A Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches

Abstract

El ectrocardi ogram captures the el ectri cal activity of the heart. The si gnal obtained can be used for vari ous purposes such as emoti on recogni ti on, heart rate measuring and the mai n one, cardi ac di sease di agnosi s. But ECG anal ysis and cl assification requi re experi enced speci al ists once i t presents hi gh vari abil ity and suffers i nterferences from noi ses and artefacts. Wi th the i ncrease of data amount on l ong term records, i t mi ght l ead to l ong term dependenci es and the process become exhausti ve and error prone. Automated systems associ ated wi th si gnal processing techni ques ai m to hel p on these tasks by i mprovi ng the qual i ty of data, extracti ng meani ngful features, sel ecti ng the most sui tabl e and trai ni ng machi ne l earning model s to capture and general i ze i ts behavi our. Thi s revi ew bri ngs a bri ef stage sense of how data fl ows i nto these approaches and somewhat techni ques are most used. It ends by presenti ng some of the countl ess appl i cations that can be found i n the research communi ty.