Analysis of electrode arrangements for brain stroke diagnosis via electrical impedance tomography through numerical computational models

Objective.Rapid stroke-type classification is crucial for improved prognosis. However, current methods for classification are time-consuming, require expensive equipment, and can only be used in the hospital. One method that has demonstrated promise in a rapid, low-cost, non-invasive approach to stroke diagnosis is electrical impedance tomography (EIT). While EIT for stroke diagnosis has been the topic of several studies in recent years, to date, the impact of electrode placements and arrangements has rarely been analyzed or tested and only in limited scenarios. Optimizing the location and choice of electrodes can have the potential to improve performance and reduce hardware cost and complexity and, most importantly, diagnosis time.Approach.In this study, we analyzed the impact of electrodes in realistic numerical models by (1) investigating the effect of individual electrodes on the resulting simulated EIT boundary measurements and (2) testing the performance of different electrode arrangements using a machine learning classification model.Main results.We found that, as expected, the electrodes deemed most significant in detecting stroke depend on the location of the electrode relative to the stroke lesion, as well as the role of the electrode. Despite this dependence, there are notable electrodes used in the models that are consistently considered to be the most significant across the various stroke lesion locations and various head models. Moreover, we demonstrate that a reduction in the number of electrodes used for the EIT measurements is possible, given that the electrodes are approximately evenly distributed.Significance.In this way, electrode arrangement and location are important variables to consider when improving stroke diagnosis methods using EIT.

Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models

Electrical impedance tomography (EIT) may have potential to overcome existing limitations in stroke differentiation, enabling low-cost, rapid, and mobile data collection. Combining bioimpedance measurement technologies such as EIT with machine learning classifiers to support decision-making can avoid commonly faced reconstruction challenges due to the nonlinear and ill-posed nature of EIT imaging. Therefore, in this work, we advance this field through a study integrating realistic head models with clinically relevant test scenarios, and a robust architecture consisting of nested cross-validation and principal component analysis. Specifically, realistic head models are designed which incorporate the highly conductive layers of cerebrospinal fluid in the subarachnoid space and ventricles. In total, 135 unique models are created to represent a large patient population, with normal, haemorrhagic, and ischemic brains. Simulated EIT voltage data generated from these models are used to assess the classification performance of support vector machines. Parameters explored include driving frequency, signal-to-noise ratio, kernel function, and composition of binary classes. Classifier accuracy at 60 dB signal-to-noise ratio, reported as mean and standard deviation, are (79.92% ± 10.82%) for lesion differentiation, (74.78% ± 3.79%) for lesion detection, (77.49% ± 15.90%) for bleed detection, and (60.31% ± 3.98%) for ischemia detection (after ruling out bleed). The results for each method were obtained with statistics from 3 independent runs with 17,280 observations, polynomial kernel functions, and feature reduction of 76% by PCA (from 208 to 50 features). While results of this study show promise for stroke differentiation using EIT data, our findings indicate that the achievable accuracy is highly dependent on the classification scenario and application-specific classifiers may be necessary to achieve acceptable accuracy.

Exploration of the etiology of single small subcortical infarctions using high-resolution vessel wall MRI

Objective

We aimed to explore imaging indicators for diagnosing the etiology of single small subcortical infarctions (SSI) using high-resolution vessel wall imaging (HR-VWI).

Methods

Patients with acute isolated subcortical cerebral infarction were prospectively enrolled and classified as having large artery atherosclerosis (LAA), stroke of undetermined etiology (SUD), or small artery disease (SAD). The infarct information, the cerebral small vessel disease (CSVD) score, morphological characteristics of the lenticulostriate arteries (LSAs), and plaque characteristics were compared between the three groups.

Results

Seventy seven patients were enrolled (30 LAA, 28 SUD, and 19 SAD). The total CSVD score of the LAA (P = 0.001) and SUD groups (P = 0.017) was significantly lower than that of the SAD group. The number and total length of LSA branches in the LAA and SUD groups were shorter than in the SAD group. Moreover, the total length laterality index (LI) of the LSAs in the LAA and SUD groups was greater than in the SAD group. The total CSVD score and LI of total length were independent predictors for the SUD and LAA groups. The remodeling index of the SUD group was significantly higher than that of the LAA group (P = 0.002); positive remodeling was dominant in the SUD group (60.7%), whereas remodeling in the LAA group was primarily non-positive (83.3%).

Conclusions

SSI with and without plaques on the carrier artery may have different modes of pathogenesis. Patients with plaques may also have a coexisting mechanism of atherosclerosis.

Risk Factors, Clinical Features and Prognosis for Subtypes of Ischemic Stroke in a Chinese Population

It is unanimously accepted that stroke is a highly heterogeneous disorder. Different subtypes of ischemic stroke may have different risk factors, clinical features, and prognoses. The aim of this study was to evaluate the risk factors, clinical characteristics, and prognoses of different subtypes of ischemic stroke defined by the Trial of ORG10172 in Acute Stroke Treatment (TOAST) criteria. We prospectively analyzed the data from 530 consecutive patients who were admitted to our hospital with acute ischemic stroke within 7 days of stroke onset during the study period. Standardized data assessment was used and the cause of ischemic stroke was classified according to the TOAST criteria. Patients were followed up till 30 and 90 days after stroke onset. It was found that large-artery atherosclerosis was the most frequent etiology of stroke (37.4%), and showed the highest male preponderance, the highest prevalence of previous transient ischemic attack, and the longest hospital stay among all subtypes. Small artery disease (36.4%) was associated with higher body mass index, higher plasma triglycerides, and lower plasma high-density lipoprotein cholesterol than cardioembolism. Cardioembolism (7.7%), which was particularly common in the elderly (i.e., individuals aged 65 years and older), showed the highest female preponderance, the highest prevalence of atrial fibrillation, the earliest presentation to hospital after stroke onset, the most severe symptoms on admission, the maximum complications associated with an adverse outcome, and the highest rate of stroke recurrence and mortality. Our results suggest that ischemic stroke should be regarded as a highly heterogeneous disorder. Studies involving risk factors, clinical features, and prognoses of ischemic stroke should differentiate between etiologic stroke subtypes.

Mexican-National Institute of Neurology and Neurosurgery-Stroke Registry: Results of a 25-Year Hospital-Based Study

Background and purpose

Stroke has been scarcely studied in Latin America (LA). The Mexican Institute of Neurology Stroke Registry was established in 1990 as a prospective computer-based database to register data obtained from patients admitted with stroke. Using this data, we attempted to define the profile of risk factors and outcomes.

Methods

The demographic data, stroke description, ancillary tests, vascular risk factors, and modified Rankin scale (mRs) were registered. Ischemic stroke subtyping was based on the Trial of Org 10,172 of the Acute Stroke Treatment classification. We followed-up patients using multiple overlapping methods. Primary outcomes included mRs, recurrence, and death at 30 days and at the end of follow-up.

Results

We included 4,481 patients with a median follow-up of 27 months, (17,281 person-years follow-up). The mean age was 52.8 ± 18 years. There were 2,229 males (50%) included in the study. CI was present in 64.9%, intracerebral hemorrhage (ICH) in 25.6%, and cerebral venous thrombosis (CVT) in 6.3%. Hypertension was the major risk factor (46.5%). The most common cause of CI was atherosclerosis (27%). ICH was mainly hypertensive (58%), and 60% of CVT were puerperal. Overall, the mortality rate was 24.5%. The recurrence rate was 16.9%. Poor outcome (mRs ≥ 3) was found in 56.2% of patients. The best outcomes were observed in CVT patients (74.5% mRs ≤ 2), whereas 72.1% ICH patients had mRs ≥3.

Conclusion

This is one of the largest hospital-based registries in LA and shows significant differences with other previously published registries, including a younger age, relatively less hypertension, and larger proportion of CVT. Poor functional outcome was common. This study adds to the understanding of geographic differences in stroke characteristics and outcomes.

Stroke localization and classification using microwave tomography with k-means clustering and support vector machine

For any chance for stroke patients to survive, the stroke type should be classified to enable giving medication within a few hours of the onset of symptoms. In this paper, a microwave-based stroke localization and classification framework is proposed. It is based on microwave tomography, k-means clustering, and a support vector machine (SVM) method. The dielectric profile of the brain is first calculated using the Born iterative method, whereas the amplitude of the dielectric profile is then taken as the input to k-means clustering. The cluster is selected as the feature vector for constructing and testing the SVM. A database of MRI-derived realistic head phantoms at different signal-to-noise ratios is used in the classification procedure. The performance of the proposed framework is evaluated using the receiver operating characteristic (ROC) curve. The results based on a two-dimensional framework show that 88% classification accuracy, with a sensitivity of 91% and a specificity of 87%, can be achieved. Bioelectromagnetics. 39:312-324, 2018. © 2018 Wiley Periodicals, Inc.

Five-year survival, disability, and recurrence after first-ever stroke in a middle-income country: A population-based study in Joinvile, Brazil

Background Information about long-term outcomes after stroke in developing countries provided by population-based methodologies is scarce. Aim This study aimed to know outcomes five years after a first-ever stroke in Joinville, Brazil. Methods Data were extracted from the Joinville Stroke Registry about all patients who had strokes in Joinville in 2010 and were followed up to 2015. Stroke recurrence, Kaplan-Meier survival probabilities, functional outcomes, and causes of death were ascertained at 30 days, six months, one and five years. Results A total of 399 strokes were studied. The mean age was 64 (standard deviation 16) years. After five years, 52% (95% confidence interval: 47-57%) survived and 20% (95% confidence interval: 15-26%) of the survivors had modified Rankin scale scores >2. More than half of these patients were institutionalized in nursing or home care settings. The average risk of death per year was ≈7%. Survival rates were significantly lower for subarachnoid hemorrhage and primary intracerebral hemorrhage than for ischemic stroke. The five-year recurrence rate was 12% (95% confidence interval: 9-15%). The index stroke was the cause of death in three quarters of the patients. Conclusions The results showed that 68% of the patients with stroke were either dead or disabled five years after first-ever stroke. This percentage is similar to proportions of other recent cohorts from developed countries, despite the lower age of the patients in this study.

Epidemiology of Stroke in Costa Rica: A 7-Year Hospital-Based Acute Stroke Registry of 1319 Consecutive Patients

BACKGROUND

Limited data on stroke exist for Costa Rica. Therefore, we created a stroke registry out of patients with stroke seen in the Acute Stroke Unit of the Hospital Calderon Guardia.

METHODS

We analyzed 1319 patients enrolled over a 7-year period, which incorporated demographic, clinical, laboratory, and neuroimaging data.

RESULTS

The mean age of patients with stroke was 68.0 ± 15.5 years. Seven hundred twenty-five were men and the age range was 13-104 years. The most prevalent risk factors were hypertension (78.8%), dyslipidemia (36.3%), and diabetes (31.9%). Fifteen percent had atrial fibrillation and 24.7% had a previous stroke or transient ischemic attack. Prevalence of hypertension and atrial fibrillation increased with age; however, younger patients were more associated with thrombophilia. We documented 962 (72.9%) ischemic and 270 (20.5%) hemorrhagic strokes. Of the ischemic strokes, 174 (18.1%) were considered secondary to large-artery atherothrombosis, 175 (18.2%) were due to cardiac embolism, 19 (2.0%) were due to lacunar infarcts, and 25 (2.6%) were due to other determined causes. Five hundred sixty-nine (59.1%) remained undetermined. Atherothrombotic strokes were mostly associated with dyslipidemia, diabetes, metabolic syndrome, and obesity, whereas lacunar infarcts were associated with hypertension, smoking, sedentary lifestyle, and previous stroke or transient ischemic attack. Of our patients, 69.9% scored between 0 and 9 in the initial National Institutes of Health Stroke Scale (NIHSS).

CONCLUSIONS

We found differences in sociodemographic features, risk factors, and stroke severity among stroke subtypes. Risk factor prevalence was similar to other registries involving Hispanic populations.

Antiplatelet therapy as a modulator of stroke aetiology: a meta-analysis

AIMS

Antiplatelet therapy reduces the incidence of ischaemic stroke. Platelet-mediated thrombosis contributes variably to the major subtypes of stroke as defined by the TOAST criteria: large artery atherosclerosis (LAA), cardioembolic (CE) and small vessel occlusion (SVO). The effect of antiplatelet therapy on the incidence of each subtype is unknown and is the subject of this meta-analysis.

METHODS

Electronic databases were searched for articles comparing the effect of antiplatelet therapy on the incidence of stroke according to aetiological subtype. Studies containing subjects prescribed anticoagulant therapy or solely investigating subjects with atrial fibrillation were excluded. Pooled odds ratios (ORs) were calculated using a fixed effects model.

RESULTS

Nine studies were included (n = 5739). In patients who had an ischaemic stroke, pre-event antiplatelet therapy was associated with significantly decreased incidence of LAA (OR 0.88, 95% CI 0.79, 0.99; P = 0.026), increased incidence of CE (OR 1.23, 95% CI 1.08, 1.41; P = 0.002) and no effect on SVO (OR 0.99, 95% CI 0.88, 1.11; P = 0.806). Concordant non-significant trends were observed in primary prevention populations (n = 751): LAA (OR 0.81, 95% CI 0.57, 1.15; P = 0.240), CE (OR 1.29, 95% CI 0.89, 1.87; P = 0.179) and SVO (OR 0.99, 95% CI 0.73, 1.36; P = 0.970). Subgroup analysis of aspirin monotherapy (n = 3786) demonstrated a significant reduction in LAA (OR 0.87, 95% CI 0.76, 1.00; P = 0.046), but non-significant effects on the incidence of CE (OR 1.17, 95% CI 0.99, 1.39; P = 0.068) and SVO (OR 1.04, 95% CI 0.91, 1.20; P = 0.570). Probability of publication bias was low (P > 0.05).

CONCLUSIONS

Antiplatelet therapy preferentially reduces the incidence of LAA stroke compared with CE and SVO subtypes.

Detecting atheromatous plaques in the aortic arch or supra-aortic arteries for more accurate stroke subtype classification

INTRODUCTION

To investigate the correlations of atheromatous plaques in the aortic arch or supra-aortic arteries with intracranial arterial stenosis and carotid plaques in stroke patients, and to determine whether taking these plaques into account will reduce the proportion of patients in the undetermined etiology group.

METHODS

We prospectively enrolled 308 ischemic stroke patients, whose clinical characteristics and A-S-C-O classifications were compared with analyses of intracranial arteries, carotid arteries, aortic arch, and supra-aortic arteries.

RESULTS

125(40.6%) patients had plaques in the aortic arch or supra-aortic arteries, of which 106 (84.8%) had complex plaques. No correlations were observed between these plaques and carotid plaques ( p = 0.283) or intracranial arterial stenosis ( p = 0.097). After detecting the mobile thrombi in the aortic arch and supra-aortic arteries, the proportion of patients in the atherothrombosis group was increased from 33.8% to 55.5% ( p = 0.00), whereas the proportion of patients in stroke of undetermined etiology group was decreased from 19.2% to 11.0% ( p = 0.00).

DISCUSSION

Examining only the carotid and intracranial arteries may not provide adequate information about large arteries in stroke patients. Therefore, it would be better to include a search for relevant plaques in the aortic arch or supra-aortic arteries in modern stroke workup, for it may lead to more accurate stroke subtype classification and guide secondary prevention.