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<p>Cardiovascular diseases are one of the largest chronic killers in the modern world. AI enabled real time bedside monitoring and alerting from the electrocardiogram (ECG) of acute patients can help improve clinical outcome. We present a AI based clinical decision support system (CDSS) that extracts and presents domain-inspired features from ECG in real time. It also raises an alarm in an anomaly detection setting. Furthermore, we demonstrate that the system can also produce a diagnosis to assist the doctor connected to the clinically relevant features. We use arrhythmia as a test case. Given that the purpose of the paper is to develop a simple yet effective multimedia tool that can be applied in a practical healthcare setting, we have purposefully selected a non deep learning model. This is to make the CDSS lightweight and interpretable to increase the chances of future clinical translation.</p>

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A Domain Inspired AI Based Clinical Decision Support System for Real Time Heartbeat Monitoring and Arrhythmia Diagnosis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月27日

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  • Subrata Sarkar,
  • Debjit Dhar,
  • Rajib Sarkar,
  • Sanjoy K. Saha,
  • Tapabrata Chakraborti

摘要

Cardiovascular diseases are one of the largest chronic killers in the modern world. AI enabled real time bedside monitoring and alerting from the electrocardiogram (ECG) of acute patients can help improve clinical outcome. We present a AI based clinical decision support system (CDSS) that extracts and presents domain-inspired features from ECG in real time. It also raises an alarm in an anomaly detection setting. Furthermore, we demonstrate that the system can also produce a diagnosis to assist the doctor connected to the clinically relevant features. We use arrhythmia as a test case. Given that the purpose of the paper is to develop a simple yet effective multimedia tool that can be applied in a practical healthcare setting, we have purposefully selected a non deep learning model. This is to make the CDSS lightweight and interpretable to increase the chances of future clinical translation.