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

In the version of this article originally published, the x axis labels in Fig. 1a were incorrect. The labels originally were 'Specificity,' but should have been '1 - Specificity.

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 variety of diagnostic classes has not been previously reported.

Scoring consensus of multiple ECG annotators by optimal sequence alignment.

Development of ECG delineation algorithms has been an area of intense research in the field of computational cardiology for the past few decades. However, devising evaluation techniques for scoring and/or merging the results of such algorithms, both in the presence or absence of gold standards, still remains as a challenge. This is mainly due to existence of missed or erroneous determination of fiducial points in the results of different annotation algorithms.

Fetal QRS extraction from abdominal recordings via model-based signal processing and intelligent signal merging.

Noninvasive fetal ECG (fECG) monitoring has potential applications in diagnosing congenital heart diseases in a timely manner and assisting clinicians to make more appropriate decisions during labor. However, despite advances in signal processing and machine learning techniques, the analysis of fECG signals has still remained in its preliminary stages. In this work, we describe an algorithm to automatically locate QRS complexes in noninvasive fECG signals obtained from a set of four electrodes placed on the mother's abdomen.

Noninvasive technique for monitoring drug transport through the murine cochlea using micro-computed tomography.

Local delivery of drugs to the inner ear has the potential to treat inner ear disorders including permanent hearing loss or deafness. Current mathematical models describing the pharmacokinetics of drug delivery to the inner ear have been based on large rodent studies with invasive measurements of concentration at few locations within the cochlea. Hence, estimates of clearance and diffusion parameters are based on fitting measured data with limited spatial resolution to a model.