HRIDAI: A Tale of Two Categories of ECGs Priya Ranjan 1(B ) , Kumar Dron Shrivastav 4 , Satya Vadlamani 2 , and Rajiv Janardhanan 3 1 SRM University, Neerukonda, Mangalagiri Mandal, Guntur District, Mangalagiri 522502, Andhra Pradesh, India ranjan.p@srmap.edu.in 2 Laboratory of Disease Dynamics and Molecular Epidemiology, Amity Institute of Public Health, Amity University Uttar Pradesh, Sector 125, Noida, India vadlamani.satya93@gmail.com 3 Laboratory of Disease Dynamics and Molecular Epidemiology, Health Data Analytics and Visualization Environment, Amity Institute of Public Health, Amity University Uttar Pradesh, Sector 125, Noida, India rjanardhanan@amity.edu 4 Health Data Analytics and Visualization Environment, Amity Institute of Public Health, Amity University Uttar Pradesh, Sector 125, Noida, India kdshrivastav@amity.edu Abstract. This work presents a geometric study of computational dis- ease tagging of ECGs problems. Using ideas like Earthmover’s distance (EMD) and Euclidean distance, it clusters category 1 and category -1 ECGs in two clusters, computes their average and then predicts the cat- egory of 100 test ECGs, if they belong to category 1 or category -1. We report 80% success rate using Euclidean distance at the cost of intense computation investment and 69% success using EMD. We suggest fur- ther ways to augment and enhance this automated classiﬁcation scheme using bio-markers like Troponin isoforms, CKMB, BNP. Future direc- tions include study of larger sets of ECGs from diverse populations and collected from a heterogeneous mix of patients with diﬀerent CVD condi- tions. Further we advocate the robustness of this programmatic approach as compared to deep learning kind of schemes which are amenable to dynamic instabilities. This work is a part of our ongoing framework Heart Regulated Intelligent Decision Assisted Information (HRIDAI) system. Keywords: ECG · Computational disease tagging · Biomarkers · ECG-visualization 1 Introduction Cardiovascular diseases (CVD) account for 17.9 million (31%) deaths each year worldwide. More than 75% of CVD deaths occur in low- to middle-income coun- c  Springer Nature Singapore Pte Ltd. 2021 S. M. Thampi et al. (Eds.): SIRS 2020, CCIS 1365, pp. 1–21, 2021. https://doi.org/10.1007/978-981-16-0425-6_18