Apple Watch heart rate, activity data key in Australian murder case

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Activity data recovered from an Apple Watch will be reportedly become key evidence for the prosecution in an Australian murder trial.
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Consumer wearables can detect major heart problem

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the cardiogram app combines consumer wearables with AI to detect health conditions

The dawn of fitness wearables has allowed us to track exercise, count steps and generally look like we know what we’re doing in the gym. But arguably their full potential has yet to be fully harnessed.

A study has just been published showing that everyday consumer wearables, including the Apple Watch, Android Wear, and products from Garmin, are capable of detecting the most common abnormal heart rhythm with 97 percent accuracy.

The study was carried out by mobile health data startup Cardiogram in conjunction with the University of California, San Francisco (UCSF). Titled, ‘Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch’, it’s available to read online at JAMA Cardiology.

Read more: Health IoT: KardiaBand sensor could replace invasive blood tests

Training a deep neural network

Cardiogram and UCSF have developed a deep neural network capable of detecting atrial fibrillation. The condition will affect 25 percent of us at some point in our lives, is responsible for 25 percent of all strokes, and is often left undiagnosed.

“By using software to transform ordinary wearables into personal health monitors, we can literally save lives,” said Cardiogram co-founder Brandon Ballinger.

The software, named DeepHeart, is the result of 9,750 Cardiogram users taking part in UC San Francisco’s Health eHeart Study. Together, they contributed 139 million heart rate and step count measurements, which were used by the DeepHeart neural network.

To validate the software, 51 cardioversion patients at UCSF were tested. DeepHeart was able to distinguish between normal heart rhythm and atrial fibrillation with an accuracy of 97 percent.

Read more: Health IoT: App helps sports stars predict and manage injuries

Consumer wearables for health

Cardiogram claims to have over 750,000 monthly users, with 78 percent of those using its mobile application every day. That engagement rate, which is higher than that of the most popular social media networks, has coincided with the company’s recent research on major health conditions: hypertension, sleep apnea, diabetes, and, this week, atrial fibrillation.

Read more: Fitbit and Apple Watch can help predict diabetes, says report

“Over the last year, we’ve presented research on four major health conditions. The link between these health conditions and heart rate comes from the autonomic nervous system,” explained Ballinger.

“As you develop hypertension, for example, your pattern of beat-to-beat heart rate variability shifts – so your heart is not just an important organ in its own right, but also a vantage point into the rest of your health.”

The Cardiogram application is compatible with the Apple Watch, and any Android Wear watch with a heart rate sensor, including models from Huawei, LG, New Balance, and Montblanc.

Internet of Business says

Personal healthtech devices and other wearables that can be optimised to monitor health and fitness have been one of the big stories this year, alongside smart/connected car partnerships (good news) IoT security (bad news), and AI ethics (calls for action).

The revelation that wearables’ key application isn’t to offer us a window into our social world, as many people had thought, but a window into ourselves and how our own bodies are performing, has been transformative, and the levels of user engagement prove this.

The IoT is saving lives with data, and encouraging us to look after ourselves better: as positive an application of networked computing and analytics as you could wish for.

The post Consumer wearables can detect major heart problem appeared first on Internet of Business.

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Apple Watch more effective at detecting heart condition than KardiaBand accessory, study finds

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A continuing study into the medical potential of consumer wearables has confirmed devices like Apple Watch are sensitive enough to detect abnormal heart rhythms with a 97 percent accuracy, a performance that beats out add-on ECG accessory KardiaBand.
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Apple Watch can detect serious heart problem

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Apple Watch can accurately detect atrial fibrillation, a serious heart condition that is a leading cause of stroke. This advanced feature remains in testing. However, a new medical study offers proof that wearables can do far more than simply track fitness. In fact, they could actually keep the wearer alive. The Health eHeart Study, coordinated […]

(via Cult of Mac – Tech and culture through an Apple lens)

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Color’s new DNA test kit can identify inherited heart conditions

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Genetic testing company Color is mostly known for its DNA tests for cancer, but its most recent test kit is all about the heart. Color's new kit can detect predispositions to various inherited heart conditions, such as abnormal heart rhythms and any…
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‘One Finger Death Punch 2’ will pummel its way into your heart

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At the GDC 2018 Indie Megabooth on Monday, Silver Dollar Games showed off One Finger Death Punch 2, the sequel to its popular 2013 brawler. Fans of the original — and fans of smash-em-ups in general — are not going to be disappointed.
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The HeartMonitor app tracks your heart rate continuously without starting an active workout

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Due to power concerns, Apple Watch does not monitor your heart rates continually…. Read the rest of this post here


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Susceptible to Heart Disease? Gene Editing Could Change That

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One in Four

According to the Centers for Disease Control and Prevention (CDC), heart disease causes one in every four deaths in the United States. In fact, it’s the nation’s leading cause of death, and every year, more than 700,000 people in the U.S. have a heart attack.

A person’s lifetime risk of developing heart disease depends on many factors. Several are related to lifestyle, so if a person is at a high-risk of developing heart disease, their doctor might recommend they get more exercise, improve their eating habits, or quit smoking. Other risk factors aren’t so easily altered – namely, a person’s genes – but as a new study has shown, that might change in the age of gene editing.

Some people have a naturally occurring mutation in a gene called ANGPTL3, which plays a role in the regulation of fats in our blood called triglycerides. Having too many triglycerides increases our risk of developing heart disease, so doctors usually recommend patients change their diets or take medications to lower these levels.

However, the ANGPTL3 mutation seems to lower the person’s risk of developing cardiovascular disease without causing any harmful side effects. Now, researchers from the University of Pennsylvania have tested a gene-editing technique inspired by these people who lucked out in the genetic lottery.

A First Step

For their study, which was published in the journal Circulation, the team used a CRISPR-like technique called base editing. First, they injected healthy mice with the base-editing treatment to modify the ANGPTL3 gene. Then, they compared the animals’ blood fat levels with those of untreated mice. The levels of the treated mice were up to 30 percent lower than those of the mice that hadn’t been treated.

Next, the researchers set out to determine if their treatment could help patients with a rare inherited disorder called homozygous familial hypercholesterolemia. These patients have very few effective options for treatments and carry a severe risk of heart disease as a result. If doctors could forcibly lower their triglyceride levels by “turning off” a gene, it could be life-saving.

How CRISPR Works: The Future of Genetic Engineering and Designer Humans
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To test this possibility, the team created a mouse model of homozygous familial hypercholesterolemia. After two weeks, the mice given the base-editing treatment showed substantially reduced triglycerides – up to 56 percent – over the mice that weren’t treated. The modified gene appeared to help treat the rare condition.

For the time being, researchers have only tested the treatment in mice, but these initial results are encouraging. If researchers can find a way to make it work in humans, the technique could potentially help patients whose triglyceride levels aren’t responding to lifestyle changes and medication, as well as those with homozygous familial hypercholesterolemia.

The team is now preparing for the next step toward human trials, which will involve injecting human liver cells into mice in order to test the treatment’s effectiveness and safety on human ANGPTL3 genes. If all goes well, the team could move ahead with clinical trials. According to a press release statement by the study’s lead author, Kiran Musunuru, patients with homozygous familial hypercholesterolemia could be just five years away from “a one-time CRISPR ‘vaccination’.”

The post Susceptible to Heart Disease? Gene Editing Could Change That appeared first on Futurism.

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Google’s new AI algorithm can predict heart disease through retina scan

Google has announced its new AI algorithm that can detect heart diseases by retina scanning. Scientists from Google and its subsidiary health tech Verily claim that by deep scanning of a patient’s eye, the newly developed AI software is capable of accurately detecting data including age, blood pressure, and whether or not they smoke and more.  Furthermore, this can also detect the risk of a person suffering a major cardiac event with roughly more or less the same accuracy as current leading methods. the algorithm is said to make it simple for doctors to analyze a patient’s cardiovascular risk since it doesn’t require a blood test. However, even before this software is implemented, it needs to be tested for accuracy and perfected. Google says that using the deep learning algorithms trained on data from over 284,335 patients, it was able to predict CV risk factors from retinal images which also included eye scans as well as general medical data.The company claims that the algorithm was able to distinguish the retinal images of a smoker from that of a non-smoker 71% of the time. Neural networks were used to mine this information for patterns, learning to associate telltale signs in the eye scans with the metrics …
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