Multi-modal Feature Based for Phonocardiogram Signal Classification Using Autoencoder

Fathurahman, Muhamad; Rachmawati, Ummi Azizah; Haryanti, Sri Chusri

doi:10.1007/978-3-030-36056-6_17

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…As seen in the examples above, CNN is practically used as a deep learning method in classifying heart sounds. Recently, there is still great interest in running alternative heart sound classification solutions developed with CNN [71] , [72] , Recurrent CNN [73] , general CNN models [74] , [75] , Deep Neural Network (DNN) [76] , Long Short-Term Memory [77] , and AEN [78] , [79] , [80] . In this study, an alternative model of AEN was used to directly classify heart sound data without ever dealing with images.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network

Deperlioğlu

Köse

Gupta

et al. 2020

Computer Communications

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Objective of this study is to introduce a secure IoHT system, which acts as a clinical decision support system with the diagnosis of cardiovascular diseases. In this sense, it was emphasized that the accuracy rate of diagnosis (classification) can be improved via deep learning algorithms, by needing no hybrid-complex models, and a secure data processing can be achieved with a multi-authentication and Tangle based approach. In detail, heart sounds were classified with Autoencoder Neural Networks (AEN) and the IoHT system was built for supporting doctors in real-time. For developing the diagnosis infrastructure by the AEN, PASCAL B-Training and Physiobank-PhysioNet A-Training heart sound datasets were used accordingly. For the PASCAL dataset, the AEN provided a diagnosis-classification performance with the accuracy of 100%, sensitivity of 100%, and the specificity of 100% whereas the rates were respectively 99.8%, 99.65%, and 99.13% for the PhysioNet dataset. It was seen that the findings by the developed AEN based solution were better than the alternative solutions from the literature. Additionally, usability of the whole IoHT system was found positive by the doctors, and according to the 479 real-case applications, the system was able to achieve accuracy rates of 96.03% for normal heart sounds, 91.91% for extrasystole, and 90.11% for murmur. In terms of security approach, the system was also robust against several attacking methods including synthetic data impute as well as trying to penetrating to the system via central system or mobile devices.

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