Deep Reinforcement-Based Anomaly Detection: An Enhanced Unsupervised Approach for Medical Time Series Data

In the era of big data, the ability to identify anomalies in medical time series data has become increasingly vital for a variety of healthcare applications. Timely identification of anomalies in critical vital signs, such as heart rate and blood pressure, is essential for ensuring patient safety and guiding clinical decision-making. Traditional anomaly detection methods, however, often struggle with the inherent complexity of time series data, including patterns of periodicity, trends, and seasonality. This study introduces Deep Reinforcement-Based Anomaly Detection, a revolutionary unsupervised deep learning approach that effectively detects several forms of anomalies in time series data, including point anomalies, contextual anomalies, and discords. By leveraging Convolutional Neural Networks (CNNs), the method predicts future values based on time series windows, and compares these predictions with actual data points to identify anomalies in real time. This approach has the advantage of being able to operate without labeled data and to manage scenarios where anomalies make up less than 5% of the dataset, making it suitable for healthcare applications where anomalies are rare. Empirical evaluations on publicly available benchmark datasets show that the approach we employ outperforms cutting-edge methods across diverse datasets, including medical time series data. Furthermore, its versatility in supporting both univariate and multivariate time series enables wide applicability, making it a powerful tool for detecting time series anomalies.