NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning
<italic>Goal:</italic> Auscultation for neonates is a simple and non-invasive method of diagnosing cardiovascular and respiratory disease. However, obtaining high-quality chest sounds containing only heart or lung sounds is non-trivial. Hence, this study introduces a new deep-learning mo...
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/10531026/ |
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| author | Yang Yi Poh Ethan Grooby Kenneth Tan Lindsay Zhou Arrabella King Ashwin Ramanathan Atul Malhotra Mehrtash Harandi Faezeh Marzbanrad |
| author_facet | Yang Yi Poh Ethan Grooby Kenneth Tan Lindsay Zhou Arrabella King Ashwin Ramanathan Atul Malhotra Mehrtash Harandi Faezeh Marzbanrad |
| author_sort | Yang Yi Poh |
| collection | DOAJ |
| description | <italic>Goal:</italic> Auscultation for neonates is a simple and non-invasive method of diagnosing cardiovascular and respiratory disease. However, obtaining high-quality chest sounds containing only heart or lung sounds is non-trivial. Hence, this study introduces a new deep-learning model named NeoSSNet and evaluates its performance in neonatal chest sound separation with previous methods. <italic>Methods:</italic> We propose a masked-based architecture similar to Conv-TasNet. The encoder and decoder consist of 1D convolution and 1D transposed convolution, while the mask generator consists of a convolution and transformer architecture. The input chest sounds were first encoded as a sequence of tokens using 1D convolution. The tokens were then passed to the mask generator to generate two masks, one for heart sounds and one for lung sounds. Each mask is then applied to the input token sequence. Lastly, the tokens are converted back to waveforms using 1D transposed convolution. <italic>Results:</italic> Our proposed model showed superior results compared to the previous methods based on objective distortion measures, ranging from a 2.01 dB improvement to a 5.06 dB improvement. The proposed model is also significantly faster than the previous methods, with at least a 17-time improvement. <italic>Conclusions:</italic> The proposed model could be a suitable preprocessing step for any health monitoring system where only the heart sound or lung sound is desired. |
| format | Article |
| id | doaj-art-68eb0d8fbb184e2383757b7c1580dc27 |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-68eb0d8fbb184e2383757b7c1580dc272025-08-20T03:30:52ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762024-01-01534535210.1109/OJEMB.2024.340157110531026NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep LearningYang Yi Poh0https://orcid.org/0009-0001-0869-7047Ethan Grooby1https://orcid.org/0000-0002-1563-4976Kenneth Tan2https://orcid.org/0000-0002-1931-0549Lindsay Zhou3https://orcid.org/0000-0002-3013-5040Arrabella King4Ashwin Ramanathan5https://orcid.org/0000-0002-9298-1234Atul Malhotra6https://orcid.org/0000-0001-9664-4182Mehrtash Harandi7https://orcid.org/0000-0002-6937-6300Faezeh Marzbanrad8https://orcid.org/0000-0003-0551-1611Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Clayton, VIC, AustraliaDepartment of Electrical and Computer Systems Engineering, Monash University, Melbourne, Clayton, VIC, AustraliaMonash Newborn, Monash Children's Hospital and Department of Paediatrics, Monash University, Melbourne, Clayton, VIC, AustraliaMonash Newborn, Monash Children's Hospital and Department of Paediatrics, Monash University, Melbourne, Clayton, VIC, AustraliaMonash Newborn, Monash Children's Hospital and Department of Paediatrics, Monash University, Melbourne, Clayton, VIC, AustraliaMonash Newborn, Monash Children's Hospital and Department of Paediatrics, Monash University, Melbourne, Clayton, VIC, AustraliaMonash Newborn, Monash Children's Hospital and Department of Paediatrics, Monash University, Melbourne, Clayton, VIC, AustraliaDepartment of Electrical and Computer Systems Engineering, Monash University, Melbourne, Clayton, VIC, AustraliaDepartment of Electrical and Computer Systems Engineering, Monash University, Melbourne, Clayton, VIC, Australia<italic>Goal:</italic> Auscultation for neonates is a simple and non-invasive method of diagnosing cardiovascular and respiratory disease. However, obtaining high-quality chest sounds containing only heart or lung sounds is non-trivial. Hence, this study introduces a new deep-learning model named NeoSSNet and evaluates its performance in neonatal chest sound separation with previous methods. <italic>Methods:</italic> We propose a masked-based architecture similar to Conv-TasNet. The encoder and decoder consist of 1D convolution and 1D transposed convolution, while the mask generator consists of a convolution and transformer architecture. The input chest sounds were first encoded as a sequence of tokens using 1D convolution. The tokens were then passed to the mask generator to generate two masks, one for heart sounds and one for lung sounds. Each mask is then applied to the input token sequence. Lastly, the tokens are converted back to waveforms using 1D transposed convolution. <italic>Results:</italic> Our proposed model showed superior results compared to the previous methods based on objective distortion measures, ranging from a 2.01 dB improvement to a 5.06 dB improvement. The proposed model is also significantly faster than the previous methods, with at least a 17-time improvement. <italic>Conclusions:</italic> The proposed model could be a suitable preprocessing step for any health monitoring system where only the heart sound or lung sound is desired.https://ieeexplore.ieee.org/document/10531026/Deep learningheart soundlung soundphonocardiogram (PCG)single-channel sound separation |
| spellingShingle | Yang Yi Poh Ethan Grooby Kenneth Tan Lindsay Zhou Arrabella King Ashwin Ramanathan Atul Malhotra Mehrtash Harandi Faezeh Marzbanrad NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning IEEE Open Journal of Engineering in Medicine and Biology Deep learning heart sound lung sound phonocardiogram (PCG) single-channel sound separation |
| title | NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning |
| title_full | NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning |
| title_fullStr | NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning |
| title_full_unstemmed | NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning |
| title_short | NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning |
| title_sort | neossnet real time neonatal chest sound separation using deep learning |
| topic | Deep learning heart sound lung sound phonocardiogram (PCG) single-channel sound separation |
| url | https://ieeexplore.ieee.org/document/10531026/ |
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