Memory-Efficient CNN Autoencoder for Real-Time ECG Anomaly Detection on TinyML-Enabled Edge Devices

Background: Continuously monitoring an ECG signal is vital for patients who cannot articulate their symptoms in a healthcare setting. To identify abnormalities, we introduce a compact one-dimensional convolutional autoencoder (CNN-AE) that can operate at low power consumption on TinyML-enabled edge devices. Methods: Comprising two convolutional layers and a bottleneck fully connected layer, the CNN-AE was trained on normal ECG signals from the Kaggle heartbeat Categorization dataset, derived from the PTB Diagnostic ECG Database. Anomalies were detected by comparing the reconstruction errors with the thresholds. To deploy the model at the edge, symmetric INT8 post-training quantization was applied, and the threshold was fine-tuned with calibration data. Results: The FP32 CNN-AE registered an accuracy of 83.12%, an F1-score of 88.67%, a precision of 85.88%, and a recall of 91.47% on 4046 normal and 10,506 abnormal ECG beats. The INT8 quantized model reduced the memory size by 75% (5.44 KB to 1.36 KB) while maintaining its performance (82.19% accuracy, 83.66% precision, 93.61% recall, and F1-score 88.36%). Inspection of the reconstruction errors and QRS morphology verified that quantization did not compromise the integrity of the ECG signals. Conclusions: The CNN-AE achieves a balance between accuracy, resource efficiency, and memory usage, allowing for real-time abnormality detection in assisted living and low-resource healthcare environments. Its capability on TinyML platforms indicates its role in integration with edge–cloud architectures, cloud-assisted optimization, and secure offloading strategies for scalable healthcare informatics.