Key result
A Feed Forward Neural Network optimized with Particle Swarm Optimization achieved an overall accuracy of 93.687% and sensitivity of 92% in classifying ECG signals for cardiac arrhythmia detection.
Why the study?
Cardiac arrhythmia is an anomaly within the heart that may be diagnosed using electrocardiogram signals, motivating the development of a software model for ECG signal classification.
A machine learning model using a Feed Forward Neural Network optimized with Particle Swarm Optimization achieved 93.7% accuracy in classifying ECG signals.
Promising ECG classification accuracy in small sample warrants clinical caution; leaves open larger prospective validation.
The electrical activity which might be acquired by inserting the probes on the body exterior that is originated within the individual muscle cells of the heart and is summed to indicate an indication wave form referred to as the EKG (ECG). Cardiac Arrhythmia is an associate anomaly within the heart which may be diagnosed with the usage of signals generated by Electrocardiogram (ECG). For the classification of ECG signals a software application model was developed and has been investigated with the usage of the MIT-BIH database. The version is based on some existing algorithms from literature, entails the extraction of a few temporal features of an ECG signal and simulating it with a trained FFNN. The software version may be employed for the detection of coronary heart illnesses in patients. The neural network’s structure and weights are optimized using Particle Swarm Optimization (PSO). The FFNN trained with set of rules by PSO increase its accuracy. The overall accuracy and sensitivity of the algorithm is about 93.687 % and 92%.
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A 2019 study studied Cardiac Arrhythmia (n=48). Feed Forward Neural Network optimized with Particle Swarm Optimization (FFNN-PSO) vs. Ground truth (Doctor's diagnosis) was evaluated on Overall classification accuracy. A Feed Forward Neural Network optimized with Particle Swarm Optimization achieved an overall accuracy of 93.687% and sensitivity of 92% in classifying ECG signals for cardiac arrhythmia detection.
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