Key result
Automated CNN-LSTM framework with explainable AI achieves superior detection of cardiac anomalies.
Why the study?
Cardiovascular diseases cause 17.9 million annual deaths globally, motivating automated and interpretable diagnostic frameworks for detecting cardiac anomalies.
Does an automated framework using CNN-LSTM and Explainable AI improve the detection of cardiac anomalies in heartbeat data?
Does an automated framework using CNN-LSTM and Explainable AI improve the detection of cardiac anomalies in heartbeat data?
An automated CNN-LSTM framework integrating Explainable AI (SHAP and LIME) provides an interpretable and efficient method for detecting cardiac anomalies from heartbeat data.
Answers are grounded in this paper and cite their sources.
Hypothesis-generating for AI-based ECG anomaly detection; prospective validation required before clinical adoption.
Cardiovascular diseases are responsible for 17.9 million deaths annually, accounting for approximately 32% of global fatalities. This study introduces an automated framework for detecting cardiac anomalies across five heartbeat classes: normal, murmur, extrasystole, artifact, and extrahls from the Dangerous Heartbeat Disease dataset. The approach utilizes feature fusion of state-of-the-art deep learning models into a fine-tuned CNN-LSTM network, combining representations extracted from ResNet101, VGG16, and Inception V3 architectures. These features derived from Mel-Frequency cepstral coefficients (MFCCs) and spectrogram analysis, capture critical acoustic patterns indicative of cardiac conditions. The core contribution lies in the integration of Explainable AI (XAI) techniques, SHAP and LIME to add a factor of interpretability to the system. This fine-tuned framework resulted in superior classification performance across key evaluation metrics, offering a transparent and efficient solution for cardiovascular diagnostics. The research lays a foundation for encouraging the trustworthy adoption of automated systems in clinical applications.
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Krishna et al. (2025) studied Cardiac anomalies. Automated framework using fine-tuned CNN-LSTM network with Explainable AI was evaluated on Classification performance across key evaluation metrics. An automated framework integrating a fine-tuned CNN-LSTM network with Explainable AI techniques achieved superior classification performance for detecting cardiac anomalies.
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