基于ML和ES(智能穿戴式健康监测系统)的心脏病检测

Osama M. Abu Zaid, adham mohamed, Kamel El-Sehly, Mahmoud Ossman, Mostafa Kamal, M. Aly
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引用次数: 1

摘要

本文提出了一种基于机器学习和嵌入式系统的智能可穿戴心脏病检测系统。一种能够监测患者心率状况的智能可穿戴系统。采用光电容积描记图(PPG)检测心率。信号使用ATmega32微控制器进行处理,以确定每分钟的心率。然后,通过蓝牙通信将心率以BPM表示发送到安卓应用程序,安卓应用程序向医疗专家或患者家属或其亲属的手机发送短信提醒,通过短信包含用户当前的位置,安卓应用程序计算每天的步数。Android/桌面应用程序允许用户检查最近的医院,心脏中心,最近的健康中心(健身房)和用户当前的位置。Android/桌面应用程序允许用户知道他是否患有心脏病,通过一键运行机器/深度学习模块,分析用户的数据,以检测心脏病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Heart Disease Detection using ML and ES (Smart Wearable Health Monitoring System)
This paper proposed a smart wearable system for heart disease detection using machine learning and embedded systems. A smart wearable system that able to monitor the heart beat rate condition of patient. The heart beat rate is detected using photoplethysmogram (PPG). The signal is processed using ATmega32 Microcontroller to determine heart beat rate per minute. Then, it sends the heart rate represented as BPM to Android App Via Bluetooth Communication, Android app sends SMS alert to the mobile phone of medical experts or patient's family member, or their relatives via SMS contains user's current location, Android app calculates daily steps count. Android/Desktop app allow user to check nearest hospitals, cardiac centers, nearest Health centers (GYM) and also user's current location. Android/Desktop app allow user to know if he suffers from heart disease or not by one click which run a machine/Deep learning module that analyze user ‘s data to detect heart disease.
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