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<p>Electrocardiogram (ECG) signal analysis is fundamental for the diagnosis of cardiac disorders and the detection of arrhythmias. However, manual interpretation remains labor-intensive and prone to variability, emphasizing the necessity for automated diagnostic systems. This study presents a novel methodology that combines particle swarm optimization (PSO) with wavelet transform (WT) to enhance ECG signal quality through effective noise suppression. The proposed denoising framework achieves a notable signal-to-noise ratio (SNR) of 18.24 at an input SNR of 10&#xa0;dB, preserving vital signal features. Subsequently, the denoised signals are converted into two-dimensional representations via the 2D discrete wavelet transform (2D DWT), enabling robust feature extraction for classification using convolutional neural networks (CNNs). The classification model achieves exceptional performance, with precision values of 0.99 for atrial fibrillation (ARR) and normal sinus rhythm (NSR), and 0.98 for congestive heart failure (CHF), accompanied by high recall and F1-scores. In addition, the study introduces a fully automated arrhythmia detection and classification system, integrating an optimized deep learning architecture with real-time ECG acquisition using the AD8232 sensor. Data transmission is facilitated through a secure Internet of Things (IoT) framework employing the Node-RED IBM platform and the Message Queuing Telemetry Transport (MQTT) protocol, ensuring efficient and reliable communication with analytical modules. The system is deployed on an ESP32 microcontroller, demonstrating low-latency processing with the complete diagnostic workflow spanning acquisition to classification executed within 32&#xa0;s. These results underscore the system’s efficacy in delivering accurate and timely cardiac anomaly detection, with promising implications for real-time healthcare monitoring and smart medical diagnostics.</p>

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IoT-enabled ECG signal preprocessing and CNN-based classification: a novel approach for enhanced healthcare monitoring

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月22日

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  • Abdallah Azzouz,
  • Abdelhafid Tobbal,
  • Billel Bengherbia,
  • Patrice Wira,
  • Abdelkerim Souahlia,
  • Boubakeur Latreche,
  • Abdelaziz Rabehi,
  • Takele Ferede Agajie,
  • Abdullah K. Alanazi

摘要

Electrocardiogram (ECG) signal analysis is fundamental for the diagnosis of cardiac disorders and the detection of arrhythmias. However, manual interpretation remains labor-intensive and prone to variability, emphasizing the necessity for automated diagnostic systems. This study presents a novel methodology that combines particle swarm optimization (PSO) with wavelet transform (WT) to enhance ECG signal quality through effective noise suppression. The proposed denoising framework achieves a notable signal-to-noise ratio (SNR) of 18.24 at an input SNR of 10 dB, preserving vital signal features. Subsequently, the denoised signals are converted into two-dimensional representations via the 2D discrete wavelet transform (2D DWT), enabling robust feature extraction for classification using convolutional neural networks (CNNs). The classification model achieves exceptional performance, with precision values of 0.99 for atrial fibrillation (ARR) and normal sinus rhythm (NSR), and 0.98 for congestive heart failure (CHF), accompanied by high recall and F1-scores. In addition, the study introduces a fully automated arrhythmia detection and classification system, integrating an optimized deep learning architecture with real-time ECG acquisition using the AD8232 sensor. Data transmission is facilitated through a secure Internet of Things (IoT) framework employing the Node-RED IBM platform and the Message Queuing Telemetry Transport (MQTT) protocol, ensuring efficient and reliable communication with analytical modules. The system is deployed on an ESP32 microcontroller, demonstrating low-latency processing with the complete diagnostic workflow spanning acquisition to classification executed within 32 s. These results underscore the system’s efficacy in delivering accurate and timely cardiac anomaly detection, with promising implications for real-time healthcare monitoring and smart medical diagnostics.