Machine Learning Algorithms for Heart Rhythm

​Machine learning algorithms are capable of analyzing electrocardiogram (ECG) data to identify arrhythmias and categorize heart disease. A new algorithm combining two-event-related moving averages (TERMA) and fractional Fourier transforms (FrFT) is outperforming state of the art algorithms in detecting heart rhythm peaks.

These two methods combined have achieved accurate detection of P, QRS, and T waves. In an electrocardiogram (ECG or EKG), the PQRST waves represent the heart's electrical activity, with the P wave triggering the beat, the QRS complex causing the ventricles to contract, and the T wave allowing the ventricles to recover and prepare for the next beat. The features of ECG signals can be used to train machine learning models to classify heart disease. The algorithm, with the help of different databases, detects the peaks of heart rhythms and classifies heart diseases.

The algorithm under consideration utilizes TERMA and FrFT to identify heart rhythm peaks effectively. TERMA identifies specific regions of interest to find the desired peaks, whereas FrFT rotates the ECG signals to reveal the peaks' positions. This fusion improves the algorithm by allowing it to detect P, QRS, and T waves with ease. The detection performance of the algorithm surpasses that of existing algorithms, proving the efficiency of the method used for ECG signal analysis.

The proposed algorithm extracts features such as PR and RT durations from the detected peak locations. Age, sex, and other features are used to train machine learning models such as MLP (multilayer perceptron, a type of neural network) and SVM (support vector machine, used for classification and regression analysis). These trained models can predict the presence of heart disease with high accuracy, demonstrating the applicability of machine learning to cardiovascular disease diagnosis and treatment.

Sanjiv Narayan

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