M Anand, K Arjun, E Ramya Krishna, Hirematt Ganesh Kumar
Abstract
Over 26 million individuals worldwide suffer from chronic heart failure (CHF), and the number of cases is rising by 2% a year. Even in the academic community, there are surprisingly few approaches for automatically identifying CHF, considering the substantial burden that CHF presents and the widespread use of sensors in our daily life. We describe a cardiac sound-based technique for CHF detection. The approach blends end-to-end Deep Learning (DL) with traditional Machine Learning (ML). While the DL learns from a spectro-temporal representation of the signal, the standard ML learns from expert characteristics. 947 participants' recordings from six publicly accessible datasets and one CHF dataset that was gathered specifically for this research were used to assess the approach. Utilising the same assessment technique as a previous PhysoNet challenge, the suggested approach obtained a score of 89.3, surpassing the challenge's baseline method by 9.1. The total accuracy of the approach is 92.9% (error of 7.1%); while there is a lack of direct comparability between the experimental and method findings, this error rate is similar to the proportion of recordings that experts have classified as "unknown" (9.7%). Ultimately, with an accuracy of 93.2%, we discovered 15 expert characteristics that are helpful for developing machine learning models that distinguish between CHF phases—that is, the decompensated phase during hospitalisation and the recompensated phase. The suggested approach demonstrates encouraging outcomes for the identification of discrete stages of CHF as well as for the differentiation of recordings between patients and healthy people. This could facilitate the identification of newly diagnosed CHF patients and facilitate the creation of CHF monitors that can be used at home to prevent hospital stays.
