Multiscale Wavelet Method for Heart Abnormality Detection Within IoTs Environment

Abstract

The Internet of Things (IoT) is one of the fastest emerging technologies with many different applications in a number of fields. Within the medical domain it is rapidly expanding the capabilities of the IoT technology. The adoption of the IoT to ECG monitoring has the potential to provide maximum information about the electrical activity of the heart as well as allows the large volume of information to be fully used. This paper proposes the idea of a system that utilizes the IoT with the pre-processing and feature extractions done with the use of discrete wavelet transforms and multiscale analysis. However, efficiency is an important issue due to large and complicated interconnections. The use of features rather than raw data makes the process efficient. We introduce multiscale concept based on modulus maxima and minima for feature extraction, which relies on relative distances from R peaks. We named it Gated multiscale selection and also extended this methodology and introduced a Linear multiscale approach. We have found that the specific Linear multiscale combinations achieve the highest accuracy in individual peak identifications and we demonstrated that the proposed method performs better than methods found in literature.