Hedén B, Ohlsson M, Rittner R, Pahlm O, Haisty W K, Peterson C, Edenbrandt L
Department of Clinical Physiology, Lund University, Sweden.
J Am Coll Cardiol. 1996 Oct;28(4):1012-6. doi: 10.1016/s0735-1097(96)00269-0.
The purpose of this study was to compare the diagnoses of healed myocardial infarction made from the 12-lead electrocardiogram (ECG) by artificial neural networks and an experienced electrocardiographer.
Artificial neural networks have proved of value in pattern recognition tasks. Studies of their utility in ECG interpretation have shown performance exceeding that of conventional ECG interpretation programs. The latter present verbal statements, often with an indication of the likelihood for a certain diagnosis, such as "possible left ventricular hypertrophy." A neural network presents its output as a numeric value between 0 and 1; however, these values can be interpreted as Bayesian probabilities.
The study was based on 351 healthy volunteers and 1,313 patients with a history of chest pain who had undergone diagnostic cardiac catheterization. A 12-lead ECG was recorded in each subject. An expert electrocardiographer classified the ECGs in five different groups by estimating the probability of anterior myocardial infarction. Artificial neural networks were trained and tested to diagnose anterior myocardial infarction. The network outputs were divided into five groups by using the output values and four thresholds between 0 and 1.
The neural networks diagnosed healed anterior myocardial infarctions at high levels of sensitivity and specificity. The network outputs were transformed to verbal statements, and the agreement between these probability estimates and those of an expert electrocardiographer was high.
Artificial neural networks can be of value in automated interpretation of ECGs in the near future.
本研究旨在比较人工神经网络和经验丰富的心电图专家通过12导联心电图（ECG）对陈旧性心肌梗死的诊断。
人工神经网络已在模式识别任务中证明具有价值。对其在心电图解读中的效用研究表明，其表现优于传统的心电图解读程序。传统程序给出文字描述，通常会指明某种诊断的可能性，如“可能左心室肥厚”。神经网络将其输出呈现为0到1之间的数值；然而，这些数值可被解释为贝叶斯概率。
该研究基于351名健康志愿者和1313名有胸痛病史且已接受诊断性心导管检查的患者。为每位受试者记录一份12导联心电图。一位专业心电图专家通过估计前壁心肌梗死的概率将心电图分为五个不同组。对人工神经网络进行训练和测试以诊断前壁心肌梗死。通过使用输出值和0到1之间的四个阈值将网络输出分为五组。
神经网络对陈旧性前壁心肌梗死的诊断具有较高的敏感性和特异性。将网络输出转换为文字描述，这些概率估计与专业心电图专家的概率估计之间的一致性很高。
在不久的将来，人工神经网络在心电图自动解读中可能具有价值。
