ECG Time Series Analysis and Prediction: A Data-Driven Approach
Abstract
Electrocardiography (ECG) serves as a vital diagnostic tool in cardiology, capturing the electrical activity of the heart over time. In this study, we analyze a univariate ECG time-series dataset with over 17,000 data points. By exploring patterns in the data using descriptive analytics and visualizations, we aim to identify trends, anomalies, and insights for potential predictive modeling. This paper elaborates on the methodology, data analysis, results, and implications for cardiovascular monitoring systems.
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Introduction
Cardiovascular diseases are a leading cause of death worldwide, with ECG monitoring being pivotal for early detection and management. The ability to interpret ECG signals using computational tools has immense clinical value. This study aims to analyze a time-series ECG dataset to understand its statistical characteristics and visualize heartbeat dynamics over time.
Conclusion
This study provides an in-depth exploratory analysis of ECG data using a 1D time-series dataset.