First responders have been observed to be at an increased risk of cardio-vascular diseases compared to the general population with a high percentage of cardiac events occurring during mission execution. Continuous physiological monitoring during missions can be effective in reducing the number of fatalities. Real-time physiological data such as ECG can be collected using sensors worn on the body. This sensor data can be processed on the body itself or can be communicated over an ad hoc wireless network to the incident command center or base station located near by. First responder missions often take place inside building structures where network connectivity is intermittent. Intermittent connectivity can lead to loss of critical physiological data or delay in that information reaching the base station. Hence, some amount of local processing is needed in order to limit the data that is communicated. In this paper, we introduce a novel Hidden Markov Model based myocardial infarction detection approach. The fidelity of this approach can be adapted based on the processing power available. We present a peer-to-peer networking protocol for communication over disrupted networks. A low fidelity classifier is used to perform local processing and assign priorities to the data based on its criticality. It is complemented by a disruptionaware epidemic forwarding protocol for transferring first responder’s physiological data to the base station. With prioritized epidemic forwarding and buffer eviction policy, our protocol increases packet delivery ratio and reduces networking delay when end-to-end route disruption occurs. Finally, we report the effect of network disruption on myocardial infarction detection rate and latency of detection and the improvements achieved by our protocol
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