Application of LSTM Networks for Continuous Patient Monitoring and Anomaly Detection in Wearable Health Devices

Objectives: This paper looks into the application of LSTM networks in improving continuous patient monitoring and anomaly detection on wearable health devices by using accurate analysis of sequential physiological data. Methods: This paper presents a new use of LSTM networks, which are very powerful in the analysis of time series data, making them quite accurate in their predictions and thus adding functionality to wearable health devices. Our approach is to train an LSTM model on a huge dataset such as PhysioNet, which includes over 60,000 recordings of ECG signals from more than 50 different databases, encompassing a wide variety of physiological conditions and signal types, so the model learns across a wide range of health conditions and demographics. Findings: The proposed LSTM-based model reduces false positives by 30% and false negatives by 40%, hence improving anomaly detection accuracy by up to 30% over prior methods such as SVM, Random Forest, KNN, DBSCAN, TDL, and DQN. Novelty: This work uniquely enhances the existing literature by applying LSTM networks specifically to wearable health devices, optimizing the detection of health anomalies in real time with higher accuracy compared to traditional methods. It also introduces a novel approach to handling complex sequential physiological data, contributing to more reliable and timely medical interventions. Keywords: LSTM networks, Wearable Health Devices, Patient Monitoring, Anomaly Detection, Time-series data