Research To Reduce The Risk Of Heart Attacks And Strokes In Hazardous Occupations
A rise in blood pressure that often goes unnoticed dramatically increases the risk of heart problems. Doctors need regular monitoring data to make a timely diagnosis of blood pressure disorders, such as high or low blood pressure. learn more in blood pressure not only pose a threat to the health of a specific individual but can also severely affect other people if, for example, a pilot, a bus driver or a train operator suddenly loses consciousness or has a heart attack or a stroke. A classical cuff blood pressure monitor (BPM) is simple but not mobile enough. A taxi driver or a crane operator could not use it for regular blood pressure checks. Another indicator, heart rate, can be measured fairly accurately using a small, wearable heart rate monitor. Its light sensor receives and reflects the light directed onto the wrist by the light emitting diode. Part of the light penetrates the skin and is absorbed by the blood in the artery, while the rest is reflected back to the light sensor. This method, known as photoplethysmography (PPG), uses the dependence between the blood volume in the artery and the current pulse phase, as the reflected light pulsates in sync with the heart. Suggestions to use PPG for blood pressure measurements have been made for a long time; however, they require better accuracy for medical applications. Recent advances, such as compact sensors and higher digital signal processing speeds, have resulted in a wearable BPM technology, as evidenced by the abundance of commercial compact BPMs and smart watches with pressure monitoring capability. In its recent study, the NTI CoE team led by Skoltech Professor Dmitry Dylov determined how to make the best use of PPG data with the aid of AI in order to reduce the measurement error in wearable BPMs. According to Dylov, instead of measuring the heart rate, the wrist device captures the entire pulse wave, including all bends in its shape, time delays, and even abstract features indiscernible by the human eye but detectable by AI neural networks. The team trained the neural networks using their lab sensor readings and extensive, publicly available data. The team performed an extensive analytical study of the existing PPG data analysis approaches, which helped the researchers to understand their limitations and find a niche for new inventions, and eventually created a set of new algorithms. He notes that there are papers published these days where functional pulse of the blood under human skin is captured by analyzing simple video footage.