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In this study, the convolutional neural network (CNN) and multipart interactive training were used to create a state-of-the-art classifier for the early detection of cardiac pathologies. The classification was performed using three sets of data samples; the first was recorded using a digital stethoscope and the others were recorded with mobile smart devices. These data were part of the competition on the Kaggle platform and used on the CNN in the form of audio samples as well as in spectrogram format, and experiments were conducted using both methods. Moreover, this method was used not only to distinguish healthy from unhealthy heart rhythm but also to attempt reaching some initial diagnosis. It was possible to identify a set of problems with the dataset, make corrections, and share them with the scientific community. The experiments were conducted using the so-called multi-part interactive training and an additional ResNet pre-trained network, resulting in more than 93% precision. This allows anyone to undertake prophylactical diagnosis using a smartphone alone. It also has a great educational potential for young doctors and students. Greater improvement is possible by supplying more data to the described method.

Early and Remote Detection of Possible Heartbeat Problems With Convolutional Neural Networks and Multipart Interactive Training