SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals
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 th...
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2024-12-01
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| author | Davide Lillini Carlo Aironi Lucia Migliorelli Leonardo Gabrielli Stefano Squartini |
| author_facet | Davide Lillini Carlo Aironi Lucia Migliorelli Leonardo Gabrielli Stefano Squartini |
| author_sort | Davide Lillini |
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| description | 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 <i>apnea</i> events is performed using an unsupervised clustering algorithm, specifically <i>k-means</i>. 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></semantics></math></inline-formula> score of up to 95% for <i>apnea</i> 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. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-f9750a6587a84c42a559bc6c9b939e5b2024-12-13T16:32:45ZengMDPI AGSensors1424-82202024-12-012423778210.3390/s24237782SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone SignalsDavide Lillini0Carlo Aironi1Lucia Migliorelli2Leonardo Gabrielli3Stefano Squartini4Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, ItalyDepartment of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, ItalyDepartment of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, ItalyDepartment of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, ItalyDepartment of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, ItalySleep 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 <i>apnea</i> events is performed using an unsupervised clustering algorithm, specifically <i>k-means</i>. 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></semantics></math></inline-formula> score of up to 95% for <i>apnea</i> 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.https://www.mdpi.com/1424-8220/24/23/7782sleep apnea syndromedeep learningclinical decision support systemsleep apneaOSA detection |
| spellingShingle | Davide Lillini Carlo Aironi Lucia Migliorelli Leonardo Gabrielli Stefano Squartini SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals Sensors sleep apnea syndrome deep learning clinical decision support system sleep apnea OSA detection |
| title | SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals |
| title_full | SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals |
| title_fullStr | SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals |
| title_full_unstemmed | SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals |
| title_short | SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals |
| title_sort | sicrnn a siamese approach for sleep apnea identification via tracheal microphone signals |
| topic | sleep apnea syndrome deep learning clinical decision support system sleep apnea OSA detection |
| url | https://www.mdpi.com/1424-8220/24/23/7782 |
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