TY - A2的帕伦博,大卫。非盟-沙菲克,默罕默德盟- Du,长庆盟——贾马尔,纳西尔盟——爱宝,Junaid Hussain AU - Kamal, Tahir盟——Afsar萨尔曼盟——米娅,Md。Solaiman PY - 2023 DA - 2023/04/20 TI -智能E-Health心脏病检测系统利用人工智能和物联网集成下一代传感器网络SP - 6383099六世- 2023 AB -据世界卫生组织统计,心脏病是全球死亡的最大原因。它可能会降低个体的总体死亡率如果可以检测到心血管疾病的早期阶段。如果检测到心脏疾病在早期阶段,有一个更大的可能性,它可能成功在医生的指导下治疗和管理。近年来物联网等领域的进步,云存储,和机器学习上升到新的乐观情绪的能力技术在全球范围内带来一种范式变革。在床边,使用传感器来获取生命体征近年来已经越来越普遍。病人是手动监控使用监视器位于病人的床边;没有自动数据处理。这些结果来自一项调查心血管疾病进行的大量的医院,已经使用在早期的协议的发展,自动化,智能识别心脏疾病。帕斯卡的数据集是由收集数据从不同的医院使用数码听诊器。这些数据集是公开的,它被许多世界各地的研究人员在实验工作。 The proposed strategy for doing research includes three steps. The first stage is known as the data collection phase, the data is collected using biosensors and IoT devices through wireless sensor networks. In the second step, all of the information pertaining to healthcare is uploaded to the cloud so that it may be analyzed. The last step in the process is training the model using data taken from already-existing medical records. Deep learning strategies are used in order to classify the sound that is produced by the heart. The deep CNN algorithm is used for sound feature extraction and classification. The PASCAL data set is essential to the functioning of the experimental environment. The deep CNN model is performing most accurately. SN - 1687-725X UR - https://doi.org/10.1155/2023/6383099 DO - 10.1155/2023/6383099 JF - Journal of Sensors PB - Hindawi KW - ER -