Smart Supervision of Cardiomyopathy Based on Fuzzy Harris Hawks Optimizer and Wearable Sensing Data Optimization: A New Model
Smart Supervision of Cardiomyopathy Based on Fuzzy Harris Hawks Optimizer and Wearable Sensing Data Optimization: A New Model
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DOI：
10.1109/tcyb.2020.3000440
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发表时间：
2021-10-01
影响因子：
11.8
通讯作者：
de Albuquerque, Victor Hugo C.
中科院分区：
文献类型：
--
作者：
Ding, Weiping;Abdel-Basset, Mohamed;de Albuquerque, Victor Hugo C.
心肌病是一种描述心肌疾病的疾病类别。它可以感染所有年龄段的人，引起不同的严重并发症，如心力衰竭和心脏骤停。通常，心肌病的体征和症状包括心律异常、头晕、头晕和昏厥。智能设备为心脏病患者的监护带来了一场非临床革命。特别是，运动传感器可以同时监测患者的异常运动。智能可穿戴设备可以有效地跟踪异常心律。这些智能可穿戴设备发出的数据必须经过充分处理，才能为心脏病患者做出正确的决定。在这篇文章中，介绍了一种全面的优化模型，用于通过传感器和可穿戴设备对心肌病患者进行智能监测。该模型包括两个新提出的算法。首先，引入模糊Harris Hawks优化器（FHHO），通过人工智能（AI）和模糊逻辑（FL）的混合，通过在观察区域中重新分配传感器来增加监测患者的覆盖范围。其次，我们引入了可穿戴传感数据优化（WSDO），这是一种准确可靠地处理心肌病传感数据的新型算法。经过测试和验证，FHHO证明可以提高患者覆盖率，减少所需传感器的数量。同时，WSDO被用于大规模仿真中的心率和衰竭检测。这些实验结果表明，WSDO可以有效地细化与高准确率和低时间成本的传感器数据。
Cardiomyopathy is a disease category that describes the diseases of the heart muscle. It can infect all ages with different serious complications, such as heart failure and sudden cardiac arrest. Usually, signs and symptoms of cardiomyopathy include abnormal heart rhythms, dizziness, lightheadedness, and fainting. Smart devices have blown up a nonclinical revolution to heart patients' monitoring. In particular, motion sensors can concurrently monitor patients' abnormal movements. Smart wearables can efficiently track abnormal heart rhythms. These intelligent wearables emitted data must be adequately processed to make the right decisions for heart patients. In this article, a comprehensive, optimized model is introduced for smart monitoring of cardiomyopathy patients via sensors and wearable devices. The proposed model includes two new proposed algorithms. First, a fuzzy Harris hawks optimizer (FHHO) is introduced to increase the coverage of monitored patients by redistributing sensors in the observed area via the hybridization of artificial intelligence (AI) and fuzzy logic (FL). Second, we introduced wearable sensing data optimization (WSDO), which is a novel algorithm for the accurate and reliable handling of cardiomyopathy sensing data. After testing and verification, FHHO proves to enhance patient coverage and reduce the number of needed sensors. Meanwhile, WSDO is employed for the detection of heart rate and failure in large simulations. These experimental results indicate that WSDO can efficiently refine the sensing data with high accuracy rates and low time cost.