A software sensor using neural networks for detection of patient workload

The morphology of intracardiac electrograms (IEGMs) was used for pacemaker patient workload estimation. The body posture also was studied as another characteristic. The IEGMs were obtained and recorded via temporary transcutaneous leads connected to the implanted pacemaker. IEGMs were recorded during exercise and at rest. Recordings at rest were performed in different body positions. The morphology was analyzed visually in order to observe changes due to workload and posture. The recordings were digitized and processed by a computer-simulated neural network. The network was used as an automatic IEGM classifier based on the morphology. Our results show that the morphology of the IEGM may be used as an indicator of patient workload and body posture. The necessary information is found mainly in the ST segment. We conclude that neural networks seem to be useful in an active cardiac device.