In order to analyze the experimental cardiovascular signal with high accuracy, a system, integrating real-time monitoring and off-line further analysis, was developed and verified. The design, data processing and anal...In order to analyze the experimental cardiovascular signal with high accuracy, a system, integrating real-time monitoring and off-line further analysis, was developed and verified. The design, data processing and analysis methods as well as testing results are described. With 5 sampling frequency choices and 8 channel data acquisition, the system achieved high performances in beat-to-beat monitoring, signal processing and analysis. Tests were carried out to validate its performance in real-time monitoring, effectiveness of digital filters, QRS and blood pressure detection reliability, and RR-interval timing accuracy. The QRS detection rate was at least 99.46% for the records with few noises from MIT-BIH arrhythmia database using the algorithm for real-time monitoring, and no less than 96.43% for the records with some noises. In the condition that noise amplitude levels were less than 80%,the standard deviations for RR-interval timing were less than 1 ms with a generated ECG corrupted with various noises from MIT-BIH Noise Stress Test Database. Besides, the system is open for function expansion to meet further study-specific needs.展开更多
The rapid advancement of deep learning has revolutionized electrocardiogram(ECG)analysis,with Transformer architectures emerging as powerful tools for automated arrhythmia classification.This paper presents a comprehe...The rapid advancement of deep learning has revolutionized electrocardiogram(ECG)analysis,with Transformer architectures emerging as powerful tools for automated arrhythmia classification.This paper presents a comprehensive review of Transformer-based arrhythmia classification methods,examining their evolution,current capabilities,and future potential.We systematically analyze the architectural adaptations of Transformers for ECG signal processing,including Vision Transformers adapted for 1D medical signals,hybrid CNN-Transformer models,and lightweight implementations for edge computing.Our review encompasses recent studies demonstrating exceptional performance,with models like ECGformer achieving 98%accuracy on MIT-BIH datasets and tiny Transformer variants reaching 98.97%accuracy with only 6k parameters suitable for wearable devices.We discuss key advantages including the ability to capture long-range dependencies in ECG sequences,handle variable-length inputs,and integrate multi-lead spatial information through attention mechanisms.However,significant challenges remain,including high computational requirements,dependence on large labeled datasets,limited interpretability in clinical settings,and over-fitting r isks with imbalanced data.The paper explores emerging solutions such as transfer learning,data augmentation techniques,and explainable AI methods to address these limitations.Future prospects include the development of more efficient architectures for real-time monitoring,integration with multi-modal physiological data,and enhanced clinical interpretability.This comprehensive analysis provides valuable insights for researchers and clinicians working toward more accurate,efficient,and clinically viable automated arrhythmia detection systems.展开更多
基金This work is supported by Beijing Natural Science Foundation (3052015)
文摘In order to analyze the experimental cardiovascular signal with high accuracy, a system, integrating real-time monitoring and off-line further analysis, was developed and verified. The design, data processing and analysis methods as well as testing results are described. With 5 sampling frequency choices and 8 channel data acquisition, the system achieved high performances in beat-to-beat monitoring, signal processing and analysis. Tests were carried out to validate its performance in real-time monitoring, effectiveness of digital filters, QRS and blood pressure detection reliability, and RR-interval timing accuracy. The QRS detection rate was at least 99.46% for the records with few noises from MIT-BIH arrhythmia database using the algorithm for real-time monitoring, and no less than 96.43% for the records with some noises. In the condition that noise amplitude levels were less than 80%,the standard deviations for RR-interval timing were less than 1 ms with a generated ECG corrupted with various noises from MIT-BIH Noise Stress Test Database. Besides, the system is open for function expansion to meet further study-specific needs.
基金supported by the Henan Provincial Scientific and Technological Research Project(No.252102210005,222102310222)the Training Program for Young Backbone Teachers in Higher Education Institutions of Henan Province(No.2025GGJS149).
文摘The rapid advancement of deep learning has revolutionized electrocardiogram(ECG)analysis,with Transformer architectures emerging as powerful tools for automated arrhythmia classification.This paper presents a comprehensive review of Transformer-based arrhythmia classification methods,examining their evolution,current capabilities,and future potential.We systematically analyze the architectural adaptations of Transformers for ECG signal processing,including Vision Transformers adapted for 1D medical signals,hybrid CNN-Transformer models,and lightweight implementations for edge computing.Our review encompasses recent studies demonstrating exceptional performance,with models like ECGformer achieving 98%accuracy on MIT-BIH datasets and tiny Transformer variants reaching 98.97%accuracy with only 6k parameters suitable for wearable devices.We discuss key advantages including the ability to capture long-range dependencies in ECG sequences,handle variable-length inputs,and integrate multi-lead spatial information through attention mechanisms.However,significant challenges remain,including high computational requirements,dependence on large labeled datasets,limited interpretability in clinical settings,and over-fitting r isks with imbalanced data.The paper explores emerging solutions such as transfer learning,data augmentation techniques,and explainable AI methods to address these limitations.Future prospects include the development of more efficient architectures for real-time monitoring,integration with multi-modal physiological data,and enhanced clinical interpretability.This comprehensive analysis provides valuable insights for researchers and clinicians working toward more accurate,efficient,and clinically viable automated arrhythmia detection systems.
