The microchip sensor is slightly larger than a penny. (Credit: StethX and the Georgia Institute of Technology)

Researchers have developed a deep learning (DL) model that they paired with a wearable patch equipped with a highly sensitive sensor that can automatically detect wheezing sounds. The deep learning model has the potential to classify respiratory diseases, which could speed up their diagnosis and treatment.

The patch has a microchip sensor, which unlike traditional microphones in digital stethoscopes, can detect tiny vibrations at a high sensitivity with minimal distortion. By incorporating data from these wheeze variations into a deep learning model, and by taking advantage of the sensor’s ability to eliminate ambient sounds, the detection method led to higher accuracy, sensitivity [it correctly identified the presence of a wheeze], and specificity [it correctly identified absence of a wheeze] compared to the standard time-frequency approach.

The researchers envision two potential uses for the wearable patch framework. One is for short-term screening in a clinic and the other is for long-term home monitoring. The researchers are developing a wireless version of the patch for remote monitoring that could transmit data to a patient’s physician to facilitate treatment.

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Topics:
Medical Patient Monitoring Smart Patches and Bandages Wearables

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