PulsECG – Wearable unobtrusive cuffless blood pressure monitor”, funded by PoC instrument of LINKS foundation and LIFTT srl, for the design of a cuffless AI-blood pressure measurement wearable device.
GOALS: Ensuring health and well-being for all and for all ages
PulsEcg is a device capable of continuously monitoring blood pressure and other vital parameters (electrocardiogram, heart rate and blood oxygen saturation level), useful for monitoring the patient’s health. PulsEcg has the appearance of a smartwatch and, through artificial intelligence algorithms, is able to determine the values of systolic and diastolic pressure. After having detected the trend of the electrocardiogram signals (which describes the patient’s cardiac activity) and plethysmography (which represents the level of oxygen saturation in the blood), by using a neural network it is possible to obtain the pressure values at any time. The information is displayed and recorded through a smartphone application; in this way it is possible to conduct a sophisticated analysis to detect the presence of cardiovascular diseases, such as arteriosclerosis, heart attacks, aneurysms and ischemias. The simplicity of hardware implementation of the device and the ability to apply machine learning algorithms, allows it to be purchased at low cost directly by the end user (the patient). The most interesting aspect of the device is the practicality of use, which makes PulsEcg attractive for every age group: just touch the electrode on the clock face (see figure below) to have all the information regarding the health. The device can be used as a preventive measure, in fact when one of the vital parameters takes on an unusual value, an alarm warns the patient of the danger in order to intervene in a short time. This can help doctors and patients identify situations that need further investigation. PulsEcg represents the first wearable device able to trace the pressure value with a cuff-less approach, avoiding the inconvenient use of the sphygmomanometer (which represents the gold standard in the medical field).
KEYWORDS: Cardiovascular diseases, Medical engineering and technology, Machine learning, statistical data processing and applications using signal processing, Artificial intelligence, intelligent systems, multi agent systems, Signal processing