AI Summary & Key Takeaways
AI InsightThe results showed that the Neural Network achieved the highest performance with an accuracy of 94.5.0% and an AUC of 99.2%, followed by Logistic Regression and SVM.
📌 3 Key Takeaways
Abstract
In this study, a portable electrocardiogram (ECG) device was developed using the Arduino Portenta embedded system board and the AD8232 sensor to enable continuous and real-time cardiac monitoring. The designed system acquires ECG signals through surface electrodes and transfers them wirelessly to a computer, where the data are recorded and analyzed in real time using MATLAB. The main objective of this research is to automatically detect cardiac arrhythmias by integrating a compact ECG acquisition system with machine learning (ML) algorithms. The training dataset was obtained from the MIT-BIH Arrhythmia Database on PhysioNet, while test data were collected in the laboratory using the proposed device from 20 individuals (10 healthy and 10 with arrhythmia). ECG signals were segmented into 60-second intervals, preprocessed, normalized, and analyzed to extract time-domain and statistical features. Several feature selection methods (GINI, ReliefF, Information Gain, Chi-square, and FCBF) were applied, and various ML classifiers were trained, including Logistic Regression, Support Vector Machine (SVM), Naïve Bayes, k-Nearest Neighbours (kNN), Decision Tree, Stochastic Gradient Descent (SGD), Random Forest, Gradient Boosting, and Artificial Neural Network (ANN). The results showed that the Neural Network achieved the highest performance with an accuracy of 94.5.0% and an AUC of 99.2%, followed by Logistic Regression and SVM. The integration of a self-designed portable ECG device with intelligent ML algorithms provides a low-cost and efficient solution for real-time arrhythmia detection, supporting early diagnosis and continuous monitoring within the Internet of Medical Things (IoMT) framework.
Özet
In this study, a portable electrocardiogram (ECG) device was developed using the Arduino Portenta embedded system board and the AD8232 sensor to enable continuous and real-time cardiac monitoring. The designed system acquires ECG signals through surface electrodes and transfers them wirelessly to a computer, where the data are recorded and analyzed in real time using MATLAB. The main objective of this research is to automatically detect cardiac arrhythmias by integrating a compact ECG acquisition system with machine learning (ML) algorithms. The training dataset was obtained from the MIT-BIH Arrhythmia Database on PhysioNet, while test data were collected in the laboratory using the proposed device from 20 individuals (10 healthy and 10 with arrhythmia). ECG signals were segmented into 60-second intervals, preprocessed, normalized, and analyzed to extract time-domain and statistical features. Several feature selection methods (GINI, ReliefF, Information Gain, Chi-square, and FCBF) were applied, and various ML classifiers were trained, including Logistic Regression, Support Vector Machine (SVM), Naïve Bayes, k-Nearest Neighbours (kNN), Decision Tree, Stochastic Gradient Descent (SGD), Random Forest, Gradient Boosting, and Artificial Neural Network (ANN). The results showed that the Neural Network achieved the highest performance with an accuracy of 94.5.0% and an AUC of 99.2%, followed by Logistic Regression and SVM. The integration of a self-designed portable ECG device with intelligent ML algorithms provides a low-cost and efficient solution for real-time arrhythmia detection, supporting early diagnosis and continuous monitoring within the Internet of Medical Things (IoMT) framework.