Monitoring Critical Health Conditions in the Elderly: A Deep Learning-Based Abnormal Vital Sign Detection Model

Global population aging creates distinct healthcare needs, particularly for older adults and those with serious illnesses. There are several gaps in current models for monitoring elderly individuals. These include the limited application of advanced deep learning techniques in elderly health monitoring, the lack of real-time anomaly detection for vital signs, the absence of robust evaluations using real-world data, and the failure to tailor monitoring systems specifically for the unique needs of elderly individuals. This study addresses these gaps by proposing a Hierarchical Attention-based Temporal Convolutional Network (HATCN) model, which enhances anomaly detection accuracy and validates effectiveness using real-world datasets. While the HATCN approach has been used in other fields, it has not yet been applied to elderly healthcare monitoring, making this contribution novel. Specifically, this study introduces a Hierarchical Attention-based Temporal Convolutional Network with Anomaly Detection (HATCN-AD) model, based on the real-world MIMIC-II dataset. The model was validated using two subjects from the MIMIC-II dataset: Subject 330 (Dataset 1) and Subject 441 (Dataset 2). For Dataset 1 (Subject 330), the model achieved an accuracy of 99.15% and precision of 99.47%, with stable recall (99.45%) and F1-score (99.46%). Similarly, for Dataset 2 (Subject 441), the model achieved 99.11% accuracy, 99.35% precision, and an F1-score of 99.44% at 100 epochs. The results show that the HATCN-AD model outperformed similar models, achieving high recall and precision with low false positives and negatives. This ensures accurate anomaly detection for real-time healthcare monitoring. By combining Temporal Convolutional Networks and attention mechanisms, the HATCN-AD model effectively monitors elderly patients' vital signs.