Novel graph-based method detects R-peaks in noisy ECG signals without preprocessing.
problem Detecting R-peaks in noisy ECG signals for real-time analysis.
method Graph-constrained Changepoint Detection (GCCD) approach.
result GCCD achieves high sensitivity, positive predictivity, and low detection error rate.
Paper tackles ECG signal classification with active learning.
problem Challenges in obtaining labeled ECG data and class label noise.
method Robust active learning approach with clustering and noisy label reduction.
result Demonstrates effectiveness of the proposed algorithm on ECG signal classification.
Compact neural network for ECG classification reduces resource needs.
problem Current reliance on deep learning for ECG analysis requires extensive resources and large datasets.
method Simple ANN architecture with advanced feature engineering.
result Achieved 97.36% accuracy in classifying 4 types of arrhythmias.
Generative adversarial network system improves ECG arrhythmia classification.
problem Improving automatic ECG arrhythmia classification accuracy.
method Generative adversarial network with patient-specific normal beats and generated abnormal beats.
result Superior overall classification performance for SVEB and VEB on MIT-BIH arrhythmia database.
This paper presents a novel signal compression algorithm based on the Blaschke unwinding adaptive Fourier decomposition (AFD). The Blaschke unwinding AFD is a newly developed signal decomposition theory. It utilizes the Nevanlinna factorization and the maximal selection principle in each decomposition step, and achieve…
A new model classifies multi-lead ECGs better than single-channel models.
problem Classifying multi-lead ECGs for accurate diagnosis.
method Channel-wise attention mechanism in CNNs for multi-lead ECG classification.
result The model achieves better sensitivity and precision than plain ResNet models.
This paper explores four different visualization techniques for long short-term memory (LSTM) networks applied to continuous-valued time series. On the datasets analysed, we find that the best visualization technique is to learn an input deletion mask that optimally reduces the true class score. With a specific focus o…
Efficient classifier with uncertainty bounds for safety-critical applications.
problem Lack of uncertainty bounds in high-accuracy classifiers for safety-critical tasks.
method Nadaraya-Watson estimator with frequentist bounds.
result Competitive accuracy and uncertainty bounds at reduced computational cost.
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…
Deep learning detects arrhythmias from ECGs using multidimensional representations.
problem Detecting arrhythmias from ECGs using traditional methods.
method Convert 1-D ECG data into 2-D images, then use deep learning for classification.
result Deep learning outperforms existing methods in arrhythmia detection.
Reservoir computing models classify ECG signals for patient-adaptive monitoring.
problem Classifying ECG signals for patients with imbalanced heartbeat classes.
method Reservoir computing paradigm applied to recurrent neural networks (RNNs).
result Accurate patient-adaptive ECG classifier that handles imbalanced classes.
Deep learning detects arrhythmia from RR-interval ECG data.
problem Diagnosing arrhythmia using ECG data.
method Convolutional neural network (CNN) on time-sliced RR-interval data.
result Compact system achieves accurate arrhythmia detection.
Deep learning model detects and classifies arrhythmia from ECG signals.
problem Detecting and classifying abnormal heartbeats (arrhythmia) from ECG signals.
method Use of topological data analysis in a modular neural network architecture for generalization.
result Model achieves state-of-the-art performance in arrhythmia detection and classification.
We release a large ECG dataset for arrhythmia subtype discovery.
problem Discovering unknown subtypes of arrhythmia from continuous raw signals.
method Unsupervised representation learning task using semi-supervised evaluation.
result Qualitative evaluations show potential for representation learning in arrhythmia sub-type discovery.
A new framework for semi-supervised learning using pseudo-representation labeling.
problem Improving deep learning models with limited labeled data.
method Pseudo-representation labeling framework integrating pseudo-labeling and self-supervised representation learning.
result Outperforms state-of-the-art semi-supervised learning methods in industrial classification problems.
Deep learning improves ECG arrhythmia classification.
problem Classifying ECG patterns accurately.
method Transfer learning from image classification to ECG spectrograms.
result 97.23% accuracy in classifying 7000 ECG instances.
Deep learning detects atrial fibrillation with high accuracy.
problem Detecting atrial fibrillation in ECG signals.
method Extracted deep features from spectrograms using convolutional networks.
result Convolutional network achieved 93.16% classification accuracy.
This work improves interpretability and calibration of complex-valued neural networks using Newton-Puiseux analysis.
problem Insufficient interpretability and probability calibration of complex-valued neural networks.
method Newton-Puiseux framework to examine local decision geometry, fitting a polynomial surrogate and factorizing it using Newton-Puiseux expansions.
result Enhanced Expected Calibration Error in ECG and wireless modulation datasets compared to uncalibrated softmax and standard post-hoc baselines.
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 cardiac arrhythmia detection in wearable devices with neural networks.
problem Resource constraints in low-power wearable devices for accurate arrhythmia detection.
method Adapted a convolutional-recurrent neural network to a low-power microcontroller, optimizing for precision and memory usage.
result Reduced F1 score from 0.8 to 0.784 in fixed-point precision, with a 195.6KB memory footprint and 33.98MOps/s throughput. We present algorithms for the detection of a class of heart arrhythmias with the goal of eventual adoption by practicing cardiologists. In clinical practice, detection is based on a small number of meaningful features extracted from the heartbeat cycle. However, techniques proposed in the literature use high dimensiona…
Paper tackles ICU false alarms by learning features from ECG signals.
problem High rate of false alarms in ICU due to patient movements and sensor detachment.
method Unsupervised feature learning to extract and cluster high-level features from ECG signals.
result The method reduces false arrhythmia alarms using a few high-level features from a single ECG lead.
A new model combines VAE and GAN for better anomaly detection in imbalanced datasets.
problem Anomaly detection in imbalanced datasets, especially in medical applications.
method β-VAEGAN model combining VAE and GAN, kernelized SVM for anomaly scores, and deviation from Gaussian prior.
result Improved F1 score from 0.85 to 0.92 on MITBIH Arrhythmia Database. Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to…
DeepBeat uses deep learning to assess signal quality and detect arrhythmia in wearable devices.
problem Detecting atrial fibrillation from wearable devices with noise.
method Multi-task deep learning approach using convolutional denoising autoencoders.
result Significantly improved AF detection accuracy compared to traditional methods.
Improved robustness of 1D CNNs for heart arrhythmia classification.
problem Improving the robustness of 1D CNNs for classification tasks.
method Parameterization using Cayley transform and controllability Gramian for Lipschitz-bounded CNNs.
result Improved robustness of trained Lipschitz-bounded 1D CNNs for heart arrhythmia classification.
Develops a method to interpret deep learning models by identifying key features.
problem Revealing the decision-making process of blackbox models from raw data to predictions.
method Adversarial attacks to localize discriminative features with statistical guarantees.
result Locally identified features are both biologically plausible and statistically significant.
We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localized destruc…
Aligns databases with Gaussian features using MAP estimation and thresholding.
problem Aligning databases with jointly Gaussian features.
method Two algorithms: MAP estimation and thresholding of log likelihood ratios.
result Conditions on mutual information for reliable performance of algorithms.
Personalized deep learning reduces inappropriate shocks in VA detection.
problem High inappropriate shock rate in traditional VA detection methods.
method Personalized deep learning framework using CNN for real-time VA detection and collaborative inference.
result 6.6% reduction in inappropriate shock rate compared to traditional methods.
System detects and predicts cardiac anomalies from ECG data.
problem Early detection of cardiac anomalies to reduce healthcare costs and mortality.
method Discrete Wavelet Transform (DWT) and Undecimated Wavelet Transform (UWT) for feature extraction; Bayesian Network Classifier for anomaly prediction.
result Average accuracy of 96.6% for PAC, 92.8% for MI, and 87% for PVC on real ECG datasets.
Study tackles database variability in medical data using ensemble models and CNNs.
problem Achieving robust generalization in machine learning models across multiple medical databases.
method Ensemble of local models based on convolutional neural networks (CNNs) and various data preprocessing techniques.
result Improved inter-database generalization performance and scalability of models.
Study connects database alignment and planted matching using Gaussian features.
problem Identify matching between correlated user features in anonymized databases.
method Derived results for database alignment and planted matching, showing connections and thresholds.
result Performance thresholds for database alignment converge to planted matching when feature dimensionality is sufficiently high.
This work applies SQL to deep learning, leveraging database techniques.
problem Applying deep learning techniques to databases.
method Expressing deep learning operations using SQL, a multidimensional array language.
result Demonstrates the feasibility of using SQL for deep learning operations.
Study compares CDS databases and finds discrepancies due to various factors.
problem Comparing discrepancies among CDS databases.
method Comparing five major sources of corporate CDS prices over 2004-2010.
result CMA quotes lead price discovery and databases disagree on stock-CDS return analysis.
Graph database outperforms in filtering ESG stocks efficiently.
problem Efficiently filtering ESG stocks from large lists of equities.
method Compared SQL, No-SQL, and graph databases; used Python for database interactions; collected data from stock price and financial news.
result Graph database is more efficient for ESG stock filtering.
CwA optimizes search performance by jointly learning a balanced database partition and a neural probing function.
problem Suboptimal search performance due to mismatched database and query distributions.
method CwA jointly learns a balanced database partition and a neural probing function to optimize search performance directly for the query distribution.
result CwA achieves up to 4.7x throughput over state-of-the-art methods at equal recall.
PyODDS is a Python system for outlier detection in databases.
problem Outlier detection in databases for various users.
method End-to-end Python system with database support, including statistical and deep learning algorithms.
result Supports outlier detection algorithms for users with or without machine learning background.
Taxonomies of cryptocurrencies and comparisons with fiat money and databases.
problem Comparing cryptocurrencies, fiat money, and databases.
method Taxonomy creation and comparison analysis.
result Cryptocurrencies and databases have unique characteristics not found in fiat money.
Bayesian entity resolution merges together multiple, noisy databases and returns the minimal collection of unique individuals represented, together with their true, latent record values. Bayesian methods allow flexible generative models that share power across databases as well as principled quantification of uncertain…
We present a database of parliamentary debates that contains the complete record of parliamentary speeches from Dáil Éireann, the lower house and principal chamber of the Irish parliament, from 1919 to 2013. In addition, the database contains background information on all TDs (Teachta Dála, members of parliament), such…
Paper improves text-to-SQL translation by encoding schema relations with self-attention.
problem Improving text-to-SQL translation accuracy across diverse databases.
method Uses relation-aware self-attention to encode schema information.
result Significant gains on Spider dataset (42.94% exact match accuracy).
Graph Neural Networks improve machine learning on relational databases.
problem Training machine learning models on relational databases requires costly data extraction and feature engineering.
method Uses Graph Neural Networks to extract features from relational databases.
result Outperforms state-of-the-art automatic feature engineering methods.
This paper explores how to fool ECG diagnosis systems with adversarial ECGs.
problem Vulnerability of DNN-powered ECG diagnosis systems to adversarial attacks.
method Analyzed ECG properties to design effective adversarial attacks under two models.
result Demonstrates weaknesses in DNN-powered ECG diagnosis systems under adversarial attacks.
New deep learning method validated across multiple sleep staging databases.
problem Improving automatic sleep scoring accuracy across different datasets.
method Ensemble of local models using deep learning for automatic sleep staging.
result Good general performance compared to human experts and state-of-the-art methods.
EERN uses deep learning for relational databases, outperforming other methods.
problem Deep learning for relational databases.
method Equivariant Entity-Relationship Network (EERN) using MLP equivariant to Entity-Relationship model symmetries.
result EERN outperforms other methods in synthetic and real-data experiments.
In this paper we deal with the offline handwriting text recognition (HTR) problem with reduced training datasets. Recent HTR solutions based on artificial neural networks exhibit remarkable solutions in referenced databases. These deep learning neural networks are composed of both convolutional (CNN) and long short-ter…
Extends Aff-Wild database for affect recognition in real-world settings.
problem Complex human emotional states in real-world settings.
method Developed deep neural architectures with attention mechanism for emotion recognition.
result Improved performance in emotion recognition using Aff-Wild2.