CNN and Rf based Early Detection of Brain Stroke Using Bio-Electrical Signals

The brain is a vital component of the body that is in control of involuntary and voluntary movements such as walking,   memory, and vision. Nowadays, some of the most prevalent brain disorders include Alzheimer's disease, brain tumors, and epilepsy (paralysis or stroke).  As a result, stroke has become a significant global health concern, with high rates of mortality and disability. Importantly, approximately two-thirds of all strokes occur in developing countries, highlighting the significant burden of this condition in these regions. Therefore, emphasizing the timely detection and appropriate treatment of brain tumors is crucial. Given the high potential for mortality or severe disability associated with stroke disease, prioritizing active primary prevention and early identification of prognostic symptoms is of paramount importance. Ischemic stroke and hemorrhagic stroke are the two primary classifications for stroke diseases. Each type calls for specific emergency treatments, such as the administration of thrombolytics or coagulants, tailored to their respective underlying mechanisms. However, to effectively manage stroke, it is crucial to promptly identify the precursor symptoms in real-time, as they can vary among individuals. Timely professional treatment within the appropriate treatment window is essential and should be provided by a medical institution. In contrast, prior research has primarily centered around the formulation of acute treatment strategies or clinical guidelines subsequent to the occurrence of a stroke, rather than giving sufficient attention to the early identification of prognostic symptoms. Specifically, recent research has extensively utilized image analysis techniques, such as computed tomography (CT) or magnetic resonance imaging (MRI), as a primary approach for detecting and predicting prognostic symptoms in stroke patients. Traditional methodologies not only encounter difficulties in achieving early real-time diagnosis but also exhibit limitations in terms of prolonged testing duration and high testing costs. In this study, we introduce a novel system that employs machine learning techniques to predict and semantically interpret prognostic symptoms of stroke. Our approach utilizes real-time measurement of multi-modal bio-signals, namely electrocardiogram (ECG) and photoplethysmography (PPG), with a specific focus on the elderly population. To facilitate real-time prediction of stroke disease during walking, we have developed a stroke disease prediction system that incorporates a hybrid ensemble architecture. This architecture synergistically combines Convolutional Neural Network (CNN) and Random Forest (RF) models, enabling accurate and timely prognostication of stroke disease. The suggested method prioritises the convenience of use of bio-signal sensors for the elderly by collecting bio-signals from three electrodes placed on the index finger. These signals include ECG and PPG, and they are obtained while the participants walk. The CNN-RF model delivers satisfactory prediction accuracy when using raw ECG and PPG data. F1-Score, Sensitivity, Specificity, and Accuracy were the performance parameters used to evaluated the model's performance

ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset.

Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions ( https://doi.org/10.5281/zenodo.7153326 ). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge ( https://www.isles-challenge.org/ ) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke

ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset

Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions (https://doi.org/10.5281/zenodo.7153326). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge (https://www.isles-challenge.org/) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke

AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning

Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution methods are used to obtain clinically interpretable perfusion parameters that allow identifying brain tissue abnormalities. Deconvolution methods require the selection of two reference vascular functions as inputs to the model: the arterial input function (AIF) and the venous output function, with the AIF as the most critical model input. When manually performed, the vascular function selection is time demanding, suffers from poor reproducibility and is subject to the professionals' experience. This leads to potentially unreliable quantification of the penumbra and core lesions and, hence, might harm the treatment decision process. In this work we automatize the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable deep learning approach for estimating the vascular functions. Unlike previous methods using clustering or segmentation techniques to select vascular voxels, AIFNet is directly optimized at the vascular function estimation, which allows to better recognise the time-curve profiles. Validation on the public ISLES18 stroke database shows that AIFNet reaches inter-rater performance for the vascular function estimation and, subsequently, for the parameter maps and core lesion quantification obtained through deconvolution. We conclude that AIFNet has potential for clinical transfer and could be incorporated in perfusion deconvolution software.Comment: Preprint submitted to Elsevie

Emergency department crowding and its impact on the clinical care and mortality outcomes of stroke patients at the Tema General Hospital in Ghana

Stroke is a cardiovascular related disease that commonly presents at the emergency department(ED) with high mortality rates and lifelong disability. At the ED, overcrowding is a reportedchallenge that has impact on the outcome of patient care. The overall aim of the study was toevaluate the levels of overcrowding and predictors of mortality outcomes among stroke patientsat the ED of Tema General Hospital (TGH) in the Greater Accra Region of Ghana, a lowermiddle income country (LMIC) in sub-Saharan Africa (SSA). The study aimed to evaluate thecrowding status of the ED, stroke specific case fatality, stroke specific mortality by strokesubtype, association between CT scan use and stroke mortality, association between admissionBP levels and mortality, and to evaluate ED overcrowding and other predictors of strokespecific mortality.This was a facility-based retrospective study of prospectively collected secondary data alreadydocumented in the patients’ clinical records between October 2019 and March 2020.Participants were all patients aged 18 years and above who presented at the ED with any focalneurologic deficit suggestive of acute stroke (ischaemic, haemorrhagic, and transient ischaemicattack). The National Emergency Department Overcrowding Scale (NEDOCS) was thestandard metric used to assess the levels of crowding. The analysis was evaluated at the 95%confidence interval and a p-value of <0.05 was considered significant. The outcome variableof interest was stroke mortality.A total of 175 (89 males and 86 females) stroke patients visited the ED during the period ofdata collection. Only 70 (40.0%) stroke patients had a computer tomography (CT) scan doneduring admission at the ED. The ED was always overcrowded with the NEDOCS greater than100. There were 139 deaths representing a stroke specific mortality rate of 79.4%. Overall,there were 104 (59.4%) ischaemic strokes of which 78 (75.0%) died, and there were 71 (40.6%)haemorrhagic strokes of which 61 (85.7%) died at the ED. There were three statisticallyxxvsignificant stroke predictors; average NEDOCS (AOR = 1.033; 95% C1: 1.003 – 1.064; p =0.033), type of stroke (haemorrhagic stroke) (AOR = 3.834; 95% CI: 1.184 – 12.416; p = 0.025)and a medical history of diabetes mellitus (AOR = 3.001; 95% CI: 1.006 – 8.951; p = 0.049).In conclusion, in-patient stroke case fatality was extremely high and stroke mortality werehigher among younger patients and patients with haemorrhagic stroke. There is an urgent needto establish comprehensive stroke care systems at the ED to reduce stroke mortality, andpractical measures to improve the crowding situation at the ED are required