Arrhythmias Detection: Speeding Diagnosis and Treatment – New deep learning algorithm can diagnose 14 types of heart rhythm defects by sifting through hours of ECG data generated by some REMOTELY iRhythm’s wearable monitors | Leaders in Pharmaceutical Business Intelligence (LPBI) Group

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Arrhythmias Detection: Speeding Diagnosis and Treatment – New deep learning algorithm can diagnose 14 types of heart rhythm defects by sifting through hours of ECG data generated by some REMOTELY iRhythm’s wearable monitors

July 10, 2017 by 2012pharmaceutical

Arrhythmias Detection: Speeding Diagnosis and Treatment – New deep learning algorithm can diagnose 14 types of heart rhythm defects by sifting through hours of ECG data generated by some REMOTELY iRhythm’s wearable monitors

Reporter: Aviva Lev-Ari, PhD, RN

UPDATED on 9/27/2022

Paul Ching• 2nd 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐄𝐱𝐩𝐞𝐫𝐭

#ECG_Sensor_Patch

ECG sensor patch is a diagnostic tool used by the clinicians for early detection of atrial fibrillation and to ensure timely treatment for such patients. It also acts as triggering alarm for the #cardiac patient about the stress levels and thus increasing the patient compliance. With advances in device miniaturization and wireless technologies and changing consumer expectations, wearable “on-body” ECG patch devices have evolved to meet contemporary needs. The wearable patch continuously record the ECG of user, which aids in arrhythmia detection and management at the point of care. It also acts as triggering alarm for the cardiac patient about the stress levels and thus increasing the patient compliance.

𝐆𝐞𝐭 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐏𝐃𝐅, 𝐂𝐥𝐢𝐜𝐤 𝐇𝐞𝐫𝐞: https://lnkd.in/dbJYWhyH

#ecg #sensor #patch #ecgsensorpatch #atrialfibrillation #heart #heartbeat #technology #medicaldevice #wearable #wearabletechnology #healthcare

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Long term, the group hopes this algorithm could be a step toward expert-level arrhythmia diagnosis for people who don’t have access to a cardiologist, as in many parts of the developing world and in other rural areas. More immediately, the algorithm could be part of a wearable device that at-risk people keep on at all times that would alert emergency services to potentially deadly heartbeat irregularities as they’re happening.

said Pranav Rajpurkar, a graduate student and co-lead author of the paper. “For example, two forms of the arrhythmia known as second-degree atrioventricular block look very similar, but one requires no treatment while the other requires immediate attention.”

To test accuracy of the algorithm, the researchers gave a group of three expert cardiologists 300 undiagnosed clips and asked them to reach a consensus about any arrhythmias present in the recordings. Working with these annotated clips, the algorithm could then predict how those cardiologists would label every second of other ECGs with which it was presented, in essence, giving a diagnosis.

http://news.stanford.edu/2017/07/06/algorithm-diagnoses-heart-arrhythmias-cardiologist-level-accuracy/

iRhythm, maker of portable ECG devices

Image Source:

https://www-technologyreview-com.cdn.ampproject.org/c/s/www.technologyreview.com/s/608234/the-machines-are-getting-ready-to-play-doctor/amp/

Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

Pranav Rajpurkar, Awni Y. Hannun, Masoumeh Haghpanahi, Codie Bourn, Andrew Y. Ng

(Submitted on 6 Jul 2017)

We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. We build a dataset with more than 500 times the number of unique patients than previously studied corpora. On this dataset, we train a 34-layer convolutional neural network which maps a sequence of ECG samples to a sequence of rhythm classes. Committees of board-certified cardiologists annotate a gold standard test set on which we compare the performance of our model to that of 6 other individual cardiologists. We exceed the average cardiologist performance in both recall (sensitivity) and precision (positive predictive value).

Subjects:	Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:1707.01836 [cs.CV]
	(or arXiv:1707.01836v1 [cs.CV] for this version)

Submission history

From: Awni Hannun [view email]
[v1] Thu, 6 Jul 2017 15:42:46 GMT (852kb,D)

SOURCE

https://arxiv.org/abs/1707.01836

Active Learning Applied to Patient-Adaptive Heartbeat Classification

Part of: Advances in Neural Information Processing Systems 23 (NIPS 2010)

[PDF] [BibTeX] [Supplemental]

Authors

Abstract

While clinicians can accurately identify different types of heartbeats in electrocardiograms (ECGs) from different patients, researchers have had limited success in applying supervised machine learning to the same task. The problem is made challenging by the variety of tasks, inter- and intra-patient differences, an often severe class imbalance, and the high cost of getting cardiologists to label data for individual patients. We address these difficulties using active learning to perform patient-adaptive and task-adaptive heartbeat classification. When tested on a benchmark database of cardiologist annotated ECG recordings, our method had considerably better performance than other recently proposed methods on the two primary classification tasks recommended by the Association for the Advancement of Medical Instrumentation. Additionally, our method required over 90% less patient-specific training data than the methods to which we compared it.

SOURCE

http://papers.nips.cc/paper/4091-active-learning-applied-to-patient-adaptive-heartbeat-classification

Cardiologist-Level Arrhythmia Detection With Convolutional Neural Networks

Pranav Rajpurkar*, Awni Hannun*, Masoumeh Haghpanahi, Codie Bourn, and Andrew Ng

A collaboration between Stanford University and iRhythm Technologies

https://stanfordmlgroup.github.io/projects/ecg/

JULY 6, 2017

Stanford computer scientists develop an algorithm that diagnoses heart arrhythmias with cardiologist-level accuracy

A new deep learning algorithm can diagnose 14 types of heart rhythm defects, called arrhythmias, better than cardiologists. This could speed diagnosis and improve treatment for people in rural locations.

BY TAYLOR KUBOTA

SOURCES

http://news.stanford.edu/2017/07/06/algorithm-diagnoses-heart-arrhythmias-cardiologist-level-accuracy/