Analyzing Electrocardiogram Data-Statistical Insights Into Cardiac Health

Abstract

The integration of statistical analysis methods with electrocardiogram (ECG) data has revolutionized cardiac health assessment. This chapter delves into key statistical techniques applied to ECG data for enhanced insights into heart health. It covers data preprocessing steps like noise reduction and signal normalization to ensure data accuracy. Statistical analysis methods such as correlation analysis, time series analysis, and regression modeling are explored to uncover patterns and relationships in ECG signals. The chapter also discusses the importance of statistical power analysis for sample size determination in ECG studies. Furthermore, it highlights the role of machine learning algorithms in predictive modeling and anomaly detection for early cardiac risk assessment. Overall, statistical approaches to ECG data analysis play a pivotal role in advancing cardiovascular health diagnostics and patient care strategies.