Heart attack symptoms are sometimes similar to non-heart-related conditions, making diagnosis tricky. UK researchers have turned to machine learning to provide doctors with a fast and accurate way of diagnosing heart attacks that has the potential to shorten the time needed to make a diagnosis and provide more efficient and effective treatment to patients.

Currently, the gold-star method for diagnosing a heart attack is to measure levels of the protein troponin in the blood. Troponin is released when the heart muscle is damaged; levels usually increase sharply within three to 12 hours after a heart attack, peaking after about 24 hours. Many hospitals worldwide have adopted diagnostic pathways that include assessing troponin levels when someone is admitted with a suspected heart attack. But they have some limitations: they require the fixed-time collection of blood samples which can be a challenge in the emergency department setting; they only categorize patients as being a low, intermediate or high risk of a heart attack without considering other important information such as when the symptoms began or electrocardiogram (ECG) findings; and, they don’t take into account the influence of sex, age and comorbidities.

Now UK researchers have developed an AI-based machine learning algorithm that is fast and accurate. Named the Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS), the algorithm was designed to calculate the probability of a heart attack for an individual patient. The researchers used data from 10,286 patients who presented with possible heart attacks across six countries worldwide. The machine learning algorithm was “taught” using the patient’s sex, age, ECG findings and medical history, in addition to troponin levels, to identify the probability that a heart attack had occurred. Compared to existing methods, the researchers found that CoDE-ACS could rule out a heart attack in more than double the number of patients, with an accuracy of 99.6%.

The researchers say that their CoDE-ACS algorithm could prevent unnecessary hospital admissions in patients unlikely to have had a heart attack or those at low risk of suffering heart muscle damage or death following a heart attack. They say this would make emergency treatment more efficient and effective, identifying which patients are safe to go home and which need to stay for further tests.

“For patients with acute chest pain due to a heart attack, early diagnosis and treatment saves lives,” said Nicholas Mills, corresponding author of the study. “Unfortunately, many conditions cause these common symptoms, and the diagnosis is not always straightforward. Harnessing data and artificial intelligence to support clinical decisions has enormous potential to improve care for patients and efficiency in our busy Emergency Departments.”

Source: https://newatlas.com/

There’s no need to don uncomfortable smartwatches or chest straps to monitor your heart if your comfy shirt can do a better job. That’s the idea behind “smart clothing” developed by a Rice University lab, which employed its conductive nanotube thread to weave functionality into regular apparel.

The Brown School of Engineering lab of chemical and biomolecular engineer Matteo Pasquali reported in the American Chemical Society journal Nano Letters that it sewed nanotube fibers into athletic wear to monitor the heart rate and take a continual electrocardiogram (EKG) of the wearer. The fibers are just as conductive as metal wires, but washable, comfortable and far less likely to break when a body is in motion, according to the researchers. On the whole, the shirt they enhanced was better at gathering data than a standard chest-strap monitor taking live measurements during experiments. When matched with commercial medical electrode monitors, the carbon nanotube shirt gave slightly better EKGs.

Rice University graduate student Lauren Taylor shows a shirt with carbon nanotube thread that provides constant monitoring of the wearer’s heart

“The shirt has to be snug against the chest,” said Rice graduate student Lauren Taylor, lead author of the study. “In future studies, we will focus on using denser patches of carbon nanotube threads so there’s more surface area to contact the skin.”

Source: https://news.rice.edu/

Researchers have developed a neural network approach that can accurately identify congestive heart failure with 100% accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat, a new study reports.

Congestive heart failure (CHF) is a chronic progressive condition that affects the pumping power of the heart muscles. Associated with high prevalence, significant mortality rates and sustained healthcare costs, clinical practitioners and health systems urgently require efficient detection processes.

Dr Sebastiano Massaro, Associate Professor of Organisational Neuroscience at the University of Surrey, has worked with colleagues Mihaela Porumb and Dr Leandro Pecchia at the University of Warwick and Ernesto Iadanza at the University of Florence, to tackle these important concerns by using Convolutional Neural Networks (CNN) – hierarchical neural networks highly effective in recognising patterns and structures in data.

Published in Biomedical Signal Processing and Control Journal, their research drastically improves existing CHF detection methods typically focused on heart rate variability that, whilst effective, are time-consuming and prone to errors. Conversely, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100% accuracy.

“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. 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,”  explains Dr Massaro.  Dr Pecchia, President at European Alliance for Medical and Biological Engineering, explains the implications of these findings: “With approximately 26 million people worldwide affected by a form of heart failure, our research presents a major advancement on the current methodology. Enabling clinical practitioners to access an accurate CHF detection tool can make a significant societal impact, with patients benefitting from early and more efficient diagnosis and easing pressures on NHS resources.”

Source: https://www.surrey.ac.uk/