Rapid detection and interpretation of heart murmurs using phonocardiograms, transfer learning and explainable artificial intelligence

Yadav H, et al. CNN and bidirectional GRU-based heartbeat sound classification architecture for elderly people. Mathematics. 2023;11(6):1365.

Article  Google Scholar 

Alrabie S, Barnawi A. Evaluation of pre-trained CNN models for cardiovascular disease classifification: a benchmark study. Inform Sci Lett. 2023. https://doi.org/10.18576/isl/120755.

Article  Google Scholar 

Yaseen Y.G. Son, Kwon S. Classification of heart sound signal using multiple Features. Appl Sci. 2018. https://doi.org/10.3390/app8122344.

Article  Google Scholar 

Montinari MR, Minelli S. The first 200 years of cardiac auscultation and future perspectives. J Multidiscip Healthc. 2019;12:183–9.

Article  Google Scholar 

Attenhofer Jost CH, et al. Echocardiography in the evaluation of systolic murmurs of unknown cause. Am J Med. 2000;108(8):614–20.

Article  Google Scholar 

Elola A, et al. Beyond heart murmur detection: automatic murmur grading from phonocardiogram. IEEE J Biomed Health Inform. 2023;27(8):3856–66.

Article  Google Scholar 

Oliveira J et al. The CirCor DigiScope Phonocardiogram Dataset (version 1.0. 3). PhysioNet, 2022.

Ismail S, Siddiqi I, Akram U. Localization and classification of heart beats in phonocardiography signals —a comprehensive review. EURASIP J Adv Signal Proc. 2018. https://doi.org/10.1186/s13634-018-0545-9.

Article  Google Scholar 

Ozcan F, Alkan A. Explainable audio CNNs applied to neural decoding: sound category identification from inferior colliculus. Signal Image Video Proc. 2024;18(2):1193–204.

Article  Google Scholar 

Syed ZS, Memon SA, Memon AL. Deep acoustic embeddings for identifying parkinsonian speech. Int J Adv Comput Sci Appl. 2020. https://doi.org/10.14569/IJACSA.2020.0111089.

Article  Google Scholar 

Cramer J, et al. Look listen and learn more: design choices for deep audio embeddings. IEEE. 2019. https://doi.org/10.1109/ICASSP.2019.8682475.

Article  Google Scholar 

Loh HW, et al. Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011–2022). Comput Methods Prog Biomed. 2022;226:107161.

Article  Google Scholar 

Kirmaci H. Çelişik Olmayan Önermelerden Oluşan Bir Bilim Dili Arayışı: Gösterim Teorisinden Sonra Russell’ın Tanımlı Be- timlemeler Teorisi ve Denotasyon Anlayışı. Kahramanmaraş Sütçü İmam Üniversitesi İlahiyat Fakültesi Dergisi. 2023;42:1–19.

Article  Google Scholar 

Dissanayake T, et al. A robust interpretable deep learning classifier for heart anomaly detection without segmentation. IEEE J Biomed Health Inform. 2021;25:2162–71.

Article  Google Scholar 

Vilone G, Longo L. Notions of explainability and evaluation approaches for explainable artificial intelligence. Inform Fusion. 2021;76:89–106.

Article  Google Scholar 

Tjoa E, Guan C. A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Trans Neural Netw Learn Syst. 2021;32:4793–813.

Article  Google Scholar 

Di Martino F, Delmastro F. Explainable AI for clinical and remote health applications: a survey on tabular and time series data. Artif Intell Rev. 2023;56(6):5261–315.

Article  Google Scholar 

Wang M, et al. Transfer learning models for detecting six categories of phonocardiogram recordings. J Cardiovasc Dev Dis. 2022. https://doi.org/10.3390/jcdd9030086.

Article  Google Scholar 

Azmy MM. Automatic diagnosis of heart sounds using bark spectrogram cepstral coefficients. J Med Res Inst. 2022. https://doi.org/10.21608/jmalexu.2023.281402.

Article  Google Scholar 

Maity A, Pathak A, Saha G. Transfer learning based heart valve disease classification from Phonocardiogram signal. Biomed Signal Proc Control. 2023. https://doi.org/10.1016/j.bspc.2023.104805.

Article  Google Scholar 

Yang C, et al. Abnormal heart sound detection from unsegmented phonocardiogram using deep features and shallow classifiers. Multimed Tools Appl. 2023. https://doi.org/10.1007/s11042-022-14315-8.

Article  Google Scholar 

Bhardwaj A, Singh S, Joshi D. Explainable deep convolutional neural network for valvular heart diseases classification using PCG signals. IEEE Trans Instrum Measurement. 2023. https://doi.org/10.1109/TIM.2023.3274174.

Article  Google Scholar 

Nguyen MT, Lin WW, Jin HH. Heart sound classification using deep learning techniques based on log-mel spectrogram. Circ Syst Signal Proc. 2023. https://doi.org/10.1007/s00034-022-02124-1.

Article  Google Scholar 

Donkada S et al. Early Heart Disease Detection Using Mel-Spectrograms and Deep Learning, in IEEE Conference on ICT Solutions for eHealth, IEEE, Editor. 2023.

Pleva M, Martens E, Juhar J Automated Covid-19 Respiratory Symptoms Analysis from Speech and Cough, in SAMI 2022 • IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics. 2022.

Gurjot S, et al. An automated diagnosis model for classifying cardiac abnormality utilizing deep neural networks. Multimed Tools Appl. 2023. https://doi.org/10.1007/s11042-023-16930-5.

Article  Google Scholar 

Tatulli E, Souriau R, Fontecave-Jallon J. Unsupervised segmentation of heart sounds from abrupt changes detection. Amsterdam: Elsevier; 2023.

Book  Google Scholar 

Fuadah YN, Pramudito MA, Lim KM. An optimal approach for heart sound classification using grid search in hyperparameter optimization of machine learning. Bioengineering (Basel). 2022. https://doi.org/10.3390/bioengineering10010045.

Article  Google Scholar 

Arjoune Y, et al. A noise-robust heart sound segmentation algorithm based on shannon energy. IEEE Access. 2024. https://doi.org/10.1109/ACCESS.2024.3351570.

Article  Google Scholar 

Ding J, Li J, Xu M. Classification of murmurs in PCG using combined frequency domain and physician inspired features. Comput Cardiol. 2022. https://doi.org/10.1371/journal.pdig.0000324.

Article  Google Scholar 

Bondareva E, et al. Embracing the imaginary: deep complex-valued networks for heart murmur detection. Comput Cardiol. 2022. https://doi.org/10.22489/CinC.2022.071.

Article  Google Scholar 

Summerton S, et al. Two-stage classification for detecting murmurs from phonocardiograms using deep and expert features. Comput Cardiol. 2022. https://doi.org/10.1371/journal.pdig.0000324.

Article  Google Scholar 

Xu Y, et al. Hierarchical multi-scale convolutional network for murmurs detection on PCG signals. Comput Cardiol. 2022. https://doi.org/10.1038/s41598-024-58274-6.

Article  Google Scholar 

Lu H, et al. A lightweight robust approach for automatic heart murmurs and clinical outcomes classification from phonocardiogram recordings. Comput Cardiol. 2022. https://doi.org/10.1371/journal.pdig.0000324.

Article  Google Scholar 

Costandache M, Cioata M, Iftene A. Automated heart murmur detection using sound processing techniques. Procedia Comput Sci. 2023;225(7):2961–70.

Article  Google Scholar 

Guo L, Darvenport S and Peng Y. Deep CardioSound-An Ensembled Deep Learning Model for Heart Sound MultiLabelling.

Andrade L, Camacho R and Oliveira J, A Deep Learning approach to infer morphological characteristics of the heart from cardiac sound analysis, in 12th International Conference on Bioscience, Biochemistry. 2023.

Chen Y et al. A heart sound classification method based on residual block and attention mechanism, in 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE, Editor. 2022.

Xu C, et al. Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning. Health Inf Sci Syst. 2024;12(1):2.

Article  Google Scholar 

Martins ML, Coimbra MT, Renna F. Markov-based neural networks for heart sound segmentation: using domain knowledge in a principled way. IEEE J Biomed Health Inform. 2023;27(11):5357–68.

Article  Google Scholar 

Alkhodari M, Hadjileontiadis LJ, Khandoker AH. Identification of congenital valvular murmurs in young patients using deep learning-based attention transformers and phonocardiograms. IEEE J Biomed Health Inform. 2024. https://doi.org/10.1109/JBHI.2024.3357506.

Article  Google Scholar 

CesarelliM et al. Deep learning for heartbeat phonocardiogram signals explainable classification, in 2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE). 2022.

Rajeshwari BS, et al. Detection of phonocardiogram event patterns in mitral valve prolapse: an automated clinically relevant explainable diagnostic framework. IEEE Trans Instru Meas. 2023;72:1–9.

Google Scholar 

Freeman A, Levine S. The clinical significance of the systolic murmur a study of 1000 consecutive “non-cardiac” cases. Ann Intern Med. 1933. https://doi.org/10.7326/0003-4819-6-11-1371.

Article  Google Scholar 

Oliveira J, et al. The CirCor DigiScope dataset: from murmur detection to murmur classification. IEEE J Biomed Health Inform. 2022;26(6):2524–35.

Article  Google Scholar 

Peng X, et al. Multi-class voice disorder classification using openL3-SVM. SSRN eLibrary. 2022. https://doi.org/10.2139/ssrn.4047840.

Article  Google Scholar 

Douglas O. Speech Communications: Human and Machine. 1987, http://ieeexplore.ieee.org/document/5312112: Addison-Wesley Publishing Company.

Özcan F, Alkan A. Neural decoding of inferior colliculus multiunit activity for sound category identification with temporal correlation and transfer learning. Network-Comput Neural Syst. 2024. https://doi.org/10.1080/0954898X.2023.2282576.

Article  Google Scholar 

Matlab, Standard Deviation. 2024a.

Ozcan F, Alkan A. Frontal cortex neuron type classification with deep learning and recurrence plot. Traitement du Signal. 2021;38:3.

Article 

留言 (0)

沒有登入
gif