Surgery is a mentally demanding task that is focused on patient safety and requires the precise execution of motor control and decision making in a timely manner. Episodes of high Cognitive Workload (CWL) induced by stressors or distractions have been shown to lead to inferior performance potentially compromising patient safety. We have proposed a promising CWL assessment platform utilising a wide range of physiological sensors. However, there are some disadvantages associated with a complex multimodal sensing design, including high device cost, long set up time and the discomfort caused by wearing multiple wearable sensors for long periods during surgery. To address this problem, the proposed one-dimensional convolutional neural network (1D-CNN) model discussed here, offers an alternative solution to recognising CWL states, achieving satisfactory performance (91.2% accuracy) with the use of a wireless ECG sensor alone, showing great potential for widespread deployment in the operating room (OR).