Non-Invasive, Multi-Modal Sensing Techniques for Detecting Infiltration During Intravenous Therapy
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Intravenous infiltration is a condition wherein an infused solution leaks inadvertently into soft tissue surrounding a hypodermic needle site. This occurrence affects over 10% of patients in hospitals worldwide, and can lead to severe tissue damage if not treated immediately. However, the methods currently used by medical staff to detect an infiltration are subjective and prone to error. Infiltration becomes an even larger concern in the neonatal intensive care unit, where neonates have a much higher probability of infiltration due to having smaller veins. Unfortunately, infiltrations mostly go unnoticed for an extended period of time due to the neonate's inability to communicate with medical staff. For these reasons, automatic IV infiltration detection could greatly reduce the risk associated with this damaging condition. This dissertation proposes a novel design that uses non-invasive sensing in conjunction with a low-power embedded computing platform to deliver continuous infiltration monitoring around the IV catheter site. This kind of system could be able to detect an infiltration by non-invasively monitoring for known symptoms: swelling of soft tissue, skin cooling, and increased skin firmness; these symptoms can be sensed by measuring skin stretch, temperature, and local bioimpedance. In addition, the system's low-power design and wireless capabilities make it ideal for continuous wear. The proposed automatic IV infiltration detection system could significantly improve the number of infiltrations identified and treated on time.