Iranian Journal of Biomedical Engineering (IJBME)
نوع مقاله : مقاله کامل پژوهشی
نویسندگان
1 کارشناسی ارشد، دانشکده مهندسی برق، دانشگاه صنعتی شاهرود، شاهرود، ایران
2 دانشیار، گروه الکترونیک و مهندسی پزشکی، دانشکده مهندسی برق، دانشگاه صنعتی شاهرود، شاهرود، ایران
3 استادیار، گروه الکترونیک و مهندسی پزشکی، دانشکده مهندسی برق، دانشگاه صنعتی شاهرود، شاهرود، ایران
کلیدواژهها
موضوعات
عنوان مقاله English
نویسندگان English
Classification of electrocardiogram (ECG) signals is a crucial process in the diagnosis and treatment of cardiac abnormalities. ECG signals provide valuable information about the heart's condition, and classifying these signals as normal or abnormal plays a vital role in identifying and managing various cardiac disorders. In recent years, various feature extraction methods have been developed to enhance the accuracy and efficiency of automated ECG classification. This paper proposes a robust feature extraction method for ECG signals using normalized cepstral coefficients for the detection of cardiac arrhythmias. The extracted features in the proposed method are fed into a convolutional neural network (CNN) for classification. Additionally, various parameters of the CNN were examined to select the optimal network configuration. Statistical analysis demonstrates that the proposed feature extraction method and network achieve high accuracy in distinguishing unhealthy supraventricular arrhythmia and malignant ventricular ectopy signals from healthy signals. The experimental results indicate a 98% classification accuracy of the network in the testing phase.
کلیدواژهها English
