Analysis and classification of heart diseases using heartbeat features and machine learning algorithms
TLDR
This study proposed an ECG (Electrocardiogram) classification approach using machine learning based on several ECG features that achieved an overall accuracy of 96.75% using GDB Tree algorithm and 97.98% using random Forest for binary classification.Abstract:
This study proposed an ECG (Electrocardiogram) classification approach using machine learning based on several ECG features. An electrocardiogram (ECG) is a signal that measures the electric activity of the heart. The proposed approach is implemented using ML-libs and Scala language on Apache Spark framework; MLlib is Apache Spark’s scalable machine learning library. The key challenge in ECG classification is to handle the irregularities in the ECG signals which is very important to detect the patient status. Therefore, we have proposed an efficient approach to classify ECG signals with high accuracy Each heartbeat is a combination of action impulse waveforms produced by different specialized cardiac heart tissues. Heartbeats classification faces some difficulties because these waveforms differ from person to another, they are described by some features. These features are the inputs of machine learning algorithm. In general, using Spark–Scala tools simplifies the usage of many algorithms such as machine-learning (ML) algorithms. On other hand, Spark–Scala is preferred to be used more than other tools when size of processing data is too large. In our case, we have used a dataset with 205,146 records to evaluate the performance of our approach. Machine learning libraries in Spark–Scala provide easy ways to implement many classification algorithms (Decision Tree, Random Forests, Gradient-Boosted Trees (GDB), etc.). The proposed method is evaluated and validated on baseline MIT-BIH Arrhythmia and MIT-BIH Supraventricular Arrhythmia database. The results show that our approach achieved an overall accuracy of 96.75% using GDB Tree algorithm and 97.98% using random Forest for binary classification. For multi class classification, it achieved to 98.03% accuracy using Random Forest, Gradient Boosting tree supports only binary classification.read more
Citations
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Journal ArticleDOI
Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks.
Ahmet Çinar,Seda Arslan Tuncer +1 more
TL;DR: The results show that with the proposed deep learning architecture, it classifies ECG signals with higher accuracy than conventional machine learning classifiers.
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One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments
Abstract: Cardiovascular diseases are considered the number one cause of death across the globe which can be primarily identified by the abnormal heart rhythms of the patients. By generating electrocardiogram (ECG) signals, wearable Internet of Things (IoT) devices can consistently track the patient’s heart rhythms. Although Cloud-based approaches for ECG analysis can achieve some levels of accuracy, they still have some limitations, such as high latency. Conversely, the Fog computing infrastructure is more powerful than edge devices but less capable than Cloud computing for executing compositionally intensive data analytic software. The Fog infrastructure can consist of Fog-based gateways directly connected with the wearable devices to offer many advanced benefits, including low latency and high quality of services. To address these issues, a modular one-dimensional convolution neural network (1D-CNN) approach is proposed in this work. The inference module of the proposed approach is deployable over the Fog infrastructure for analysing the ECG signals and initiating the emergency countermeasures within a minimum delay, whereas its training module is executable on the computationally enriched Cloud data centers. The proposed approach achieves the F1-measure score ≈1 on the MIT-BIH Arrhythmia database when applying GridSearch algorithm with the cross-validation method. This approach has also been implemented on a single-board computer and Google Colab-based hybrid Fog-Cloud infrastructure and embodied to a remote patient monitoring system that shows 25% improvement in the overall response time.
Journal ArticleDOI
An improved cardiac arrhythmia classification using an RR interval-based approach
TL;DR: An improved RR interval-based cardiac arrhythmia classification approach that is significantly better and more accurate than the other classifiers used in this method.
Journal ArticleDOI
An estimation of pressure rise and heat transfer rate for hybrid nanofluid with endoscopic effects and induced magnetic field: computational intelligence application
TL;DR: In this paper, the authors used feed-forward nonlinear input-output artificial neural network for the prediction of pressure rise and heat transfer coefficient in peristaltic pumping of Ree-Eyring hybrid nanofluid through an endoscope.
Journal ArticleDOI
Classification of ECG beats using optimized decision tree and adaptive boosted optimized decision tree
TL;DR: This work has proposed two classifiers for classifying six types of heartbeats and considers the instance where the uncertain data are characterized and accomplished over the evidence theory to deal with the data with uncertain class labels and attribute values.
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