Explainable Deep Learning-Based Approach for Multilabel Classification of Electrocardiogram

M. Ganeshkumar, Vinayakumar Ravi, V. Sowmya, E. A. Gopalakrishnan, Soman K.P.

IEEE Transactions on Engineering Management 2021 article cited by 85

Source More in Medicine & Health

importance 6/10 ai assisted ECG Monitoring and Analysis cardiac diagnostics stage: research

Problem

Automated ECG diagnosis must detect multiple co-occurring heart abnormalities in one signal, and deep learning's black-box nature blocks high-risk clinical use.

Why it matters

Patients often have more than one heart disease, and unreliable automated diagnosis carries life-threatening risk.

What the AI did

A deep learning method performs multilabel ECG classification (up to two labels across eight abnormalities plus normal) and adds explainable AI for transparency.

Results

not stated in source

Impact

Peer-reviewed method; clinical validation or adoption not stated in source.

Record

Venue	IEEE Transactions on Engineering Management
Published	2021
Work type	article
Authors	M. Ganeshkumar, Vinayakumar Ravi, V. Sowmya, E. A. Gopalakrishnan, Soman K.P.
Citations	85 (OpenAlex, at import time)
References	35
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1109/tem.2021.3104751
Open access	closed
Keywords	ECG, deep learning, multilabel classification, explainable AI, cardiology

Importance rating

6 / 10

significant — thousands to millions affected

Multilabel ECG diagnosis with explainability addresses automated detection of co-occurring heart abnormalities; significant clinical problem but no scale or validation results stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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