Treatment and prevention of cardiovascular diseases often rely on Electrocardiogram (ECG) interpretation. Dependent on the physician's variability, ECG interpretation is subjective and prone to errors. Machine learning models are often developed and used to support doctors; however, their lack of interpretability stands as one of the main drawbacks of their widespread operation. This paper focuses on an Explainable Artificial Intelligence (XAI) solution to make heartbeat classification more explainable using several state-of-the-art model-agnostic methods. We introduce a high-level conceptual framework for explainable time series and propose an original method that adds temporal dependency between time samples using the time series' derivative. The results were validated in the MIT-BIH arrhythmia dataset: we performed a performance's analysis to evaluate whether the explanations fit the model's behaviour; and employed the 1-D Jaccard's index to compare the subsequences extracted from an interpretable model and the XAI methods used. Our results show that the use of the raw signal and its derivative includes temporal dependency between samples to promote classification explanation. A small but informative user study concludes this study to evaluate the potential of the visual explanations produced by our original method for being adopted in real-world clinical settings, either as diagnostic aids or training resource.

Neves, I., Folgado, D., Santos, S., Barandas, M., Campagner, A., Ronzio, L., et al. (2021). Interpretable heartbeat classification using local model-agnostic explanations on ECGs. COMPUTERS IN BIOLOGY AND MEDICINE, 133(June 2021) [10.1016/j.compbiomed.2021.104393].

Interpretable heartbeat classification using local model-agnostic explanations on ECGs

Campagner A.;Cabitza F.;
2021

Abstract

Treatment and prevention of cardiovascular diseases often rely on Electrocardiogram (ECG) interpretation. Dependent on the physician's variability, ECG interpretation is subjective and prone to errors. Machine learning models are often developed and used to support doctors; however, their lack of interpretability stands as one of the main drawbacks of their widespread operation. This paper focuses on an Explainable Artificial Intelligence (XAI) solution to make heartbeat classification more explainable using several state-of-the-art model-agnostic methods. We introduce a high-level conceptual framework for explainable time series and propose an original method that adds temporal dependency between time samples using the time series' derivative. The results were validated in the MIT-BIH arrhythmia dataset: we performed a performance's analysis to evaluate whether the explanations fit the model's behaviour; and employed the 1-D Jaccard's index to compare the subsequences extracted from an interpretable model and the XAI methods used. Our results show that the use of the raw signal and its derivative includes temporal dependency between samples to promote classification explanation. A small but informative user study concludes this study to evaluate the potential of the visual explanations produced by our original method for being adopted in real-world clinical settings, either as diagnostic aids or training resource.
Articolo in rivista - Articolo scientifico
Electrocardiogram; Explainable artificial intelligence; Heartbeat classification; Human–AI interfaces; Machine learning; Model-agnostic method; Time series; Usability; Visual explanations;
English
16-apr-2021
2021
133
June 2021
104393
open
Neves, I., Folgado, D., Santos, S., Barandas, M., Campagner, A., Ronzio, L., et al. (2021). Interpretable heartbeat classification using local model-agnostic explanations on ECGs. COMPUTERS IN BIOLOGY AND MEDICINE, 133(June 2021) [10.1016/j.compbiomed.2021.104393].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/324847
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