A hybrid TCN–BiLSTM architecture for accurate real time patient health prediction

This is the authors' abstract. We don't add key points for this paper.

Models that are both highly accurate and computationally efficient for edge deployment are required for IoT sensor streams for real-time patient health prediction. To predict catastrophic health events, this work presents TCB-Health, a hybrid Temporal Convolutional Network (TCN) and Bidirectional LSTM (Bi-LSTM) architecture that uses multivariate physiological inputs, including respiration, accelerometer data, ECG, and PPG. The large MIMIC-IV waveform database (>10,000 patients) was used for additional validation, and the WESAD wearable dataset (15 participants) was employed for the investigations. The proposed model surpasses the CNN-only, GRU, and solo TCN baselines, achieving classification accuracy of 98.67%, precision of 98.54%, recall of 98.82%, F1-score of 98.90%, and inference latency of less than 10 ms per data window. The method reduces computational costs by around 35% in FLOPs and 28% in parameters, enabling reliable real-time deployment on edge devices with constrained resources. These results demonstrate that TCB-Health, in IoT-enabled healthcare systems, provides a scalable and effective solution for remote patient monitoring, early detection of cardiac abnormalities, and real-time clinical decision support.