What question did this study set out to answer?

The aim is to develop a gender-aware system for the early detection of ischemic heart disease using ECG signals.

June 18, 2026Open Access

GAMENet: Gender-Aware Morphology Encoder Network for Early Ischemia Heart Disease Classification

Q: What is the clinical evidence from this study?

Study design: Other. Population: Ischemic Heart Disease (IHD). Intervention: Gender-Aware Morphology Encoder Network (GAMENet). Primary outcome: Ischemic heart disease classification accuracy.

CDC DeeptiVisvesvaraya Technological University ADAnnapurna DammurVisvesvaraya Technological University

Key Result

The proposed Gender-Aware Morphology Encoder Network (GAMENet) using 12-lead ECG signals and clinical metadata enables accurate ischemic heart disease classification and improved interpretability.

Key Points

The aim is to develop a gender-aware system for the early detection of ischemic heart disease using ECG signals.
Developed a novel framework called GAMENet using the PTB-XL database.
Isolated relevant ECG waveform components using Adaptive Morphology Deviation Encoder.
Utilized dense layers with dropout regularization and SoftMax classification for analysis.
GAMENet significantly improved classification accuracy compared to existing approaches.
Enhanced model interpretability was achieved, especially for subtle early-stage abnormalities.
Results indicate better detection rates for female patients with atypical symptoms.

Structured PICO

Does the Gender-Aware Morphology Encoder Network (GAMENet) improve early ischemic heart disease classification using 12-lead ECG signals?

Population

ECG records from the PTB-XL database

Intervention

Gender-Aware Morphology Encoder Network (GAMENet) using 12-lead ECG signals with clinical metadata

Outcome

Accurate Ischemic Heart Disease (IHD) classification

A novel gender-aware deep learning framework (GAMENet) using 12-lead ECGs and clinical metadata may improve the accuracy and interpretability of early ischemic heart disease detection.

Abstract

Ischemic Heart Disease (IHD) is the leading cause of cardiovascular mortality worldwide. Early detection of ischemic changes using electrocardiogram (ECG) signals is vital for timely intervention and enhanced clinical outcomes. However, the diagnosis of IHD varies significantly between men and women. Women often present with atypical symptoms, and their cardiovascular risk is frequently underestimated, which leads to delayed diagnosis. Also, existing approaches face challenges in subtle early-stage abnormalities, single-lead ECG presentation, and the limited interpretability of deep learning models. These cause significant challenges to the accurate diagnosis of IHD. To address these, this study proposes a gender-aware framework, Gender-Aware Morphology Encoder Network (GAMENet), for early ischemic heart disease detection using 12-lead ECG signals with clinical metadata. A novel GAMENet is developed using the PTB-XL database. The Adaptive Morphology Deviation Encoder (AMDE) through Morphology Segment Extraction (MSEG-R) using R-Peak anchoring, isolates clinically relevant waveform components (P-wave, QRS complex, ST-segment, and T-wave) from the preprocessed ECG signals. The feature vector of morphology features is passed through dense layers with dropout regularization and a SoftMax classifier. Statistical and comparative analysis ensures that the proposed framework enables accurate IHD classification and improved interpretability.

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