Biology subjects

Eshraghian, J. K.

Publications and source records attributed to Eshraghian, J. K..

2 recordsLinked to original sources

bioRxiv2023

Neuromorphic Cytometry: Implementation on cell counting and size estimation

Flow cytometry is a widespread and high-throughput technology that can measure the features of cells and can be combined with fluorescence analysis for additional phenotypical characterisations but only provide low-dimensional output and spatial resolution. Imaging flow cytometry is another technology that offers rich spatial information, allowing more profound insight into single-cell analysis. However, offering such high-resolution, full-frame feedback can compromise speed and has become a significant trade-off challenge to tackle during development. In addition, the current dynamic range offered by conventional photosensors can only capture limited fluorescence signals, exacerbating the difficulties in elevating performance speed. Neuromorphic photo-sensing architecture focuses on the events of interest via individual-firing pixels to reduce data redundancy and provide low latency in data processing. With the inherent high dynamic range, this architecture has the potential to drastically elevate the performance in throughput by incorporating motion-activated spatial resolution. Herein, we presented an early demonstration of neuromorphic cytometry with the implementation of object counting and size estimation to measure 8 m and 15 m polystyrene-based microparticles and human monocytic cell line (THP-1). In this work, our platform has achieved highly consistent outputs with a widely adopted flow cytometer (CytoFLEX) in detecting the total number and size of the microparticles. Although the current platform cannot deliver multiparametric measurements on cells, future endeavours will include further functionalities and increase the measurement parameters (granularity, cell condition, fluorescence analysis) to enrich cell interpretation.

cell biology↗

bioRxiv2021

A multimodal AI system for out-of-distribution generalization of seizure detection

Epilepsy is one of the most common severe neurological disorders worldwide. The International League Against Epilepsy (ILAE) define epilepsy as a brain disorder that generates (1) two unprovoked seizures more than 24 hrs apart, or (2) one unprovoked seizure with at least 60% risk of recurrence over the next ten years. Complete remission has been defined as ten years seizure free with the last five years medication free. This requires a cost-effective ambulatory ultra-long term out-patient monitoring solution. The common practice of self-reporting is inaccurate. Applying artificial intelligence (AI) to scalp electroencephalogram (EEG) interpretation is becoming increasingly common, but other data modalities such as electrocardiograms (ECGs) are simpler to collect and often recorded simultaneously with EEG. Both recordings contain biomarkers in the detection of seizures. Here, we propose a state-of-the-art performing AI system that combines EEG and ECG for seizure detection, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted nonpatient-specific inference-only tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 patients shortlisted by neurologists and 30 randomly selected). Across all datasets, our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for prior state-of-the-art approaches using EEG and ECG alone, respectively. Our models state-of-the-art performance and robustness to out-ofdistribution datasets can improve the accuracy and efficiency of epilepsy diagnoses.

neuroscience↗