Myocardial Infarction

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.

Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide varie...

AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning.

OBJECTIVE: The objective of this paper is to provide an algorithm for accurate, automated detection of atrial fibrillation (AF) from ECG signals. Four types of ECG signals are considered: normal signals, signals representing symptoms of AF, other signals, and noisy signals. This paper represents follow-up work to the authors' entry in the 2017 PhysioNet Challenge as reported in the 2017 Computing ...

Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types.

Abnormality of the cardiac conduction system can induce arrhythmia - abnormal heart rhythm - that can frequently lead to other cardiac diseases and co...

Correlation of the modified Medical Research Council dyspnea scale with airway structure assessed by three-dimensional CT in patients with chronic obstructive pulmonary disease.

BACKGROUND: Dyspnea is a common symptom in chronic obstructive pulmonary disease (COPD). The modified Medical Research Council (mMRC) dyspnea scale is...

Cardiac Troponin I: Ultrasensitive Detection Using Faradaic Electrochemical Impedance.

An electrochemical biosensor for the detection of cardiac troponin I, cTnI, an important cardiac biomarker, is described. A combination of a novel mon...

Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study.

BACKGROUND: The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the Amer...

A Unique Case of Severe Anemia Secondary to Copper Deficiency in an Adult Patient.

Anemia is a frequently encountered problem in the healthcare system. Common causes of anemia include blood loss, followed by impaired red blood cell p...

Localization of Ventricular Activation Origin from the 12-Lead ECG: A Comparison of Linear Regression with Non-Linear Methods of Machine Learning.

We have previously developed an automated localization method based on multiple linear regression (MLR) model to estimate the activation origin on a g...

A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation.

BACKGROUND: Cardiologs® has developed the first electrocardiogram (ECG) algorithm that uses a deep neural network (DNN) for full 12‑lead ECG analysis,...

Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks.

Excellent image quality is a primary prerequisite for diagnostic non-invasive coronary CT angiography. Artifacts due to cardiac motion may interfere w...

Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things.

Electrocardiography (ECG) sensors play a vital role in the Internet of Medical Things, and these sensors help in monitoring the electrical activity of...

Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection.

OBJECTIVE: Atrial fibrillation is a common type of heart rhythm abnormality caused by a problem with the heart's electrical system. Early detection of...

A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation.

OBJECTIVE: Fragmented QRS (fQRS) is an accessible biomarker and indication of myocardial scarring that can be detected from the electrocardiogram (ECG...

A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms.

OBJECTIVE: Electrocardiography is the most common tool to diagnose cardiovascular diseases. Annotation, segmentation and rhythm classification of ECGs...

ECG Signal Classification Using Various Machine Learning Techniques.

Electrocardiogram (ECG) signal is a process that records the heart rate by using electrodes and detects small electrical changes for each heat rate. I...

ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation.

Electrocardiogram (ECG) is gaining increased attention as a biometric method in a wide range of applications, such as access control and security/priv...

ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network.

OBJECTIVE: The electrocardiogram (ECG) provides an effective, non-invasive approach for clinical diagnosis in patients with cardiac diseases such as a...

Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks.

This paper proposes deep learning methods with signal alignment that facilitate the end-to-end classification of raw electrocardiogram (ECG) signals i...

Analytical Concordance of Diverse Point-of-Care and Central Laboratory Troponin I Assays.

BACKGROUND: Cardiac troponin I (cTnI) 99th percentile cutoffs, used in the diagnosis of acute myocardial infarction, are not standardized across cTnI ...

Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device.

OBJECTIVE: Use of wearable ECG devices for arrhythmia screening is limited due to poor signal quality, small number of leads and short records, leadin...