Why the study?
There is a pressing lack of accurate and interpretable machine models providing transparency in disease and anomaly detection.
Does an interpretable Autoencoder model provide accurate and transparent anomaly detection in ECGs?
Does an interpretable Autoencoder model provide accurate and transparent anomaly detection in ECGs?
The paper proposes an interpretable Autoencoder model for detecting anomalies in ECGs to address the lack of transparency in machine learning models.
May enhance ECG anomaly detection transparency; leaves open prospective clinical validation.
When it comes to your heart, getting a second opinion is never a bad thing. We all need assurances and the more people tell us, the better we feel about our decision being right. In medicine, the judgment of doctors over some symptom is either based on a machine or on their experience. A patient can communicate with the doctor to understand how they reached a particular judgement. This is where the machines lack when it comes to providing accuracy and more importantly, transparency. The lack of accurate and interpretable machine models for disease/anomaly detection is a pressing issue and needs to be tackled effectively. This paper aims to target anomaly detection in ECGs through the means of an accurate and interpretable machine learning model.
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Sanyal et al. (2023) studied this question.
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