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International Society of Science and Applied Technologies |
| Reliability-Quantified Multi-Task Learning: Dual-Head Architectures of Risk-Awareness for EEG Analysis | ||||
| Author |
Wenjuan Wu
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| Co-Author(s) |
Mohsin Hasan; Xufeng Zhao
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| Abstract | Neurological Disorders globally affect millions of people,
a major burden, both personally and financially for individuals,
as well as for society. Although deep learning models provide
accurate diagnoses in laboratory settings with curated data.
Deep learning models frequently do not work well when they
are used on newer portable, user friendly, for EEG reliability
because of the more contaminated noise found within the signal due to the variability of channels. In addition to there being less channels than with traditional EEG analysis this reliability issue prevents the clinician from being able to use the output of the deep learning model to make an informed decision, because there is no way to provide the clinician with the uncertainty associated with the output using currently available standard modeling. Therefore, this work’s objectives are to present a framework called a reliability-based LSTM, a multi-task learning system of analysing the EEG; for use in a clinical setting. the analysis has been modified to using a dual-head LSTM with three qualitatively different options for estimating the outputs (easy measure—High Reliability Score (HRS), temperature scaling, and conformal prediction. This integration will allow a model to continue to provide a high degree of diagnostic confidence and produce appropriately calibrated confidence estimates for risk-based decision making. The proposed model achieved a seizure detection accuracy rate of 96.66%, while also providing appropriately calibrated results as evidenced by near perfect performance on reliability diagrams and the Risk-Coverage curve. The HRS allowed the authors to risk stratify using a mean HRS of 0.78 and a high confidence (0.8) rate covering 72.88%. This highlighted the pathway available for partially automating clinical surveillance application where a high-confidence prediction could initiate an immediate alert and cases of low confidence would be flagged for expert review. In conclusion, this project provided precise diagnoses of EEG data in combination with reliable, actionable measures of reliability from which, high performance models can be transformed into clinically reliable tools suitable for real world use.
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| Keywords | Dual-Head Architecture, EEG Reliability, Risk Assessment, LSTM, Failure Case Analysis | |||
| Article #: RQD2026-162 | ||||
Proceedings of 31st ISSAT International Conference on Reliability & Quality in Design |