Key result
Autoencoder outperforms other unsupervised algorithms for anomaly detection in ECG signals.
Why the study?
The study was conducted to increase the precision and efficacy of cardiac abnormality diagnosis using unsupervised machine learning algorithms for ECG anomaly detection.
Autoencoders demonstrate superior performance compared to other unsupervised machine learning algorithms for detecting anomalies in ECG signals.
An essential diagnostic tool for finding irregularities in the rhythm and function of the heart is the electrocardiogram (ECG) signal. After obtaining the ECG signals from the adjacent hospital, we preprocessed and examined the data. We used unsupervised learning methods for anomaly detection in this paper. Important elements of the ECG signal are also covered in detail in our study. With the goal of increasing the precision and efficacy of cardiac abnormality diagnosis, we used this knowledge to conduct research on anomaly detection using unsupervised machine learning algorithms. After comparison of algorithm for anomaly detection, we found that autoencoder performance was best than other algorithms. This study is in line with our goal of advancing innovative technology that improves diagnostic accuracy while also fostering sustainable health solutions, which in turn strengthens the resilience of medical infrastructure and promotes long-term well-being to meet the SDGs.
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Alam et al. (2024) studied Cardiac abnormality. Autoencoder vs. Other unsupervised machine learning algorithms was evaluated on Anomaly detection performance. Autoencoder demonstrated the best performance for anomaly detection in ECG signals compared to other unsupervised machine learning algorithms.
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