for echocardiogram videos.
Machine learning predicts exercise load from heart rate data post-exercise.
This study compares two neural models for financial forecasting, showing their superiority.
AI-assisted heart disease diagnosis reduces misdiagnosis and saves lives.
Novel method segments heart sound signals using LSTMs with attention.
Study predicts heart failure patient survival using stacked ensemble ML.
Interpretable survival analysis improves heart failure risk prediction.
Cardiovascular Disease (CVD) is considered as one of the principal causes of death in the world. Over recent years, this field of study has attracted researchers' attention to investigate heart sounds' patterns for disease diagnostics. In this study, an approach is proposed for normal/abnormal heart sound classificatio…
Adversarial policies beat superhuman Go AI systems.
The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording, and analyzing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods ha…
A CNN-based method improves DTI of the human heart, compensating for motion.
Developed shrinkage methods for Poisson regression models with experts to handle multicollinearity.
Study examines heart and football-shaped metrics, verifying geometric structure.
The study examines machine learning classification algorithms and their generalizability using Framingham Heart Study data.
Deep learning speeds up whole heart MRI to 30 seconds.
IBPF algorithm tackles high-dimensional parameter learning for complex systems.
N-BEATS(P) efficiently forecasts millions of time series with reduced memory and time.
Machine learning improves CHD screening accuracy from 70% to 87.7%.
Improved robustness of 1D CNNs for heart arrhythmia classification.
Causal analysis reveals regional discrepancies in TOPCAT trial results.
New scalable method balances hospital profit status and heart attack outcomes.
The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV). Thus, continuous monitoring of HRV can contribute to determining and predicting issues in well-being and mental health. HRV can be measured in everyday life by consumer wear…
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…
An electrocardiogram (EKG) is a common, non-invasive test that measures the electrical activity of a patient's heart. EKGs contain useful diagnostic information about patient health that may be absent from other electronic health record (EHR) data. As multi-dimensional waveforms, they could be modeled using generic mac…