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使用i-AlexNet架构进行心脏监测的心电图信号的人工智能驱动实时分类

AI-Driven Real-Time Classification of ECG Signals for Cardiac Monitoring Using i-AlexNet Architecture.

作者信息

Kolhar Manjur, Kazi Raisa Nazir Ahmed, Mohapatra Hitesh, Al Rajeh Ahmed M

机构信息

Department Health Informatics, College of Applied Medical Sciences, King Faisal University, Al Hofuf 61421, Saudi Arabia.

College of Applied Medical Sciences, King Faisal University, Al Hofuf 61421, Saudi Arabia.

出版信息

Diagnostics (Basel). 2024 Jun 25;14(13):1344. doi: 10.3390/diagnostics14131344.

DOI:10.3390/diagnostics14131344
PMID:39001235
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11240622/
Abstract

The healthcare industry has evolved with the advent of artificial intelligence (AI), which uses advanced computational methods and algorithms, leading to quicker inspection, forecasting, evaluation and treatment. In the context of healthcare, artificial intelligence (AI) uses sophisticated computational methods to evaluate, decipher and draw conclusions from patient data. AI has the potential to revolutionize the healthcare industry in several ways, including better managerial effectiveness, individualized treatment regimens and diagnostic improvements. In this research, the ECG signals are preprocessed for noise elimination and heartbeat segmentation. Multi-feature extraction is employed to extract features from preprocessed data, and an optimization technique is used to choose the most feasible features. The i-AlexNet classifier, which is an improved version of the AlexNet model, is used to classify between normal and anomalous signals. For experimental evaluation, the proposed approach is applied to PTB and MIT_BIH databases, and it is observed that the suggested method achieves a higher accuracy of 98.8% compared to other works in the literature.

摘要

随着人工智能（AI）的出现，医疗保健行业得到了发展，人工智能使用先进的计算方法和算法，从而实现更快的检查、预测、评估和治疗。在医疗保健领域，人工智能使用复杂的计算方法来评估、解读患者数据并从中得出结论。人工智能有潜力在多个方面彻底改变医疗保健行业，包括提高管理效率、制定个性化治疗方案以及改善诊断。在本研究中，对心电图信号进行预处理以消除噪声并进行心跳分割。采用多特征提取从预处理数据中提取特征，并使用一种优化技术来选择最可行的特征。i - AlexNet分类器是AlexNet模型的改进版本，用于对正常信号和异常信号进行分类。为了进行实验评估，将所提出的方法应用于PTB和MIT_BIH数据库，并且观察到与文献中的其他工作相比，所建议的方法实现了98.8%的更高准确率。

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