Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach

A core aim of neurocritical care is to prevent secondary brain injury. Spreading depolarizations (SDs) have been identified as an important independent cause of secondary brain injury. SDs are usually detected using invasive electrocorticography recorded at high sampling frequency. Recent pilot studies suggest a possible utility of scalp electrodes generated electroencephalogram (EEG) for non-invasive SD detection. However, noise and attenuation of EEG signals makes this detection task extremely challenging. Previous methods focus on detecting temporal power change of EEG over a fixed high-density map of scalp electrodes, which is not always clinically feasible. Having a specialized spectrogram as an input to the automatic SD detection model, this study is the first to transform SD identification problem from a detection task on a 1-D time-series wave to a task on a sequential 2-D rendered imaging. This study presented a novel ultra-light-weight multi-modal deep-learning network to fuse EEG spectrogram imaging and temporal power vectors to enhance SD identification accuracy over each single electrode, allowing flexible EEG map and paving the way for SD detection on ultra-low-density EEG with variable electrode positioning. Our proposed model has an ultra-fast processing speed (<0.3 sec). Compared to the conventional methods (2 hours), this is a huge advancement towards early SD detection and to facilitate instant brain injury prognosis. Seeing SDs with a new dimension - frequency on spectrograms, we demonstrated that such additional dimension could improve SD detection accuracy, providing preliminary evidence to support the hypothesis that SDs may show implicit features over the frequency profile

Sensor prototype for checkerboard visual evoked potentials

Trabajo de InvestigaciónIn this project the development of a sampling system of encephalographic signals stimulated by visual evoked potentials is disclosed. Through the design and construction of an analog sensor, the reading, amplification and filtering of encephalographic signals captured by dry electrodes is proposed. These signals are processed and taken to digital values to be sampled and analyzed by statistical techniques that allow the signal variations to be recognized and standardized against stimuli of visual evoked potentials presented at different frequencies. This system is proposed as a support system for the interpretation of data that may indicate neurodegenerative diseases with which psychologists and psychiatrists can work and diagnose patients.1. INTRODUCTION 2. PROBLEM STATEMENT 3. OBJECTIVES 4. CONCEPTUAL FRAMEWORK ¡Error! Marcador no definido. 5. THEORETICAL FRAMEWORK ¡Error! Marcador no definido. 6. STATE OF THE ART 7. METHODOLOGY 8. NOVEL CHARACTER OF THE PROJECT 9. ANALOG CIRCUIT DESIGN AND SIMULATION 10. ANALOG CIRCUIT ASSEMBLY 11. RESULTS 12. VALIDATION OF PROJECT 13. CONCLUSIONS AND FUTURE WORKS 14. ANNEXES 15. REFERENCIASMaestríaMagister en Ingeniería y Gestión de la Innovació