ECG wavelet graph for IoT-enabled well-being monitoring

IoT-based electrocardiogram (ECG) monitoring can support well-being assessment across a range of applications, from emotion recognition to arrhythmia detection. In this work, we propose a novel graph-wavelet-based representation of the ECG signal for classification under different emotional and health conditions. The proposed wavelet-graph encodes the persistence of ECG components across multiple wavelet subbands, effectively discriminating structurally different signals both in affective computing and arrhythmia detection tasks. The proposed wavelet-graph approach applies a single unified framework across diverse tasks-a characteristic that remains relatively uncommon in the current literature-outperforming task-specific methods by up to 4% and maintaining competitive performance across all evaluated settings, despite those methods being individually optimized for a single classification problem. Furthermore, the proposed approach relies on substantially fewer training samples and a markedly reduced number of trainable parameters compared to existing methods, thereby supporting near-real-time IoT-based well-being monitoring applications.