Biology subjects

Delaney, C. M.

Publications and source records attributed to Delaney, C. M..

3 recordsLinked to original sources

bioRxiv2026

Frequency modulated semi-continuous wave Doppler ultrasound: A novel method for the detection of scatterer velocity and range using wearable ultrasound instrumentation

Doppler ultrasound imaging has become a key tool for clinicians and researchers to measure the velocity of blood and tissues within the human body. Presently pulsed wave and continuous wave Doppler imaging dominate the space as the two most prominent methods and are considered distinct from one another. However, these two techniques can also be considered as ends of a continuum along which Doppler imaging and phase tracking techniques exist. At the midpoint between these methods exists another underexplored Doppler imaging technique we refer to as frequency modulated semi-continuous wave (FMSCW) Doppler. We present a full technical overview of this technique, simulations exploring the impact of selected imaging parameters as well as how noise impacts imaging, and present three exemplary imaging sessions collected from human participants using FMSCW Doppler collected from a wearable ultrasound system. The results of the simulations evaluating the impact of imaging parameters demonstrate the trade-off between resolution in the range and velocity axis as well as the impact of scatterer range and speed on the measurement error. The three sets of images collected from human participants serve to demonstrate FMSCW Doppler used in a variety of wearable ultrasound applications including cardiovascular monitoring, rehabilitation assessment, and prosthetic device control. Furthermore, we discuss the relationship between FMSCW Doppler and isomorphic techniques from radar as well as potential inspiration for improvements for FMSCW Doppler inspired by the echolocation of bats.

bioengineering↗

bioRxiv2025

Time-series models can forecast long periods of human temporalEEG responses to randomly alternating visual stimuli

Visual stimuli with constant temporal frequency input is known to induce peaks in the driving frequency of the power spectrum of the electroencephalogram (EEG) over the visual cortex. While EEG responses with random temporal frequencies (m-sequences) have been studied, the underlying mechanisms that shape these responses are not fully understood. We analyze our new EEG data from a controlled experiment with m-sequence inputs and model the EEG using statistical time series models: an autoregressive (AR) model, adding exogenous input to AR (ARX), adding moving average terms (ARMAX), and finally adding a seasonality term (SARMAX). We implement computational methods to robustly handle model instabilities induced by this data, fitting these models with the Box-Jenkins methodology and assessing prediction accuracy for long periods of several seconds out-of-sample. We find in-sample fits are good in all models despite the complexities of the visual pathway, and that all models can predict aspects of EEG: including the distribution of point-wise values in time, the point-wise Pearsons correlation of EEG and model, and the frequency content. Surprisingly, we find little variation in the performance among these models, with the most sophisticated model (SARMAX) performing comparatively poorly in some instances. Our results suggest the simplest AR model is viable and can out perform more complicated models. Since these models are relatively simple and more transparent than contemporary models with numerous parameters, our study could inform future mechanistic studies of the temporal dynamics of human EEG responses to visual stimuli.

neuroscience↗

bioRxiv2024

Automatic detection of seizures within zebrafish larvae epilepsy models using single-channel electroencephalography

Researchers continue to pursue new drugs capable of treating intractable, or drug- resistant, epilepsy as a large number of patients do not see a reduction in the number of seizures from current treatments. To quicken the pace of drug research, zebrafish (Danio rerio) have been utilized as a model organism for testing anticonvulsant drugs before clinical trials. However, the lengthy task of labeling electroencephalography (EEG) data slows the pace of this line of research and limits its full potential. This study investigated the creation of automatic seizure detection algorithms for electroencephalogram data recorded from seizure-induced zebrafish to detect seizure, artifact, and neurotypical events. Four unique seizure detection algorithms were proposed and implemented based on k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN) classifiers. These four techniques were tested using the same input features and their results compared. The best-performing algorithm was identified as the Single Stage KNN with an 83.8% accuracy, followed by the Single Stage ANN with an 80.3% accuracy. The results indicate that a single-stage, multiclass classification architecture may be beneficial to automatically labeling epilepsy data, thus enhancing efficiency. Furthermore, the results for the algorithms which separate the multiclass classification into a series of binary classifications suggest that there are advantages in research and clinical settings to implement a detection algorithm that can delineate neurotypical and non-neurotypical data to assist with manual labeling.

bioengineering↗