Novel wearable sensor modalities, such as bio-impedance (Bio-Z), are being introduced and often provide various advantages over current state-of-the-art in terms of accuracy, sensing coverage, or convenience of wear. The principal challenge, however, lies in the ability to interpret the sensor reading by healthcare providers. In this work, we propose a two-stage deep learning framework that leverages a novel attention mechanism to translate Bio-Z signals to highly interpretable electrocardiogram (ECG) waveforms while also predicting translation uncertainty. Our experiments indicate a 66% improvement in accuracy for 1D-CNN based models to perform competitively with more sophisticated hybrid CNN-LSTM based models in a fraction of the training time while also providing a valid uncertainty measurement.