SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals

Abstract

Sleep apnea syndrome (SAS) affects about 3-7% of the global population, but is often undiagnosed. It involves pauses in breathing during sleep, for at least 10 s, due to partial or total airway blockage. The current gold standard for diagnosing SAS is polysomnography (PSG), an intrusive procedure that depends on subjective assessment by expert clinicians. To address the limitations of PSG, we propose a decision support system, which uses a tracheal microphone for data collection and a deep learning (DL) approach-namely SiCRNN-to detect apnea events during overnight sleep recordings. Our proposed SiCRNN processes Mel spectrograms using a Siamese approach, integrating a convolutional neural network (CNN) backbone and a bidirectional gated recurrent unit (GRU). The final detection of apnea events is performed using an unsupervised clustering algorithm, specifically k-means. Multiple experimental runs were carried out to determine the optimal network configuration and the most suitable type and frequency range for the input data. Tests with data from eight patients showed that our method can achieve a Recall score of up to 95% for apnea events. We also compared the proposed approach to a fully convolutional baseline, recently introduced in the literature, highlighting the effectiveness of the Siamese training paradigm in improving the identification of SAS.

Keywords: OSA detection; clinical decision support system; deep learning; sleep apnea; sleep apnea syndrome.

Figure 1

The scatter plots illustrate the output of the principal component (PCA) applied to the output of the final GRU layer in the SiCRNN model. The resulting embeddings are derived from two patients under two conditions: (a) noise-free patient embeddings and (b) noisy patient embeddings. The observed distances between the apnea and non-apnea clusters are $2.0$ in the noise-free scenario and $0.87$ in the presence of noise, respectively.

Figure 2

Overview of the proposed SiCRNN framework. The purple dashed line highlights the Siamese configuration employed during the training phase, whereas the green dashed line corresponds to the inference phase, which is carried out through the k-means clustering algorithm.

Figure 3

The scatter density plot shows the results of the hyperparameter tuning by relating the $Precision$, $Recall$, and F1 score metrics to the number of GRU hidden layers used during training. On the x-axis, $Precision$ values are reported, while the y-axis represents $Recall$ values, and the size of the points indicates the F1 score. The different shades of orange represent the number of convolutional blocks used in the model’s training.

Figure 4

The scatter density plot shows the results of the hyperparameter tuning by relating the $Precision$, $Recall$, and F1 score metrics to the dimension of the kernel size used during training. On the x-axis, $Precision$ values are reported, while the y-axis represents $Recall$ values, and the size of the points indicates the F1 score. The different shades of gray represent the kernel size used in the model’s training.

Figure 5

The scatter density plot shows the results of the hyperparameter tuning by relating the $Precision$, $Recall$, and F1 score metrics to the number of MEL bands selected for each input sample frequency during training. On the x-axis, $Precision$ values are reported, while the y-axis represents $Recall$ values, and the size of the points indicates the F1 score. The different shades of blue represent the number of MEL bands used in the model’s training.

Figure 6

The scatter density plot shows the results of the hyperparameter tuning by relating the $Precision$, $Recall$, and F1 score metrics to the number of convolutional blocks used during training. On the x-axis, $Precision$ values are reported, while the y-axis represents $Recall$ values, and the size of the points indicates the F1 score. The different shades of green represent the number of convolutional blocks used in the model’s training.

Figure 7

(a) The region located below the top of the red mask indicates the apnea events; (b,c) spectrograms with the labeled red mask display an apnea event with significant spectral content. The time associated with each individual bin in the spectrograms is $11.56$ ms.