Accurately detecting heartattacks and strokes with Apple Watch and fitbits
Heartbeat measurement app Cardiogram and the University of California, San Francisco used the Apple Watch for 97 percent accurate detection the most common abnormal heart rhythm when paired with an AI-based algorithm.
The study involved 6,158 participants recruited through the Cardiogram app on Apple Watch. Most of the participants in the UCSF Health eHeart study had normal EKG readings. However, 200 of them had been diagnosed with paroxysmal atrial fibrillation (an abnormal heartbeat). Engineers trained a deep neural network to identify these abnormal heart rhythms from Apple Watch heart rate data.
About a quarter of strokes are caused by an abnormal heart rhythm, according to Cardiogram co-founder and data scientist for UCSF's eHeart study Brandon Ballinger.
Two-thirds of those types of strokes are preventable with relatively inexpensive drugs.
The Mobile EKG sticks onto the back of a smartphone and uses the Kardia app to determine abnormal heart rhythm, and determined it was as good as other EKG devices used in the doctor's office. The Mayo Clinic felt invested in AliveCor's latest $30 million round.
Together, the Mountain View, CA-based company and Mayo will apply AliveCor's machine learning technology to 10 million ECG recordings taken by users of the company's Kardia product, an FDA-cleared mobile device that pairs with smartphones to measure electrical activity in the heart. The goal is to "uncover hidden physiological signals to improve heart and overall human health," according to a press release.
In particular, the findings could lead to insights about the relationship between cardiac arrhythmias and changes in blood potassium levels, which can indicate kidney failure.
"Working with Mayo Clinic, we are hopeful that soon physicians will be turning to ECG data for the care of many types of patients, not just those with typical cardiovascular issues," Dave Albert, MD, AliveCor's chief medical officer, said in the press release.
