Transformer-based heart language model with electrocardiogram annotations

Figures

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  Fig. 1. ECG illustration of (a) NSR, a regular rhythm with patterns, and (b) AFIB rhythm, an irregular rhythm without patterns.

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  Fig. 2. Durations of AFIB and non-AFIB rhythm episodes in seconds.

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  Fig. 3. Characteristic features of a heartbeat in ECG.

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  Fig. 4. ECG strip with annotated RR intervals (in ms), dRR values (in ms), and encoded characters (presented bold) using the mapping from Table 2. Text encoding of the given ECG interval is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$q \mu l \beta n$$\end{document}.

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  Fig. 5. Flowchart that systematically outlines the entire workflow from taking raw ECG annotations to finetuned AFIB classifier for sequences of 41 heartbeats.

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  Fig. 6. Duration-based approach to calculate TP, TN, FP, and FN based on time instead of the count of included heartbeats.

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  Fig. 7. Loss function versus epochs while training the foundation models with different context window sizes (41, 128, 256, and 512 tokens).

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  Fig. 8. Performance versus training epochs while finetuning the models with a context window of 41, 128, 256, and 512 tokens.

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  Fig. 9. Performance trends for models with a context window size of 41, 128, 256, and 512 tokens, evaluating TDB as a collection of ECG databases excluding MITDB used for training.

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  Fig. 10. F1 and F0 scores evaluating the models with a context window size of 41, 128, 256, and 512 tokens, trained on MITDB and evaluated on AFDB, LTAFDB, MITDB, and TDB (consisting of all databases excluding MITDB).

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  Fig. 11. Performance score Differences between beat and duration-based evaluations.

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  Fig. 12. An example of an FP error (MITDB record 200).