16-year-old’s blood oxygen level drops on a skiing trip, Apple Watch saves life – Crypto News – Crypto News
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Apple Watch comes with multiple health features like SpO2, ECG and more Apple Watch comes with multiple health features like SpO2, ECG and more

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16-year-old’s blood oxygen level drops on a skiing trip, Apple Watch saves life – Crypto News

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Apple Watch is once again winning hearts for its ‘life-saving’ features. Marcella Lee, a news anchor on San Diego’s CBS 8 news channel has shared her experience about how the device helped save her son’s life during a recent skiing trip.

Lee says that during the trip, her 16-year-old son told her that he was feeling unwell and would not be able to ski. After a while, she noticed that his lips and fingertips were a bit blue, so she put her Apple Watch on his wrist to measure oxygen saturation levels. The watch then showed his blood oxygen levels to be 66%.

The family then looked on the internet where they read that blood saturation levels below 88% require medical attention. They eventually went to a local ER where they found that the blood oxygen saturation level detected by her Apple Watch was almost as accurate as the one detected by the medical device – 67%.

Upon further tests, doctors said that her son’s lungs were filled with fluid and he was suffering from High Altitude Pulmonary Edema (HAPE) which can lead to death. According to CBS 8 report, nearly one in 10,000 skiers in Colorado get affected by HAPE.

Recently, a study concluded that the apple Watch’s ECG sensor data can be used to develop a robust and accurate stress prediction tool. The study was conducted by the researchers from the Canada-based University of Waterloo. ECG or electrocardiogram is a test that records the timing and strength of the electrical signals that make the heart beat. By looking at an ECG, a doctor can gain insights about your heart rhythm and look for irregularities.

The report said that using the Apple Watch Series 6’s ECG sensor, researchers found that there was a close association between ECG data, including heart acceleration and deceleration capacity, and participants’ reported stress levels at the time the readings were taken.

Using this data, machine learning algorithms were developed to create a prediction model. It said that while the stress models are said to have a ‘high level of precision,’ but lower recall.

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