Quantitative Evaluation of Task-Induced Neurological Outcome after Stroke

Abstract

Electroencephalography (EEG) can access ischemic stroke-derived cortical impairment and is believed to be a prospective predictive method for acute stroke prognostics, neurological outcome, and post-stroke rehabilitation management. This study aims to quantify EEG features to understand task-induced neurological declines due to stroke and evaluate the biomarkers to distinguish the ischemic stroke group and the healthy adult group. We investigated forty-eight stroke patients (average age 72.2 years, 62% male) admitted to the rehabilitation center and seventy-five healthy adults (average age 77 years, 31% male) with no history of known neurological diseases. EEG was recorded through frontal, central, temporal, and occipital cortical electrodes (Fz, C1, C2, T7, T8, Oz) using wireless EEG devices and a newly developed data acquisition platform within three months after the appearance of symptoms of ischemic stroke (clinically confirmed). Continuous EEG data were recorded during the consecutive resting, motor (walking and working activities), and cognitive reading tasks. The statistical results showed that alpha, theta, and delta activities are biomarkers classifying the stroke patients and the healthy adults in the motor and cognitive states. DAR and DTR of the stroke group differed significantly from those of the healthy control group during the resting, motor, and cognitive tasks. Using the machine-learning approach, the C5.0 model showed 78% accuracy for the resting state, 89% accuracy in the functional motor walking condition, 84% accuracy in the working condition, and 85% accuracy in the cognitive reading state for classification the stroke group and the control group. This study is expected to be helpful for post-stroke treatment and post-stroke recovery.

Treatment Efficacy Analysis in Acute Ischemic Stroke Patients Using In Silico Modeling Based on Machine Learning: A Proof-of-Principle

“…The area under the ROC curve (AUC) for the IAR, IVR, and NR models were 0.979, 0.976, and 0.957, respectively. While these additional metrics are provided in keeping with the conventions of other machine learning studies in the field of stroke medicine [16,18,19,22,23,38], it should be noted that their interpretation is complicated by the fact that voxel-based tissue outcome prediction is a highly imbalanced problem. That is to say, the vast majority of brain tissue voxels will not proceed to infarction and are easy to identify as such, leading to especially high values for accuracy, specificity, and the area under the ROC curve (AUC), which may not intuitively reflect the ability of the model to discriminate between healthy and ischemic voxels.…”

“…It has been reported that this correlation also holds true for follow-up lesions predicted from acute PWI data [49], raising the possibility that mRS scores could be predicted directly from acute image data and clinical information in future and used in the same manner as the prediction lesion volume for an in silico evaluation. As mRS is used as both a clinical and a functional outcome, an imaging-based approach to predicting mRS would likely benefit from the availability of information about the infarct location, which adds valuable context when interpreting the functional severity of stroke from the lesion volume [50], as well as the potential to add salient covariates [51] and additional neurological data [38] to the model. More recently, it has been suggested that the post-treatment NIHSS score may be a suitable alternative primary outcome measure to mRS for clinical trials of acute ischemic stroke [52].…”

Validation of Soft Multipin Dry EEG Electrodes

“…Each block started with a 10 s baseline. The power of the delta [1-4 Hz], theta [4][5][6][7][8], alpha [8][9][10][11][12][13][14], beta [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], and gamma [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] band were used to compare both systems in the frequency domain. Moreover, the topographical distribution of the alpha band was used to compare both systems in the spatial domain.…”

“…Electroencephalography (EEG) is an electrophysiological measurement technique that records the electrical activity of the cerebral cortex with a high temporal resolution, using non-invasive scalp electrodes [1]. In research, it is a widely used technique to investigate visual, auditory, and cognitive functioning, as well as to gain insights, for instance, into the cortical pathways and impairments involved in neurological (movement) disorders [2], such as Parkinson's disease [3] or stroke [4]. More recently, EEG has become an important tool in research on brain-computer interfaces [5,6].…”

Prediction of Myoelectric Biomarkers in Post-Stroke Gait

“…Functional motor and cognitive deficits are usual and persistent consequences of stroke and a significant factor responsible for physical dysfunction, slow physiological recovery, and a worse post-stroke lifestyle [ 10 ]. Conventional mental and neurological evaluations cannot be conducted immediately after stroke due to the medical aspects (e.g., fluctuating levels of arousal, pain, confusion, tiredness) and functional obstacles (e.g., sensory, linguistic, motor shortfalls) that hamper the patients’ capability to approach physiological examinations [ 11 ].…”