Explainable Deep Learning for Non-Invasive Detection of Pulmonary Artery Hypertension from Heart Sounds
| العنوان: | Explainable Deep Learning for Non-Invasive Detection of Pulmonary Artery Hypertension from Heart Sounds |
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| المؤلفون: | Alex Gaudio, Coimbra, Miguel T., Aurélio Campilho, Asim Smailagic, Samuel Emil Schmidt, Francesco Renna |
| المصدر: | Gaudio, A, Coimbra, M T, Campilho, A, Smailagic, A, Schmidt, S E & Renna, F 2022, ' Explainable Deep Learning for Non-Invasive Detection of Pulmonary Artery Hypertension from Heart Sounds ', Computing in Cardiology, CinC, Tampere, Finland, 04/09/2022-07/09/2022 . < https://cinc.org/2022/Program/accepted/295.html > Aalborg University |
| بيانات النشر: | Computing in Cardiology, 2022. |
| سنة النشر: | 2022 |
| الوصف: | Aims: Late diagnoses of patients affected by pulmonary hypertension (PH) have a poor outcome. This observation has led to a call for earlier, non-invasive PH detection. Cardiac auscultation can represent a non-invasive, cost-effective alternative to both right heart catheterization and doppler analysis to measure the pulmonary artery pressure (PAP), due to the physiological relations that links changes in the PAP with the morphology of heart sounds. Methods: We propose the use of deep neural network applied to digital heart sound recordings to detect hypertensive patients. Post-hoc explanations are used to identify which features of the heart sounds are mostly informative of the presence of PH. Results: The proposed approach was tested over a dataset of 42 subjects (29 with PH and 13 without PH), with reference PAP measurements obtained via right heart catheterization. The proposed approach achieves .95 ROC AUC, compared with .78 of an adaptation of the Gaussian mixture model (GMM) method proposed in (Kaddoura et al. 2016). Classification outputs are shown to be fundamentally based on specific regions of the heart sounds. Conclusions: The results obtained with the proposed method show that deep learning approaches applied to heart sound achieve the state-of-the-art. Aims: Late diagnoses of patients affected by pulmonary hypertension (PH) have a poor outcome. This observation has led to a call for earlier, non-invasive PH detection. Cardiac auscultation can represent a non-invasive, cost-effective alternative to both right heart catheterization and doppler analysis to measure the pulmonary artery pressure (PAP), due to the physiological relations that links changes in the PAP with the morphology of heart sounds. Methods: We propose the use of deep neural network applied to digital heart sound recordings to detect hypertensive patients. Post-hoc explanations are used to identify which features of the heart sounds are mostly informative of the presence of PH. Results: The proposed approach was tested over a dataset of 42 subjects (29 with PH and 13 without PH), with reference PAP measurements obtained via right heart catheterization. The proposed approach achieves .95 ROC AUC, compared with .78 of an adaptation of the Gaussian mixture model (GMM) method proposed in (Kaddoura et al. 2016). Classification outputs are shown to be fundamentally based on specific regions of the heart sounds. Conclusions: The results obtained with the proposed method show that deep learning approaches applied to heart sound achieve the state-of-the-art. |
| تدمد: | 2325-887X |
| DOI: | 10.22489/cinc.2022.295 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3cc77c5bfab7d575442168fc93af2a79 https://doi.org/10.22489/cinc.2022.295 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....3cc77c5bfab7d575442168fc93af2a79 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 2325887X |
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| DOI: | 10.22489/cinc.2022.295 |