This study was concerned with the development and testing of online cognitive-load monitoring methods by means of a working-memory experiment using electrocardiogram (ECG) analyses for future applications in mixedinitiative human-machine interaction (HMI). To this end, we first identified potentially reliable cognitive-workload-related cardiac metrics and algorithms for online processing. We then compared our online results to those conventionally obtained with state-of-the-art offline methods. Finally, we evaluated the possibility of classifying low versus high working-memory load using different classification algorithms. Our results show that both offline and online methods reliably estimate the workload associated with a multi-level working-memory task at the group level, whether it is with the heart rhythm or the heart rate variation (standard deviation of the RR interval). Moreover, we found significant working-memory load classification accuracy using both two-dimensional linear discriminant analyses (LDA) or a support vector machine (SVM). We hence argue that our online algorithm is reliable enough to provide online electrocardiographic metrics as a tool for real-life workload evaluation and can be a valuable feature for mixed-initiative systems.