Decoding Sleep: Microphone-Based Snoring Analysis using Embedded Machine Learning for Obstructive Sleep Apnea Detection

Snoring, a recurring habit often disregarded within the Indian community, can signal a grave underlying issue of Obstructive Sleep Apnea (OSA). OSA is a severe sleep disorder characterized by recurrent interruptions in breathing for more than 10 seconds during sleep, typically due to partial or complete airway obstructions. Neglecting OSA can lead to a range of significant health risks, including increased likelihood of occupational accidents, motor vehicle accidents, heightened susceptibility to severe depression, cardiac and cerebrovascular diseases, and reduced life expectancy. The main objective of the study is to detect snoring while at sleep and also to classify it as normal snoring and OSA snoring. Arduino nano 33 BLE sense is used to capture the snore signal, it houses a built-in MP34DT05 sensor. The sensor has a signal-to-noise ratio of 64dB and sensitivity of - 26dBFS ± 3dB. This captures the sound signal of the individual, it is further processed to extract the Mel-filter bank energy features, Mel Frequency Cepstral Coefficients and Spectrogram features. The features are further used to build a model and the same is trained using edge impulse to classify the signal. The dataset is divided into training, testing, and validation sets, with 80% of the data allocated to training, 20% to testing, and an additional 20% within the training data set aside for validation purposes. The results for the two class classification (snoring and non snoring) indicate that the spectrogram-based approach achieved an accuracy rate of 96.9%, while the other two methods yielded accuracy rates of 93.8%. The accuracy for three class classification (normal, snoring and OSA snoring) using the Embedded Machine Learning (EML) approach is 88%. The proposed study demonstrates enhanced accuracy in identifying OSA by snoring compared to previous research. This autonomous system can facilitate the detection of OSA through the analysis of snoring patterns, subsequently alerting the subject to implement pre-emptive measures for remediation. Timely intervention and rectification can enable the subject to attain an undisturbed and restful night’s sleep, thereby augmenting their overall quality of life.