Key result
The EMAP cloud-edge hybrid framework achieved an average prediction accuracy of 94% for seizures, 73% for encephalopathy, and 79% for strokes in real-time EEG monitoring.
Why the study?
Current techniques for predicting neurological disorders focus on individual disorders and are computationally expensive, causing delays that can compromise predictions during critical events.
The EMAP cloud-edge hybrid framework enables real-time prediction of neurological anomalies from EEG signals with high accuracy and reduced computational exploration time.
Answers are grounded in this paper and cite their sources.
May support real-time EEG monitoring feasibility; leaves open prospective clinical validation before any practice change.
State-of-the-art techniques for detecting, or predicting, neurological disorders (1) focus on predicting each disorder individually, and are (2) computationally expensive, leading to a delay that can potentially render the prediction useless, especially in critical events. Towards this, we present a real-time two-tiered framework called EMAP, which cross-correlates the input with all the EEG signals in our mega-database (a combination of multiple EEG datasets) at the cloud, while tracking the signal in real-time at the edge, to predict the occurrence of a neurological anomaly. Using the proposed framework, we have demonstrated a prediction accuracy of up to 94% for the three different anomalies that we have tested.
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Prabakaran et al. (2020) studied Neurological anomalies (seizures, encephalopathy, strokes) (n=300). EMAP framework vs. State-of-the-art techniques was evaluated on Prediction accuracy for seizures. The EMAP cloud-edge hybrid framework achieved an average prediction accuracy of 94% for seizures, 73% for encephalopathy, and 79% for strokes in real-time EEG monitoring.
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