We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
ARAB HEALTH - INFORMA

Download Mobile App




AI Detects Congestive Heart Failure from Single Heartbeat

By HospiMedica International staff writers
Posted on 30 Sep 2019
Print article
Image: A new study claims that heart failure can now be detected from a single heartbeat (Photo courtesy University of Surrey).
Image: A new study claims that heart failure can now be detected from a single heartbeat (Photo courtesy University of Surrey).
A new study reports successful identification of severe chronic heart failure (CHF) in 100% of cases using a single heartbeat recording from an electrocardiogram (ECG).

Researchers at the University of Surrey (Guildford, United Kingdom), the University of Warwick (Coventry, United Kingdom), and The Organizational Neuroscience Laboratory (OneLab; London, United Kingdom) developed the novel approach, which is based on artificial intelligence (AI). The technique uses a convolutional neural network (CNN) data tree to detect data patterns and structures at extremely high efficiency, which accurately identifies CHF on the basis of one raw ECG heartbeat.

The CNN allows time series sub-sequences that serve as the input data to be visualized, thus allowing it to discriminate between CHF and healthy subjects, which not only allows faster detection, but also helps to understand how certain tissue behavior is related to the signals recorded. The researchers tested the model on ECG datasets comprising 490,505 heartbeats; it achieved 100% CHF detection accuracy, and also identified class-discriminative heartbeat sequences and specific ECG morphological characteristics. The study was published on September 3, 2019, in Biomedical Signal Processing and Control Journal.

“We trained and tested the CNN model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts. Our model delivered 100% accuracy; by checking just one heartbeat we are able detect whether or not a person has heart failure,” said study author Sebastiano Massaro, PhD, of University of Surrey. “Our model is also one of the first known to be able to identify the ECG' s morphological features specifically associated to the severity of the condition.”

“We envision the adaptation of this system to wearable devices that may be able to perform prediction and detection of CHF using interim ECG recordings, by looking at individual heartbeat morphology. This could allow not only cardiologists but even patients and their caregivers, nurses, trainees, and GPs to take part in the detection process,” concluded Dr. Massaro. “With the completion of the training phase, the network works very rapidly, making it fit for deployment into cloud systems or adaptation to mobile devices.”

CHF is a progressive pathophysiological condition responsible for chronic loss of pumping power in the heart. According to the European Society of Cardiology (ESC), around 26 million people worldwide are affected. Its prevalence increases quickly with age, and mortality rate is closely associated with the degree of severity, reaching peaks of 40% in the most serious events. CHF is also one of the foremost reasons for hospitalization in the elderly, and it is characterized by a resilient relapse rate, with half of the outpatients readmitted within a few months from hospital discharge.

Related Links:
University of Surrey
University of Warwick
The Organizational Neuroscience Laboratory

New
Gold Member
X-Ray QA Meter
T3 AD Pro
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
New
Medical-Grade POC Terminal
POC-821
New
Mobile Barrier
Tilted Mobile Leaded Barrier

Print article

Channels

Surgical Techniques

view channel
Image: Catheters coated with the new material showed a significant reduction in clotting on the device surface (Photo courtesy of UBC Faculty of Medicine)

Newly Developed Coating Makes Medical Devices Clot-Free

Thrombosis, or the formation of blood clots, presents a significant challenge for devices that come into contact with blood. Unlike natural blood vessels, these devices can activate specific proteins in... Read more

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The acoustic pipette uses sound waves to test for biomarkers in blood (Photo courtesy of Patrick Campbell/CU Boulder)

Handheld, Sound-Based Diagnostic System Delivers Bedside Blood Test Results in An Hour

Patients who go to a doctor for a blood test often have to contend with a needle and syringe, followed by a long wait—sometimes hours or even days—for lab results. Scientists have been working hard to... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.