Liotto Carmine, Petrillo Alberto, Santini Stefania, Toscano Gianluca, Tufano Vincenza
Teoresi Group S.p.a., Via Ferrante Imparato, 198, 80146 Naples, Italy.
Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy.
Biomed Eng Lett. 2022 Sep 12;12(4):433-444. doi: 10.1007/s13534-022-00246-8. eCollection 2022 Nov.
The accurate analysis of Electrocardiogram waveform plays a crucial role for supporting cardiologist in detecting and diagnosing the heartbeat disorders. To improve their detection accuracy, this work is devoted to the design of a novel classification algorithm which is composed of a cascade of two convolutional neural network (CNN), i.e a Binary CNN allowing the detection of the arrhythmic heartbeat and a Multiclass CNN able to recognize the specific disorder. Moreover, by combining the cascade architecture solution with a rule-based data splitting, which leverages the and criteria, it is possible predicting the health status of unseen patients. Numerical results, carried out considering Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database, disclose a classification accuracy of . Finally, a cross-database performance evaluation and a comparison analysis w.r.t. the current state-of-art further disclose the effectiveness and the efficiency of the proposed solution, as well as its benefits in terms of patient health status prediction.
心电图波形的准确分析对于支持心脏病专家检测和诊断心跳紊乱起着至关重要的作用。为了提高检测准确性，这项工作致力于设计一种新颖的分类算法，该算法由两个卷积神经网络（CNN）级联组成，即一个用于检测心律失常心跳的二元CNN和一个能够识别特定紊乱的多类CNN。此外，通过将级联架构解决方案与基于规则的数据分割相结合，利用[具体标准1]和[具体标准2]标准，可以预测未见过的患者的健康状况。考虑到麻省理工学院-贝斯以色列医院心律失常数据库进行的数值结果显示分类准确率为[具体准确率]。最后，跨数据库性能评估以及与当前最先进技术的比较分析进一步揭示了所提出解决方案的有效性和效率，以及其在患者健康状况预测方面的优势。
