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
Sign In
Advertise with Us
Sekisui Diagnostics UK Ltd.

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

Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Silver Member
Wireless Mobile ECG Recorder
Vital Signs Monitor
Aurus 10

Print article


Surgical Techniques

view channel
Image: Miniaturized electric generators based on hydrogels for use in biomedical devices (Photo courtesy of HKU)

Hydrogel-Based Miniaturized Electric Generators to Power Biomedical Devices

The development of engineered devices that can harvest and convert the mechanical motion of the human body into electricity is essential for powering bioelectronic devices. This mechanoelectrical energy... Read more

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... 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 Quantra Hemostasis System has received US FDA special 510(k) clearance for use with its Quantra QStat Cartridge (Photo courtesy of HemoSonics)

Critical Bleeding Management System to Help Hospitals Further Standardize Viscoelastic Testing

Surgical procedures are often accompanied by significant blood loss and the subsequent high likelihood of the need for allogeneic blood transfusions. These transfusions, while critical, are linked to various... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.