Key result
Machine learning pipeline using an LSTM classifier successfully detects Brugada Syndrome from scanned ECGs.
Why the study?
Only one diagnostic criterion exists for Brugada syndrome, namely a typical pattern on the ECG, prompting investigation into whether machine learning methods can improve detection and the diagnostic process.
Does a machine learning pipeline using an LSTM classifier improve the detection of Brugada Syndrome from scanned ECG images?
Does a machine learning pipeline using an LSTM classifier improve the detection of Brugada Syndrome from scanned ECG images?
A novel machine learning pipeline combining ECG digitization and an LSTM classifier demonstrates proof-of-concept for automated detection of Brugada Syndrome.
Supports feasibility of ML-based Brugada detection from scanned ECGs; leaves open prospective validation before clinical use.
Medical diagnoses can shape and change the life of a person drastically. Therefore, it is always best advised to collect as much evidence as possible to be certain about the diagnosis. Unfortunately, in the case of the Brugada Syndrome (BrS), a rare and inherited heart disease, only one diagnostic criterion exists, namely, a typical pattern in the Electrocardiogram (ECG). In the following treatise, we question whether the investigation of ECG strips by the means of machine learning methods improves the detection of BrS positive cases and hence, the diagnostic process. We propose a pipeline that reads in scanned images of ECGs, and transforms the encaptured signals to digital time-voltage data after several processing steps. Then, we present a long short-term memory (LSTM) classifier that is built based on the previously extracted data and that makes the diagnosis. The proposed pipeline distinguishes between three major types of ECG images and recreates each recorded lead signal. Features and quality are retained during the digitization of the data, albeit some encountered issues are not fully removed (Part I). Nevertheless, the results of the aforesaid program are suitable for further investigation of the ECG by a computational method such as the proposed classifier which proves the concept and could be the architectural basis for future research (Part II). This thesis is divided into two parts as they are part of the same process but conceptually different. It is hoped that this work builds a new foundation for computational investigations in the case of the BrS and its diagnosis.
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Simon Jaxy (2020) studied Brugada Syndrome (BrS). Machine learning pipeline and LSTM classifier was evaluated. A proposed machine learning pipeline successfully digitized scanned ECG images and utilized an LSTM classifier to detect Brugada Syndrome, providing a foundation for computational diagnosis.
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