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General General

Identification and classification of epileptic EEG signals using invertible constant-Qtransform-based deep convolutional neural network.

In Journal of neural engineering ; h5-index 52.0

Context.Epilepsy is the most widespread disorder of the nervous system, affecting humans of all ages and races. The most common diagnostic test in epilepsy is the electroencephalography (EEG).Objective.In this paper, a novel automated deep learning approach based on integrating a pre-trained convolutional neural network (CNN) structure, called AlexNet, with the constant-Qnon-stationary Gabor transform (CQ-NSGT) algorithm is proposed for classifying seizure versus seizure-free EEG records.Approach.The CQ-NSGT method is introduced to transform the input 1D EEG signal into 2D spectrogram which is sent to the AlexNet CNN model. The AlexNet architecture is utilized to capture the discriminating features of the 2D image corresponding to each EEG signal in order to distinguish seizure and non-seizure subjects using multi-layer perceptron algorithm.Main results. The robustness of the introduced CQ-NSGT technique in transforming the 1D EEG signals into 2D spectrograms is assessed by comparing its classification results with the continuous wavelet transform method, and the results elucidate the high performance of the CQ-NSGT technique. The suggested epileptic seizure classification framework is investigated with clinical EEG data acquired from the Bonn University database, and the experimental results reveal the superior performance of the proposed framework over other state-of-the-art approaches with an accuracy of 99.56%, sensitivity of 99.12%, specificity of 99.67%, and precision of 98.69%.Significance.This elucidates the importance of the proposed automated system in helping neurologists to accurately interpret and classify epileptic EEG records without necessitating tedious visual inspection or massive data analysis for long-term EEG signals.

Eltrass Ahmed S, Tayel Mazhar B, El-Qady Ahmed F

2022-Dec-15

Constant-Q Non-Stationary Gabor Transform (CQ-NSGT), convolutional neural network (CNN), deep learning (DL), electroencephalography (EEG)

General General

Characterizing physiological high-frequency oscillations using deep learning.

In Journal of neural engineering ; h5-index 52.0

Objective.Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation of HFOs. The present study aimed to characterize salient features of physiological HFOs using deep learning (DL).Approach.We studied children with neocortical epilepsy who underwent intracranial strip/grid evaluation. Time-series EEG data were transformed into DL training inputs. The eloquent cortex (EC) was defined by functional cortical mapping and used as a DL label. Morphological characteristics of HFOs obtained from EC (ecHFOs) were distilled and interpreted through a novel weakly supervised DL model.Main results.A total of 63 379 interictal intracranially-recorded HFOs from 18 children were analyzed. The ecHFOs had lower amplitude throughout the 80-500 Hz frequency band around the HFO onset and also had a lower signal amplitude in the low frequency band throughout a one-second time window than non-ecHFOs, resembling a bell-shaped template in the time-frequency map. A minority of ecHFOs were HFOs with spikes (22.9%). Such morphological characteristics were confirmed to influence DL model prediction via perturbation analyses. Using the resection ratio (removed HFOs/detected HFOs) of non-ecHFOs, the prediction of postoperative seizure outcomes improved compared to using uncorrected HFOs (area under the ROC curve of 0.82, increased from 0.76).Significance.We characterized salient features of physiological HFOs using a DL algorithm. Our results suggested that this DL-based HFO classification, once trained, might help separate physiological from pathological HFOs, and efficiently guide surgical resection using HFOs.

Zhang Yipeng, Chung Hoyoung, Ngo Jacquline P, Monsoor Tonmoy, Hussain Shaun A, Matsumoto Joyce H, Walshaw Patricia D, Fallah Aria, Sim Myung Shin, Asano Eishi, Sankar Raman, Staba Richard J, Engel Jerome, Speier William, Roychowdhury Vwani, Nariai Hiroki

2022-Dec-07

HFO, machine learning, physiological HFO

General General

Clinical Significance of Stress Hyperglycemic Ratio and Glycemic Gap in Ischemic Stroke Patients Treated with Intravenous Thrombolysis.

In Clinical interventions in aging ; h5-index 53.0

OBJECTIVE : The clinical significance of different glycemic parameters has been rarely investigated in ischemic stroke patients treated with intravenous tissue plasminogen activator (IV tPA). This study was aimed to investigate the association between different glycemic parameters and favorable functional outcome in patients treated with IV tPA.

METHODS : Patients with ischemic stroke who received IV tPA therapy at our stroke center were retrospectively enrolled. Four glycemic parameters were collected including admission glucose, HbA1c, stress hyperglycemia ratio (SHR) and glycemic gap (GG). Additional information was also recorded including demographics, medical history, stroke severity, imaging measures and mRS score at discharge. We used 5 machine learning models to investigate the predictive value of glycemic parameters.

RESULTS : Our study included 294 patients treated with IV tPA. SHR and GG were independently associated with favorable functional outcome (adjusted OR for SHR 0.03, 95% CI 0.01-0.72, P = 0.03; adjusted OR for GG 1.024, 95% CI 1.00-1.05, P = 0.04).

CONCLUSION : SHR and GG were associated with functional outcomes in acute ischemic stroke patients with intravenous thrombolysis.

Li Guangshuo, Wang Chuanying, Wang Shang, Hao Yahui, Xiong Yunyun, Zhao Xingquan

2022

glucose, hyperglycemia, stroke, thrombolysis, tissue plasminogen activator

General General

Evaluation of shelter dog activity levels before and during COVID-19 using automated analysis.

In Applied animal behaviour science ; h5-index 32.0

Animal shelters have been found to represent stressful environments for pet dogs, both affecting behavior and influencing welfare. The current COVID-19 pandemic has brought to light new uncertainties in animal sheltering practices which may affect shelter dog behavior in unexpected ways. To evaluate this, we analyzed changes in dog activity levels before COVID-19 and during COVID-19 using an automated video analysis within a large, open-admission animal shelter in New York City, USA. Shelter dog activity was analyzed during two two-week long time periods: (i) just before COVID-19 safety measures were put in place (Feb 26-Mar 17, 2020) and (ii) during the COVID-19 quarantine (July 10-23, 2020). During these two periods, video clips of 15.3 second, on average, were taken of participating kennels every hour from approximately 8 am to 8 pm. Using a two-step filtering approach, a matched sample (based on the number of days of observation) of 34 dogs was defined, consisting of 17 dogs in each group (N1/N2 = 17). An automated video analysis of active/non-active behaviors was conducted and compared to manual coding of activity. The automated analysis validated by comparison to manual coding reaching above 79% accuracy. Significant differences in the patterns of shelter dog activity were observed: less activity was observed in the afternoons before COVID-19 restrictions, while during COVID-19, activity remained at a constant average. Together, these findings suggest that 1) COVID-19 lockdown altered shelter dog in-kennel activity, likely due to changes in the shelter environment and 2) automated analysis can be used as a hands-off tool to monitor activity. While this method of analysis presents immense opportunity for future research, we discuss the limitations of automated analysis and guidelines in the context of shelter dogs that can increase accuracy of detection, as well as reflect on policy changes that might be helpful in mediating canine stress in changing shelter environments.

Byosiere Sarah-Elizabeth, Feighelstein Marcelo, Wilson Kristiina, Abrams Jennifer, Elad Guy, Farhat Nareed, van der Linden Dirk, Kaplun Dmitrii, Sinitca Aleksandr, Zamansky Anna

2022-May

Applied behavior, COVID-19, Computer vision, Dog behavior, Machine learning, Shelter research

General General

Understanding Postpartum Parents' Experiences via Two Digital Platforms

ArXiv Preprint

Digital platforms, including online forums and helplines, have emerged as avenues of support for caregivers suffering from postpartum mental health distress. Understanding support seekers' experiences as shared on these platforms could provide crucial insight into caregivers' needs during this vulnerable time. In the current work, we provide a descriptive analysis of the concerns, psychological states, and motivations shared by healthy and distressed postpartum support seekers on two digital platforms, a one-on-one digital helpline and a publicly available online forum. Using a combination of human annotations, dictionary models and unsupervised techniques, we find stark differences between the experiences of distressed and healthy mothers. Distressed mothers described interpersonal problems and a lack of support, with 8.60% - 14.56% reporting severe symptoms including suicidal ideation. In contrast, the majority of healthy mothers described childcare issues, such as questions about breastfeeding or sleeping, and reported no severe mental health concerns. Across the two digital platforms, we found that distressed mothers shared similar content. However, the patterns of speech and affect shared by distressed mothers differed between the helpline vs. the online forum, suggesting the design of these platforms may shape meaningful measures of their support-seeking experiences. Our results provide new insight into the experiences of caregivers suffering from postpartum mental health distress. We conclude by discussing methodological considerations for understanding content shared by support seekers and design considerations for the next generation of support tools for postpartum parents.

Xuewen Yao, Miriam Mikhelson, Megan Micheletti, Eunsol Choi, S Craig Watkins, Edison Thomaz, Kaya De Barbaro

2022-12-22

General General

Superiorization-inspired unrolled SART algorithm with U-Net generated perturbations for sparse-view and limited-angle CT reconstruction.

In Physics in medicine and biology

Objective.Unrolled algorithms are a promising approach for reconstruction of CT images in challenging scenarios, such as low-dose, sparse-view and limited-angle imaging. In an unrolled algorithm, a fixed number of iterations of a reconstruction method are unrolled into multiple layers of a neural network, and interspersed with trainable layers. The entire network is then trained end-to-end in a supervised fashion, to learn an appropriate regularizer from training data. In this paper we propose a novel unrolled algorithm, and compare its performance with several other approaches on sparse-view and limited-angle CT.Approach.The proposed algorithm is inspired by the superiorization methodology, an optimization heuristic in which iterates of a feasibility-seeking method are perturbed between iterations, typically using descent directions of a model-based penalty function. Our algorithm instead uses a modified U-net architecture to introduce the perturbations, allowing a network to learn beneficial perturbations to the image at various stages of the reconstruction, based on the training data.Main Results.In several numerical experiments modeling sparse-view and limited angle CT scenarios, the algorithm provides excellent results. In particular, it outperforms several competing unrolled methods in limited-angle scenarios, while providing comparable or better performance on sparse-view scenarios.Significance.This work represents a first step towards exploiting the power of deep learning within the superiorization methodology. Additionally, it studies the effect of network architecture on the performance of unrolled methods, as well as the effectiveness of the unrolled approach on both limited-angle CT, where previous studies have primarily focused on the sparse-view and low-dose cases.

Jia Yiran, McMichael Noah, Mokarzel Pedro, Thompson Brandon, Si Dong, Humphries Thomas

2022-Dec-07

computed tomography, deep learning, iterative reconstruction, limited angle, sparse view