Myocardial Infarction

A deep learning algorithm for detecting acute myocardial infarction.

BACKGROUND: Delayed diagnosis or misdiagnosis of acute myocardial infarction (AMI) is not unusual in daily practice. Since a 12-lead electrocardiogram (ECG) is crucial for the detection of AMI, a systematic algorithm to strengthen ECG interpretation may have important implications for improving diagnosis.

Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome.

AIMS: Congenital long-QT syndromes (cLQTS) or drug-induced long-QT syndromes (diLQTS) can cause torsade de pointes (TdP), a life-threatening ventricular arrhythmia. The current strategy for the identification of drugs at the high risk of TdP relies on measuring the QT interval corrected for heart rate (QTc) on the electrocardiogram (ECG). However, QTc has a low positive predictive value.

Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients.

OBJECTIVES: Identifying high-risk patients is crucial for effective cardiovascular disease (CVD) prevention. It is not known whether electronic health...

Application of Pre-Trained Deep Learning Models for Clinical ECGs.

Automatic electrocardiogram (ECG) analysis has been one of the very early use cases for computer assisted diagnosis (CAD). Most ECG devices provide so...

[Sleep apnea automatic detection method based on convolutional neural network].

Sleep apnea (SA) detection method based on traditional machine learning needs a lot of efforts in feature engineering and classifier design. We constr...

Deep learning and the electrocardiogram: review of the current state-of-the-art.

In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare da...

Histograms of Frequency-Intensity Distribution Deep Learning to Predict the Seizure Liability of Drugs in Electroencephalography.

Detection of seizures as well as that of seizure auras is effective in improving the predictive accuracy of seizure liability of drugs. Whereas electr...

More than meets the eye: Using AI to identify reduced heart function by electrocardiograms.

Electrocardiographic (ECG) assessment of patients with suspected heart disease is a bedrock of cardiology for diagnosing conduction system disease, ar...

Mortality risk stratification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients.

AIMS: An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought t...

Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis.

Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous in the daily practice of medicine largely due to sever...

Semantic Anomaly Detection in Medical Time Series.

The main goal of this project was to define and evaluate a new unsupervised deep learning approach that can differentiate between normal and anomalous...

Development and validation of a machine learning model to predict mortality risk in patients with COVID-19.

New York City quickly became an epicentre of the COVID-19 pandemic. An ability to triage patients was needed due to a sudden and massive increase in p...

Artificial Intelligence Algorithm for Screening Heart Failure with Reduced Ejection Fraction Using Electrocardiography.

Although heart failure with reduced ejection fraction (HFrEF) is a common clinical syndrome and can be modified by the administration of appropriate m...

Brief Report: Can a Composite Heart Rate Variability Biomarker Shed New Insights About Autism Spectrum Disorder in School-Aged Children?

Several studies show altered heart rate variability (HRV) in autism spectrum disorder (ASD), but findings are neither universal nor specific to ASD. W...

A deep learning-based algorithm for detection of cortical arousal during sleep.

STUDY OBJECTIVES: The frequency of cortical arousals is an indicator of sleep quality. Additionally, cortical arousals are used to identify hypopneic ...

Symmetric Projection Attractor Reconstruction analysis of murine electrocardiograms: Retrospective prediction of Scn5a genetic mutation attributable to Brugada syndrome.

BACKGROUND: Life-threatening arrhythmias resulting from genetic mutations are often missed in current electrocardiogram (ECG) analysis. We combined a ...

A Case of Dapsone-induced Mild Methemoglobinemia with Dyspnea and Cyanosis.

Dear Editor, Dapsone is a dual-function drug with antimicrobial and antiprotozoal effects and anti-inflammatory features (1). In dermatology, it is a ...

Concerns for management of STEMI patients in the COVID-19 era: a paradox phenomenon.

The pandemic of coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern. During this time, the management of...

Sleep staging from electrocardiography and respiration with deep learning.

STUDY OBJECTIVES: Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. We hypothesized that it is...

Arrhythmia Classification using Deep Learning and Machine Learning with Features Extracted from Waveform-based Signal Processing.

Arrhythmia is a serious cardiovascular disease, and early diagnosis of arrhythmia is critical. In this study, we present a waveform-based signal proce...