A Spiking Neural Network-based Olfactory Bionic Model for Periodontal Diseases Screening by Exhaled Breath with Electronic Nose

2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) Pub Date : 2022-05-29 DOI:10.1109/ISOEN54820.2022.9789617

Yingying Xue, Yizhou Xiong, Weijie Yu, Shimeng Mou, H. Wan, Ping Wang

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Abstract

Periodontal diseases seriously affect people's physical and mental health. Electronic noses combined with intelligent recognition technology have been widely used in the detection of exhaled breath in the diagnosis of diseases, while rarely focused on the detection of periodontal diseases. In this study, an electronic nose system based on electrochemical sensors was developed and a bionic olfactory model was constructed using a spiking neural network for the detection of periodontal diseases. Results show that the electronic nose system can accurately identify biomarkers related to periodontal disease (hydrogen sulfide, methanethiol). A total of 27 breath samples were analyzed to verify the performance of the model. The accuracy, sensitivity, and specificity of the model were 88.9%, 81.3%, and 100%, respectively. In conclusion, the developed electronic nose system combined with the olfactory bionic model shows a high performance in identifying patients with periodontal diseases, which have a great potential to be used both in clinical and point-of-care testing.

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基于脉冲神经网络的电子鼻呼出牙周病嗅觉仿生模型

牙周病严重影响人们的身心健康。电子鼻结合智能识别技术已广泛应用于呼出气检测中的疾病诊断，而很少关注于牙周病的检测。本研究开发了一种基于电化学传感器的电子鼻系统，并利用脉冲神经网络建立了用于牙周病检测的仿生嗅觉模型。结果表明，电子鼻系统可以准确识别与牙周病相关的生物标志物(硫化氢、甲硫醇)。总共分析了27个呼吸样本来验证模型的性能。该模型的准确率为88.9%，灵敏度为81.3%，特异性为100%。综上所述，所开发的电子鼻系统结合嗅觉仿生模型对牙周病患者的识别具有良好的性能，在临床和护理点检测中都有很大的应用潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)

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