论文标题
基于人工智能和光谱分析方法的数字化心电图信号的分析专门针对ARVC
Analysis of Digitalized ECG Signals Based on Artificial Intelligence and Spectral Analysis Methods Specialized in ARVC
论文作者
论文摘要
心律失常右心肌病（ARVC）是一种遗传性的心肌疾病，在患者生命的第二和十年之间出现，导致35岁之前的20％的心脏死亡。有效的和守点的诊断基于这种疾病（ECGS）可能在重新置于多重耐多重的副本中具有重要的作用。在我们的分析中，我们首先概述了基于纸张的ECG信号的数字化过程，该空间过滤器的旨在消除数据集图像中与ECG波形无关的黑暗区域，从而产生不良的噪声。 Next, we propose the utilization of a low - complexity convolutional neural network for the detection of an arrhythmogenic heart disease, that has not been studied through the usage of deep learning methodology to date, achieving high classification accuracy, namely 99.98% training and 98.6% testing accuracy, on a disease the major identification criterion of which are infinitesimal millivolt variations in the ECG's morphology, in与其他心律失常异常形成对比。最后，通过进行光谱分析，我们研究了与ARVC患者相对应的正常ECG和ECG之间频率领域的显着区别。在我们遇到统计学上显着分化的18个频率中，有16个中，正常的心电图的特征是与异常相比更大的归一化振幅。本文进行的总体研究强调了将数学方法整合到各种疾病的检查和有效诊断中的重要性，旨在为他们的成功治疗做出重大贡献。
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that appears between the second and forth decade of a patient's life, being responsible for 20% of sudden cardiac deaths before the age of 35. The effective and punctual diagnosis of this disease based on Electrocardiograms (ECGs) could have a vital role in reducing premature cardiovascular mortality. In our analysis, we firstly outline the digitalization process of paper - based ECG signals enhanced by a spatial filter aiming to eliminate dark regions in the dataset's images that do not correspond to ECG waveform, producing undesirable noise. Next, we propose the utilization of a low - complexity convolutional neural network for the detection of an arrhythmogenic heart disease, that has not been studied through the usage of deep learning methodology to date, achieving high classification accuracy, namely 99.98% training and 98.6% testing accuracy, on a disease the major identification criterion of which are infinitesimal millivolt variations in the ECG's morphology, in contrast with other arrhythmogenic abnormalities. Finally, by performing spectral analysis we investigate significant differentiations in the field of frequencies between normal ECGs and ECGs corresponding to patients suffering from ARVC. In 16 out of the 18 frequencies where we encounter statistically significant differentiations, the normal ECGs are characterized by greater normalized amplitudes compared to the abnormal ones. The overall research carried out in this article highlights the importance of integrating mathematical methods into the examination and effective diagnosis of various diseases, aiming to a substantial contribution to their successful treatment.
