The severe challenges of the fast aging population and the prevalence of cardiovascular diseases highlight the needs for effective solutions supporting more accurate and affordable medical diagnosis and treatment. Recent advances in cloud computing have inspired numerous designs of cloud-based health care services. In this paper, we developed a cloud-computing platform monitored by physicians, which can receive 12-lead ECG records and send back diagnostic reports to users. Aiming to lessen the physicians’ workload, we implemented an analysis algorithm that can identify abnormal heart rate, irregular heartbeat, abnormal amplitude, atrial fibrillation and abnormal ECG in it. A large number of testing samples were used to evaluate performance. Our algorithm achieved a TPR95 (specificity under the condition of negative predictive value being equal to 95 percent) of 68.5 percent and 0.9317 AUC (area under the ROC curve) for classification of normal and abnormal ECG records and a sensitivity of 98.51 percent and specificity of 98.26 percent for atrial fibrillation classification, comparable to the state-of-the-art results for each subject. The proposed ECG cloud computing service has been applied in Hunan Jinshengda Aerial Hospital Network and it now can receive and analyze ECG records in real time.

中文翻译：

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智能健康船ABC-DE：心电图云计算服务

人口快速老龄化和心血管疾病流行的严峻挑战凸显了对支持更准确和负担得起的医疗诊断和治疗的有效解决方案的需求。云计算的最新进展激发了大量基于云的医疗保健服务的设计。在本文中，我们开发了一个由医生监控的云计算平台，它可以接收 12 导联心电图记录并将诊断报告发送给用户。为了减轻医生的工作量，我们在其中实施了一种分析算法，可以识别心率异常、心律不齐、振幅异常、心房颤动和心电图异常。大量的测试样本用于评估性能。我们的算法实现了 68.5% 的 TPR95（阴性预测值等于 95% 条件下的特异性）和 0.9317 AUC（ROC 曲线下面积）用于正常和异常 ECG 记录的分类以及 98.51% 的敏感性和特异性房颤分类为 98.26%，与每个受试者的最新结果相当。所提出的心电云计算服务已在湖南金盛达航空医院网络中应用，现在可以实时接收和分析心电记录。