EarSonar: An Acoustic Signal-Based Middle-Ear Effusion Detection Using Earphones

Abstract

Middle ear effusion is a common symptom of otitis media, the reactive physical manifestation of otitis media (OM) in children's middle ear. However, diagnosing MEE for little children at home is troublesome due to their difficulty cooperating and the caregiver's lack of medical knowledge. To this end, we propose EarSonar, a novel acoustic-based MEE diagnostic system. The principle behind EarSonar is that the acoustic absorption effect exists in ear scenarios, and the volume of middle ear fluid can markedly affect the absorbed spectrum energy. By automatically eliminating the impact of potential interference factors and identifying the representative frequency range with the typical reaction of acoustic absorption, EarSonar captures fine-grained signal features on absorbed spectrum energy and models the intrinsic relationship between acoustic absorption and the volume of the filler fluid in the eardrum. On that basis, EarSonar extracts the features of the MEE signal segment and uses k-means clustering to classify middle ear effusion status. We conducted a test on 112 adolescents aged 4-6. We divided the degree of middle ear effusion into three grades. The final average detection accuracy rate exceeds 92%, which is 8 % higher than the previous method. We have implemented a proof-of-concept prototype of EarSonar by building upon earphones embedded with a microphone and speaker. Experimental results demonstrate a feasible and effective way to turn earphones into potential home-use MEE screening tools.