HRX 2024 AbstracX Library-Personalized ECG Analysis Model through Minimal External Guidance

Personalized ECG Analysis Model through Minimal External Guidance

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The document discusses the development of a personalized ECG (electrocardiogram) analysis model by JinKook Kim, the AI team lead at HUINNO Co., Ltd. The research emphasizes the need for personalized ECG analysis due to the unique nature of each individual's heart patterns and the limitations of traditional one-size-fits-all methods. With the advent of wearable devices that allow for long-term and continuous ECG monitoring, there is a significant challenge in efficiently analyzing the vast data these devices collect.<br /><br />To address these issues, the research adopts a model architecture using inter-patient 5-fold cross-validation, enabling the assessment of its performance in various unique ECG patterns. The primary aim is to analyze ECG data with minimal external guidance, making the system more efficient and personalized to individual needs.<br /><br />The results and future directions of the study highlight a focus on expanding the model's evaluation across more atypical heart conditions, integrating the algorithm into real-time ECG monitoring systems and testing its generalization capabilities through multi-center clinical validation. Moreover, there are plans to enhance the "guide update mechanism" so the model can better adapt to evolving ECG patterns over time. These improvements aim to leverage the potential of wearable technology for more accurate, efficient, and personalized heart monitoring.

Keywords

personalized ECG analysis

JinKook Kim

HUINNO Co., Ltd.

wearable devices

inter-patient cross-validation

real-time ECG monitoring

heart patterns

guide update mechanism

multi-center clinical validation

AI in healthcare