Abstract
Cardiac arrhythmia refers to a group of conditions that causes the heart to beat too slow or too fast. It is one of the major problems of the heart which needs to be diagnosed at the earliest, as it takes more time for doctors to detect and provide medication. We find different types of arrhythmias; for slow heartbeat, it is called bradycardia; for fast heartbeat, it is called tachycardia. During initial stages of cardiac arrhythmia, doctors need to carefully examine the heartbeats precisely from different locations of the body. Manually evaluating these fundamental heart sounds (FHSs) for each and every patient is time consuming. Thus, automating the procedure by using machine learning techniques to classify heart sound recordings would help in overcoming this problem. The objective is to take the phonocardiogram (PCG) signals for evaluation, convert it to spectrogram images, and train a convolutional neural network model to predict the outcome. Then given a new PCG recording, it will be able to classify as normal or abnormal. Hence, the process of detecting arrhythmia is simplified and saves people's lives.
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Athreya, A.M., Paramesha, K., Avani, H.S., Pooja, Madhu, S. (2022). Neural Networks for Detecting Cardiac Arrhythmia from PCG Signals. In: Saraswat, M., Sharma, H., Arya, K.V. (eds) Intelligent Vision in Healthcare. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-7771-7_9
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DOI: https://doi.org/10.1007/978-981-16-7771-7_9
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