Design of Portable Fuzzy Diagnosis Instrument for ECG Signal Based on Internet of Things / 中国医疗器械杂志

OBJECTIVE@#A method for dynamically collecting and processing ECG signals was designed to obtain classification information of abnormal ECG signals.@*METHODS@#Firstly, the ECG eigenvectors were acquired by real-time acquisition of ECG signals combined with discrete wavelet transform, and then the ECG fuzzy information entropy was calculated. Finally, the Euclidean distance was used to obtain the semantic distance of ECG signals, and the classification information of abnormal signals was obtained.@*RESULTS@#The device could effectively identify abnormal ECG signals on an embedded platform based on the Internet of Things, and improved the diagnosis accuracy of heart diseases.@*CONCLUSIONS@#The fuzzy diagnosis device of ECG signal could accurately classify the abnormal signal and output an online signal classification matrix with a high confidence interval.

Design and Implementation of Portable Abnormal ECG Signal Analysis Instrument Based on Feature Classifcation / 中国医疗器械杂志

OBJECTIVES@#To collect and analyze the ECG signal in real time, the analog filter and the signal amplifier were used to construct the abnormal signal acquisition and classification system.@*METHODS@#The ARM10E processor was used to detect the signal shape and QRS complex wave. Based on the Poincare support vector machine, the feature set was extracted from the training data set to construct the heart disease classifier, and the clinical classification model was given.@*RESULTS@#The device effectively reduces computational complexity, improves processor speed, real-time acquisition and diagnoses heart disease.@*CONCLUSIONS@#Portable ECG devices can capture suspected waveforms of abnormal signals, establish and evaluate high quality signals, reduce patient on-line waiting time, and facilitate early diagnosis and recognition of heart disease.