Brain stroke prediction using cnn python. The prediction model takes into account .

Brain stroke prediction using cnn python Mathew and P. -12(2018-22)TITLE-PRESENTED BY:BRAIN STROKE PREDICTION USING MACHINE LEARNING AND DEPLOYING USING FLASK1. Contribute to kishorgs/Brain-Stroke-Detection-Using-CNN development by creating an account on GitHub. The best algorithm for all classification processes is the convolutional neural network. The purpose of this paper is to develop an automated early ischemic stroke detection system using CNN deep learning algorithm. NUKAL Over the past few years, stroke has been among the top ten causes of death in Taiwan. - codexsys-7/Classifying-Brain-Tumor-Using-CNN Keywords: brain stroke, deep learning, machine learning, classification, segmentation, object detection. This is a deep learning model that detects brain stroke based on brain scans. It's a medical emergency; therefore getting help as soon as possible is critical. Reddy and Karthik Kovuri and J. BrainStroke: A Python-based project for real-time detection and analysis of stroke symptoms using machine learning algorithms. Sakthivel and Shiva Prasad Kaleru}, journal={2022 4th International Conference on Inventive Research in Computing This project is a Flask-based web application designed to predict the likelihood of a stroke in individuals using machine learning. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. The study shows how CNNs can be used to diagnose strokes. The goal of this is to use deep learning to detect whether there are initial signs of a brain stroke using CT or MRI images. Apr 21, 2023 · Peco602 / brain-stroke-detection-3d-cnn. Apr 27, 2023 · The proposed system uses an ensemble of machine learning algorithms like KNN, decision tree, random forest, SVM and CatBoost for classification. 9985596 Corpus ID: 255267780; Brain Stroke Prediction Using Deep Learning: A CNN Approach @article{Reddy2022BrainSP, title={Brain Stroke Prediction Using Deep Learning: A CNN Approach}, author={Madhavi K. We use prin- You signed in with another tab or window. [7] The title is "Machine Learning Techniques in Stroke Prediction: A Comprehensive Review" Saritha et al. • An administrator can establish a data set for pattern matching using the Data Dictionary. Seeking medical help right away can help prevent brain damage and other complications. We use Python thanks Anaconda Navigator that allow deploying isolated working environments. About. When the supply of blood and other nutrients to the brain is interrupted, symptoms Proposed system is an automation Stroke prediction and its stages using classification techniques CNN, Densenet and VGG16 Classifier to compare the performance of these above techniques based on their execution time A. Jun 24, 2022 · We are using Windows 10 as our main operating system. It takes different values such as Glucose, Age, Gender, BMI etc values as input and predict whether the person has risk of stroke or not. You signed out in another tab or window. calculated. pip The Brain Stroke Prediction project has the potential to significantly impact healthcare by aiding medical professionals in identifying individuals at high risk of stroke. This project aims to provide a interface for predicting brain tumors based on MRI scan images Jan 1, 2022 · Considering the above case, in this paper, we have proposed a Convolutional Neural Network (CNN) model as a solution that predicts the probability of stroke of a patient in an early stage to where P k, c is the prediction or probability of k-th model in class c, where c = {S t r o k e, N o n − S t r o k e}. It standardizes the brain stroke dataset and evaluates the performance of different classifiers. 82% testing accuracy using fine-tuned models for the correlation between stroke and ECG. com/detecting-brain-tumors-and-alzheimers-using-python/For 100+ More Python Pojects Ideas V Sep 15, 2022 · We set x and y variables to make predictions for stroke by taking x as stroke and y as data to be predicted for stroke against x. This project aims to detect brain tumors using Convolutional Neural Networks (CNN). [34] 2. Utilizes EEG signals and patient data for early diagnosis and intervention IndexTerms - Brain stroke detection; image preprocessing; convolutional neural network I. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. 60 % accuracy. In this project, we will attempt to classify stroke patients using a dataset provided on Kaggle: Kaggle Stroke Dataset. Symptoms may appear when the brain's blood flow and other nutrients are disrupted. Early intervention and preventive measures can be taken to reduce the likelihood of stroke occurrence, potentially saving lives and improving the quality of life for patients. Model Architecture Apr 22, 2023 · Stroke is a health ailment where the brain plasma blood vessel is ruptured, triggering impairment to the brain. Overview. Avanija and M. [5] as a technique for identifying brain stroke using an MRI. The model is trained on a dataset of brain MRI images, which are categorized into two classes: Healthy and Tumor. There is a collection of all sentimental words in the data dictionary. This project uses a CNN to detect brain strokes from CT scans, achieving over 97% accuracy. based on deep learning. INTRODUCTION Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. Demonstration application is under development. js for the frontend. Oct 1, 2023 · A brain stroke is a medical emergency that occurs when the blood supply to a part of the brain is disturbed or reduced, which causes the brain cells in that area to die. The main motivation of this paper is to demonstrate how ML may be used to forecast the onset of a brain stroke. com. The World Health Organization (WHO) defines stroke as “rapidly developing clinical signs May 15, 2024 · Brain stroke detection using deep convolutional neural network (CNN) models such as VGG16, ResNet50, and DenseNet121 is successfully accomplished by presenting a framework and fundamental principles. The main objective of this study is to forecast the possibility of a brain stroke occurring at This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. BRAIN STROKE PREDICTION BY USING MACHINE LEARNING S. Developed using libraries of Python and Decision Tree Algorithm of Machine learning. In addition, we compared the CNN used with the results of other studies. Given the rising prevalence of strokes, it is critical to understand the many factors that contribute to these occurrences. The trained model weights are saved for future use. This project develops a Convolutional Neural Network (CNN) model to classify brain tumor images from MRI scans. 60%. Brain Tumor Classification with CNN. The input variables are both numerical and categorical and will be explained below. May not generalize to other datasets. Bosubabu,S. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. A Convolutional Neural Network (CNN) is used to perform stroke detection on the CT scan image dataset. Contribute to Chando0185/Brain_Stroke_Prediction development by creating an account on GitHub. The goal is to build a reliable model that can assist in diagnosing brain tumors from MRI scans. g. Abstract—Stroke segmentation plays a crucial role in the diagnosis and treatment of stroke patients by providing spatial information about affected brain regions and the extent of damage. Stacking. ly/47CJxIr(or)To buy this proje Brain strokes are a leading cause of disability and death worldwide. Vasavi,M. Nov 19, 2024 · Welcome to the ultimate guide on Brain Stroke Prediction Using Python & Machine Learning ! In this video, we'll walk you through the entire process of making Stroke is a disease that affects the arteries leading to and within the brain. This is our final year research based project using machine learning algorithms . No use of XAI: Brain MRI images: 2023: TECNN: 96. Jul 24, 2024 · The aim of the study is to develop a reliable and efficient brain stroke prediction system capable of accurately predicting brain stroke. It is now a day a leading cause of death all over the world. Reddy and others published Brain Stroke Prediction Using Deep Learning: A CNN Approach | Find, read and cite all the research you need on ResearchGate Brain Tumor Detection using Web App (Flask) that can classify if patient has brain tumor or not based on uploaded MRI image. The dataset consists of over $5000$ individuals and $10$ different input variables that we will use to predict the risk of stroke. Explore and run machine learning code with Kaggle Notebooks | Using data from National Health and Nutrition Examination Survey Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. This code is implementation for the - A. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction May 23, 2024 · Ensemble Learning-based Brain Stroke Prediction Model Using Magnetic Resonance Imaging A python web application was created to demonstrate the results of CNN model classification using cloud Brain Stroke Prediction is an AI tool using machine learning to predict the likelihood of a person suffering from a stroke by analyzing medical history, lifestyle, and other relevant data. In [17], stroke prediction was made using different Artificial Intelligence methods over the Cardiovascular Health Study (CHS) dataset. Reload to refresh your session. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification algorithms like K-Nearest Neighbors and logistic regression. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. The underlying model was built with a Convolutional Neural Network using the Xception architecture. The project utilizes a dataset of MRI images and integrates advanced ML techniques with deep learning to achieve accurate tumor detection. Jun 4, 2022 · Major project-Batch No. 3. Brain stroke MRI pictures might be separated into normal and abnormal images Nov 22, 2024 · Stroke is a serious medical condition that can result in death as it causes a sudden loss of blood supply to large portions of brain. To gauge the effectiveness of the algorithm, a reliable dataset for stroke prediction was taken from the Kaggle website. integrated wavelet entropy-based spider web plots and probabilistic neural networks to classify brain MRI, which were normal brain, stroke, degenerative disease, infectious disease, and brain tumor in their study. The foundational framework for this implementation is a Convolutional Neural Network (CNN), implemented using the Python Jun 10, 2024 · Brain Stroke Detection System based on CT images using Deep Learning | Python IEEE Project 2024 - 2025. The project aims to create a user-friendly application with a frontend in Python and backend in MySQL to analyze stroke data and provide risk predictions. Using CT or MRI scan pictures, a classifier can predict brain stroke. Nowadays, it is a very common disease and the number of patients who attack by brain stroke is skyrocketed. Bayesian Rule Lists are proposed to generate rules to predict stroke risk using decision lists. The model has been trained using a comprehensive dataset and has shown promising results in accurately predicting the likelihood of a brain stroke. First, in the pre-processing stage, they used two dimensional (2D) discrete wavelet transform (DWT) for brain images. It features a React. The suggested method uses a Convolutional neural network to classify brain stroke images into normal and pathological categories. Stroke symptoms belong to an emergency condition, the sooner the patient is treated, the more chance the patient recovers. Keywords - Machine learning, Brain Stroke. Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. - Akshit1406/Brain-Stroke-Prediction Feb 11, 2022 · In this article you will learn how to build a stroke prediction web app using python and flask. To the best of our knowledge there is no detailed review about the application of ML for brain stroke. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome the traditional bagging technique in predicting brain stroke with more than 96% accuracy. Padmavathi,P. python database analysis pandas sqlite3 brain-stroke. You switched accounts on another tab or window. In this model, the goal is to create a deep learning Jan 20, 2023 · Early detection of the numerous stroke warning symptoms can lessen the stroke's severity. • To investigate, evaluate, and categorize research on brain stroke using CT or MRI scans. Using CNN and deep learning models, this study seeks to diagnose brain stroke images. Using the publicly accessible stroke prediction dataset, the study measured four commonly used machine learning methods for predicting brain stroke recurrence, which are as follows: (i) Random forest (ii) Decision tree (iii) The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. deep-learning traffic-analysis cnn cnn-model brain-stroke-prediction detects-stroke This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Accuracy can be improved: 3. Therefore, the aim of A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. Optimised configurations are applied to each deep CNN model in order to meet the requirements of the brain stroke prediction challenge. Sep 21, 2022 · PDF | On Sep 21, 2022, Madhavi K. Nov 26, 2021 · The most common disease identified in the medical field is stroke, which is on the rise year after year. A Multi-Class Brain Tumor Classifier using Convolutional Neural Network with 99% Accuracy achieved by applying the method of Transfer Learning using Python and Pytorch Deep Learning Framework deep-learning cnn torch pytorch neural-networks classification accuracy resnet transfer-learning brain resnet-50 transferlearning cnn-classification brain Nov 1, 2022 · In our experiment, another deep learning approach, the convolutional neural network (CNN) is implemented for the prediction of stroke. 2D CNNs are commonly used to process both grayscale (1 channel) and RGB images (3 channels), while a 3D CNN represents the 3D equivalent since it takes as input a 3D volume or a sequence of 2D frames, e. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. By implementing a structured roadmap, addressing challenges, and continually refining our approach, we achieved promising results that could aid in early stroke detection. To address challenges in diagnosing brain tumours and predicting the likelihood of strokes, this work developed a machine learning-based automated system that can uniquely identify, detect, and classify brain tumours and predict the occurrence of strokes using relevant features. Jun 12, 2024 · This code provides the Matlab implementation that detects the brain tumor region and also classify the tumor as benign and malignant. Jan 10, 2025 · In , differentiation between a sound brain, an ischemic stroke, and a hemorrhagic stroke is done by the categorization of stroke from CT scans and is facilitated by the authors using an IoT platform. The proposed methodology is to classify brain stroke MRI images into normal and abnormal images and delineate abnormal regions using semantic segmentation [4]. Python 3. The model predicts the presence of glioma tumor, meningioma tumor, pituitary tumor, or detects cases with no tumor. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such Oct 30, 2024 · 2. This enhancement shows the effectiveness of PCA in optimizing the feature selection process, leading to significantly better performance compared to the initial accuracy of 61. Collection Datasets We are going to collect datasets for the prediction from the kaggle. 27% uisng GA algorithm and it out perform paper result 96. 9. According to the WHO, stroke is the 2nd leading cause of death worldwide. The API can be integrated seamlessly into existing healthcare systems, facilitating convenient and efficient stroke risk assessment. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. No Stroke Risk Diagnosed: The user will learn about the results of the web application's input data through our web application. Jan 31, 2025 · Early brain stroke detection using a CNN-based ResNet harnesses deep learning's power for intricate feature extraction from medical images, vital for spotting subtle stroke indications early. The random forest classifier provided the highest accuracy among the models for detecting brain stroke. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. ly/3XUthAF(or)To buy this proj Deployment and API: The stroke prediction model is deployed as an easy-to-use API, allowing users to input relevant health data and obtain real-time stroke risk predictions. However, they used other biological signals that are not The script loads the dataset, preprocesses the images, and trains the CNN model using PyTorch. - rchirag101/BrainTumorDetectionFlask stroke prediction. For this reason, it is necessary and important for the health field to be handled with many perspectives, such as preventive, detective, manager and supervisory for artificial intelligence solutions for the development of value-added ideas and Sep 21, 2022 · DOI: 10. I. The proposed architectures were InceptionV3, Vgg-16, MobileNet, ResNet50, Xception and VGG19. Globally, 3% of the population are affected by subarachnoid hemorrhage… Nov 21, 2024 · This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. Deep learning is capable of constructing a nonlinear Oct 11, 2023 · Using magnetic resonance imaging of ischemic and hemorrhagic stroke patients, we developed and trained a VGG-16 convolutional neural network (CNN) to predict functional outcomes after 28-day Nov 26, 2021 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. The followed approach is based on the usage of a 3D Convolutional Neural Network (CNN) in place of a standard 2D one. In conclusion, these results underscore the significance of employing appropriate sampling and imputation strategies to improve the accuracy and dependability of stroke prediction models within clinical contexts. Evaluating Real Brain Images: After training, users can evaluate the model's performance on real brain images using the preprocess_and_evaluate_real_images function. It's much more monumental to diagnostic the brain stroke or not for doctor, but the main This study explores the application of deep learning techniques in the classification of computerized brain MRI images to distinguish various stages of Alzheimer's disease. 928: Early detection of post-stroke pneumonia will help to provide necessary treatment and to avoid severe outcomes. By using four Pre–trained models such as ResNet-50, Vision Transformer (Vit), MobileNetV2 and VGG-19, we obtained our desired results. Stroke is the leading cause of bereavement and disability Jun 22, 2021 · In another study, Xie et al. The administrator will carry out this procedure. application of ML-based methods in brain stroke. Introduction. ipynb contains the model experiments. [35] 2. It discusses scoring metrics like CHADS2 that evaluate risk factors such as heart failure, hypertension, age, and previous strokes. CNN achieved 100% accuracy. In our configuration, the number of hidden layers is four while the first two layers are convolutional layers and the last two layers are linear layers, the hyperparameters of the CNN model is given in Table 4 . The improved model, which uses PCA instead of the genetic algorithm (GA) previously mentioned, achieved an accuracy of 97. III. "No Stroke Risk Diagnosed" will be the result for "No Stroke". 01 %: 1. The TensorFlow model includes 3 convolutional layers and dropout for regularization, with performance measured by accuracy, ROC curves, and confusion matrices Using a deep learning model on a brain disease dataset, this method of predicting analytical techniques for stroke was carried out. Code Brain stroke prediction using machine learning. Mar 15, 2024 · SLIDESMANIA ConcluSion Findings: Through the use of AI and machine learning algorithms, we have successfully developed a brain stroke prediction model. EDUPALLI LIKITH KUMAR2. , attention based GRU) 13,930: EHR data: within 7 days of post-stroke by GRU: AUC= 0. Heart abnormalities detected by electrocardiogram (ECG) might provide diagnostic indicators for brain dysfunctions such as stroke. js frontend for image uploads and a FastAPI backend for processing. This project provides a comprehensive comparison between SVM and CNN models for brain stroke detection, highlighting the strengths of CNN in handling complex image data. SaiRohit Abstract A stroke is a medical condition in which poor blood flow to the brain results in cell death. 1. They have used a decision tree algorithm for the feature selection process, a PCA Jun 7, 2022 · For Free Project Document PPT Download Visithttps://nevonprojects. Brain stroke has been the subject of very few studies. 2022. A strong prediction framework must be developed to identify a person's risk for stroke. No use of XAI: Brain MRI images: 2023: CNN with GNN: 95. proposed CNN-based DenseNet for stroke disease classification and prediction based on ECG data collected using 12 leads, and they obtained 99. Mar 15, 2024 · It used a random forest algorithm trained on a dataset of patient attributes. ResNet's residual connections aid in training deeper layers effectively, improving model performance by capturing complex spatial relationships. Jul 28, 2020 · Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. Stroke is a medical emergency in which poor blood flow to the brain causes cell death. Aswini,P. A Flask web application focused on detecting various types of brain tumors using Head MRI Scan images. would have a major risk factors of a Brain Stroke. The effectiveness of several machine learning (ML Stroke Risk Prediction Using Machine Learning Algorithms Rishabh Gurjar 1 , Sahana H K 1 , Neelambika C 1 , Sparsha B Sathish 1 , Ramys S 2 1 Department of Computer Science and Engineering. Brain Stroke Prediction is an AI tool using machine learning to predict the likelihood of a person suffering from a stroke by analyzing medical history, lifestyle, and other relevant data. Jan 14, 2025 · A digital twin is a virtual model of a real-world system that updates in real-time. The Jupyter notebook notebook. GridDB. To implement a brain stroke system using SVM (Support Vector Machine) and ML algorithms (Random Forest, Decision tree, Logistic Regression, KNN) for more accurate result. By using a collection of brain imaging scans to train CNN models, the authors are able to accurately distinguish between hemorrhagic and ischemic strokes. Early prediction of stroke risk can help in taking preventive measures. In this paper, we attempt to bridge this gap by providing a systematic analysis of the various patient records for the purpose of stroke prediction. High model complexity may hinder practical deployment. A Machine Learning Model to Predict a Diagnosis of Brain Stroke | Python IEEE Final Year Project 2024. 🛒Buy Link: https://bit. Sep 1, 2019 · Deep learning and CNN were suggested by Gaidhani et al. Anto, "Tumor detection and classification of MRI brain image using wavelet transform and SVM", 2017 International Conference on Signal Processing and Communic… Aug 5, 2022 · In this video,Im implemented some practical way of machine learning model development approaches with brain stroke prediction data👥For Collab, Sponsors & Pr This repository contains the code and documentation for a project focused on the early detection of brain tumors using machine learning (ML) algorithms and convolutional neural networks (CNNs). Several risk factors believe to be related to Dec 1, 2021 · This document summarizes different methods for predicting stroke risk using a patient's historical medical information. Star 4. There have lots of reasons for brain stroke, for instance, unusual blood circulation across the brain. x = df. No use of XAI: Brain MRI Jan 24, 2023 · This section demonstrates the results of using CNN to classify brain strokes using different estimation parameters such as accuracy, recall accuracy, F-score, and we use a mixing matrix to show true positive, true negative, false positive, and false negative values. We use GridDB as our main database that stores the data used in the machine learning model. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. 99% training accuracy and 85. Oct 27, 2020 · The brain is an energy-consuming organ that heavily relies on the heart for energy supply. In theSection 2, we review some literature about ML and brain stroke field whereas, Section 3 presents the study design and selection, search strategy, and categorization of the Brain Tumor Detection Using CNN with Python Tensorflow Sklearn OpenCV Part1 Data Processing with CV2:1- Download the data2- Convert the images to grayscale3- Created a Web Application using Streamlit and Machine learning models on Stroke prediciton Whether the paitent gets a stroke or not on the basis of the feature columns given in the dataset This Streamlit web app built on the Stroke Prediction dataset from Kaggle aims to provide a user-friendly Oct 1, 2022 · One of the main purposes of artificial intelligence studies is to protect, monitor and improve the physical and psychological health of people [1]. Since the dataset is small, the training of the entire neural network would not provide good results so the concept of Transfer Learning is used to train the model to get more accurate resul So, we have developed a model to predict whether a person is affected with brain stroke or not. 75 %: 1. INTRODUCTION In most countries, stroke is one of the leading causes of death. 1109/ICIRCA54612. slices in a CT scan. so, on top of this we have also created a Front End framework with Tkinter GUI where we can input the image and the model will try to predict the output and display it on the window. The model uses various health-related inputs such as age, gender, blood glucose level, BMI, and lifestyle factors like smoking status and work type to predict stroke Oct 1, 2020 · Prediction of post-stroke pneumonia in the stroke population in China [26] LR, SVM, XGBoost, MLP and RNN (i. . e. Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. The prediction model takes into account The situation when the blood circulation of some areas of brain cut of is known as brain stroke. drop(['stroke'], axis=1) y = df['stroke'] 12. The model achieved promising results in accurately predicting the likelihood of stroke. Brain stroke, also known as a cerebrovascular accident, is a critical medical condition that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from receiving oxygen and Nov 22, 2024 · This is evidenced by its elevated F1-score and AUC values, indicative of its strong performance in stroke prediction. Accuracy can be improved 3. Dec 6, 2024 · In this work, brain tumour detection and stroke prediction are studied by applying techniques of machine learning. 8: Prediction of final lesion in Explore and run machine learning code with Kaggle Notebooks | Using data from brain_stroke Jul 1, 2023 · The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. The basic requirements you will need is basic knowledge on Html, CSS, Python and Functions in python. 3. Segmenting stroke lesions accurately is a challeng-ing task, given that conventional manual techniques are time-consuming and prone to errors. If the user is at risk for a brain stroke, the model will predict the outcome based on that risk, and vice versa if they do not. This project focuses on building a Brain Stroke Prediction System using Machine Learning algorithms, Flask for backend API development, and React. Ischemic Stroke, transient ischemic attack. Diagnosis of brain diseases by ECG requires proficient domain knowledge, which is both time and labor consuming. Jupyter Notebook is used as our main computing platform to execute Python cells. ybwlc iakx mgue uqc oxfpl rusqt vixhy jitnv nrhpd tuszmy xur bqi ztwnxx gevbaq njrdzwl