Implantable Cardiac Monitor (ICM) devices are demonstrating as of today, the fastest-growing market for implantable cardiac devices. As such, they are becoming increasingly common in patients for measuring heart electrical activity. ICMs constantly monitor and record a patient's heart rhythm and when triggered - send it to a secure server where health care professionals (denote HCPs from here on) can review it. These devices employ a relatively simplistic rule-based algorithm (due to energy consumption constraints) to alert for abnormal heart rhythms. This algorithm is usually parameterized to an over-sensitive mode in order to not miss a case (resulting in relatively high false-positive rate) and this, combined with the device's nature of constantly monitoring the heart rhythm and its growing popularity, results in HCPs having to analyze and diagnose an increasingly growing amount of data. In order to reduce the load on the latter, automated methods for ECG analysis are nowadays becoming a great tool to assist HCPs in their analysis. While state-of-the-art algorithms are data-driven rather than rule-based, training data for ICMs often consist of specific characteristics which make its analysis unique and particularly challenging. This study presents the challenges and solutions in automatically analyzing ICM data and introduces a method for its classification that outperforms existing methods on such data. As such, it could be used in numerous ways such as aiding HCPs in the analysis of ECGs originating from ICMs by e.g. suggesting a rhythm type.

For decades, heart specialists have been implanting insertable cardiac monitors (ICMs) to track sporadic heart arrhythmia. These subcutaneous devices have become the preferred diagnostic tool for prolonged heart rhythm monitoring since they were first developed in 1990. But ICM false alerts have been a problem ever since. Now, however, physicians are using artificial intelligence to reduce the incidence of ICM false positives. Last summer, Medtronic, a global leader in healthcare technology, introduced new AI algorithms to reduce false alerts from irregular or rapid heart rhythms and long pauses between heartbeats.

Efforts to automate the analysis of electrocardiograms (ECGs) date back to the 1950s when researchers first converted ECG signals from analog to digital form, enabling the subsequent creation of algorithms that could be used in computer-interpreted ECG (CIE).1,2 With continued technological advances, the use of CIE has become so common that more than 100 million ECGS are interpreted by computer each year in the United States.2 However, conventional CIE models require over-reading by a physician, and despite these checks, certain ECG features may be missed. A growing body of research highlights the potential value of deep learning-based CIE, which could detect features that may be overlooked or undetectable by a physician reader.3,4 "Deep learning is a subfield of machine learning which tends to solve a problem end to end to eliminate the need for domain expertise and to fully explore ECG features from raw ECG data," according to an article co-authored by Shijie Zhou, PhD, assistant research scientist in the department of biomedical engineering and the Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE) Institute at Johns Hopkins University in Baltimore.5 "Deep-learning models use neural networks to capture only the most important features from the input data and disregard redundant input features by means of network pruningto maintain model accuracy."

Picture: military interrogators are talking to a local man they suspect of helping to emplace roadside bombs. The man denies it, even as they show him photos of his purported accomplices. But an antenna in the interrogation room is detecting the man's heartbeat as he looks at the pictures. A UK research team is using radio waves to pick up subtle changes in heart rhythm and then, using an advanced AI called a neural network, understand what those signals mean -- in other words, what the subject is feeling. It's a breakthrough that one day might help, say, human-intelligence analysts in Afghanistan figure out who represents an insider threat.

The coronavirus pandemic has led to enhanced health-care collaboration, innovation, and increased use of digital technologies. Telehealth enables doctors to safely connect with patients virtually and monitor them remotely, whether in different cities or down the hall. And smarter and smaller medical devices are producing better outcomes for patients--a disruption is sensed, like low blood sugar or a too-rapidly beating heart, and a therapy is applied, in real time. This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review's editorial staff. All of this is aided by improved processing capabilities and data--lots of data, and that means artificial intelligence. The guest in this episode of Business Lab is Laura Mauri, vice president of global clinical research and analytics at Medtronic. And she knows all about how data can help drive better patient outcomes, improve the patient experience, and provide valuable information for doctors and medical device creators. Dr. Mauri is an interventional cardiologist and one of the world's leading experts on clinical trials, but, as she says, the success of a clinical trial really does come down to the patient experience, and how it's improved. Mauri also has great hope for health care and technology. And although she cautions that this work is not simple, you can literally see progress happening--which is the outcome we all want. Business Lab is hosted by Laurel Ruma, director of Insights, the custom publishing division of MIT Technology Review.

UC Davis Health researchers have developed a computer model to screen drugs for unintended cardiac side effects, especially arrhythmia risk. Colleen E. Clancy with Pei-Chi Yang and Kevin DeMarco of her research team (from left to right). Published in Circulation Research, the study was led by Colleen E. Clancy, professor of physiology and membrane biology, and Igor Vorobyov, assistant professor of physiology and membrane biology. Clancy is a recognized leader in using high-performance computing to understand electrical changes in the heart. "One main reason for a drug being removed from the market is potentially life-threatening arrhythmias," Clancy said.

Over the last couple of months, we've explored how AI is or will be applied in a wide range of medical markets, from ophthalmology to dentistry to Wound Care and some have started to realise the potential of AI whilst others are still just working it out. In cardiology however, there is a problem which AI can, and will, help with immediately – Atrial Fibrillation (AF). AF is, essentially, an irregular heartbeat which occurs in sporadic'episodes' and is estimated to affect tens of millions of people around the world. On their own these episodes may not cause immediate damage, but they can be indicative of a more serious problem; the condition is one of the leading causes of strokes and comes with an increased risk of heart failure and dementia. The problem with these episodes is that a patient doesn't have access to their cardiologist during the event.

Welcome to TechTalks' AI book reviews, a series of posts that explore the latest literature on AI. This post is the first part of a two-part interview with Dr. Eric Topol about the impact of artificial intelligence on health care and medicine. In the last part of our interview with Dr. Eric Topol, we discussed how artificial intelligence algorithms can return the gift of time to doctors and help them have more human interactions with their patients. This is a subject that Dr. Topol discusses early on in his latest book "Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again." Another topic Dr. Topol discussed about the role of artificial intelligence in healthcare was giving every person more insight and control on their own health. This is one of the areas where deep learning algorithms have made great inroads.

Posted on January 15th, 2019 by Dr. Francis Collins Thanks to advances in wearable health technologies, it's now possible for people to monitor their heart rhythms at home for days, weeks, or even months via wireless electrocardiogram (EKG) patches. In fact, my Apple Watch makes it possible to record a real-time EKG whenever I want. For true medical benefit, however, the challenge lies in analyzing the vast amounts of data--often hundreds of hours worth per person--to distinguish reliably between harmless rhythm irregularities and potentially life-threatening problems. Now, NIH-funded researchers have found that artificial intelligence (AI) can help. A powerful computer "studied" more than 90,000 EKG recordings, from which it "learned" to recognize patterns, form rules, and apply them accurately to future EKG readings.

Investigators from the Mayo Clinic and AliveCor demonstrated that a trained artificial intelligence network can help identify people at increased risk of arrhythmias and sudden cardiac death despite displaying a normal heart rhythm on their electrocardiogram. Up to half of patients with long QT syndrome can show a normal interval on a standard test, the personal EKG manufacturer AliveCor said in a statement. Correct diagnoses and treatment can be crucial, especially when using drugs that may prolong heartbeats. The researchers' deep neural network generated the results using data from a single lead of a 12-lead EKG--measuring the voltage between the left and right arms--suggesting that simpler, portable devices may be used to detect the concealed heart condition, the company said. The network had an overall accuracy of 79%, with 73% sensitivity and 81% specificity.