Abstract
A lot of people suffer from heart-related diseases. It is necessary to detect these problems in the early stages so that proper treatment can be administered to the patients. Deaths in rural India have overtaken deaths in urban India due to heart-related diseases. The rural areas lack proper medical facilities and doctors, and it makes the detection of heart diseases difficult. In this paper, an effective deep learning-based automatic approach for heart anomaly detection has been proposed to ease the process of heart anomaly detection. It is capable of taking heart sounds recorded through smart phones as an input. The heart sounds are pre-processed (framing, artifact removal, and de-noising), then converted into a Mel-spectrogram, i.e., visual approach for representing the heart signal over time at different frequencies present in an oriented waveform. The Mel-spectrogram was used as image for feature extraction, and then fed to a CNN for classification. It achieved an accuracy of 93.76% to diagnose multiple heart anomalies, and such technology can be integrated for heart disease screening in remote areas.
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Rani, S., Dutta, M.K. (2022). Heart Anomaly Classification Using Convolutional Neural Network. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications . Lecture Notes in Networks and Systems, vol 288. Springer, Singapore. https://doi.org/10.1007/978-981-16-5120-5_41
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DOI: https://doi.org/10.1007/978-981-16-5120-5_41
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