CN112489793A - 一种脑卒中风险患者用预警系统 - Google Patents
一种脑卒中风险患者用预警系统 Download PDFInfo
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Abstract
本发明公开了一种脑卒中风险患者用预警系统,包括:数据获取单元,用于获取患者的表征脑卒中发病风险的检测数据;数据传输单元,用于将检测数据上传至存储和计算单元;存储和计算单元,用于对检测数据进行存储和预处理;将预处理后检测数据输入脑卒中疾病深度学习模型,获取输出结果,所述输出结果为脑卒中发病概率;判断输出结果是否超过预定值;所述输出结果超过预定值时,输出报警信号;显示单元,用于显示检测数据和对应的输出结果;预警单元,用于接收并响应报警信号,本发明能够提前对脑卒中风险进行预警。
Description
技术领域
本发明涉及智能穿戴设备技术领域,尤其是涉及一种脑卒中风险患者用预警系统。
背景技术
近年来,慢性病人群不断扩大且呈年轻化趋势,社会老龄化现象日益严重,人们的健康观念由被动治疗转变为主动监测和预防,可穿戴的智能医疗设备在预防疾病中发挥着巨大的作用,因此我国的可穿戴设备医疗市场呈现快速发展的态势。目前我国国内比较成熟的预防脑卒中可穿戴设备的企业主要有:北京雪扬人工智能科技公司、天津九安医疗电子股份有限公司、河南民生智能医疗技术股份有限公司、美国强生中国分公司等,但目前国内这些智能穿戴设备对于预防脑卒中数据的采集大多是让用户穿戴设备几天后,进行用户生理信号数据采集然后建立疾病模型,实时性预警和精确度有待提升。
随着我国人民生活水平的提高,生活与饮食习惯的改变,致使脑卒中疾病发病率日趋渐增,它严重危害了中老年人生命健康,我国现有脑卒中疾病患者达300万之多,每年新增患者人数约100万左右,这给家庭和社会带来了严重的经济和精神负担。脑卒中疾病具有高发病率、高致残率和高死亡率,且至今缺乏有效的治疗手段,所以早预防、早发现、早治疗十分必要。
发明内容
有鉴于此,本发明的目的是针对现有技术的不足,提供一种脑卒中风险患者用预警系统,提前对脑卒中风险进行预警。
为达到上述目的,本发明采用以下技术方案:
一种脑卒中风险患者用预警系统,包括:
数据获取单元,用于获取患者的表征脑卒中发病风险的检测数据;
数据传输单元,用于将检测数据上传至存储和计算单元;
存储和计算单元,用于对检测数据进行存储和预处理;将预处理后检测数据输入脑卒中疾病深度学习模型,获取输出结果,所述输出结果为脑卒中发病概率;判断输出结果是否超过预定值;所述输出结果超过预定值时,输出报警信号;
显示单元,用于显示检测数据和对应的输出结果;
预警单元,用于接收并响应报警信号。
进一步的,对检测数据进行预处理的步骤包括:
采用小波分析算法提取数据到的脑电信号进行多尺度分解,实现对生理信号频率节律特征提取。
进一步的,所述脑卒中疾病深度学习模型的建立步骤包括:
将采集并进行预处理后的正常脑电信号以及各类脑卒中疾病脑电信号的节律特征信号作为训练集存储于服务器;
将训练数据直接输入到深度学习神经网络的输入层,经过各隐层变换和映射,直到到输出层;
采用临床医生诊断标注好的脑卒中疾病的脑电信号,进一步对整个多层学习模型的网络参数优化,在优化过程中进行网络权值更新,得到训练完成后的各类脑卒中疾病深度训练模型,作为脑卒中疾病的判断识别信号存储于服务器。
进一步的,对脑卒中发病概率进行等级划分,所述输出结果超过预定值时,存储和计算单元判断所述输出结果处于哪一等级,对不同等级的输出结果发出不同种类的报警信号。
本发明的有益效果是:
本发明通过传感设备对患者进行检测,获取检测数据并对其进行预处理,将预处理后的检测数据输入到脑卒中疾病深度学习模型中,获得脑卒中发病概率,脑卒中发病概率较大时,及时发出警告,提前对脑卒中进行预警,降低脑卒中的发病概率。
附图说明
图1为本发明第一种实施例的流程示意图;
图2为本发明第二种实施例的流程示意图。
具体实施方式
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述。显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。
一种脑卒中风险患者用预警系统,如图1所示,包括:
数据获取单元,用于获取一段时间(如3min)内患者的表征脑卒中发病风险的检测数据,数据获取单元为脑电帽,检测数据为脑电信号数据;
数据传输单元,采用4G移动通信网络或者WiFi将脑电帽采集的脑电信号数据传输到存储和计算单元、显示单元;
存储和计算单元,存储和计算单元为云服务器,云服务器首先对数据传输单元传输的检测数据进行存储,之后进行计算,计算的步骤为:
1)预处理,预处理的步骤为:
利用小波分析算法提取一段时间内的脑电信号的频率节律信息,并用正交小波基对采集到的脑电信号进行多尺度分解,实现对生理信号频率节律特征提取。
2)将预处理后的检测数据输入脑卒中疾病深度学习模型,获取输出结果,输出结果为脑卒中发病概率,云服务器将输出结果发送到显示单元上,其中,脑卒中疾病深度学习模型的建立方式为:
将采集并进行预处理后的正常脑电信号以及各类脑卒中疾病脑电信号的节律特征信号作为训练集存储于服务器;
将训练数据直接输入到深度学习神经网络的输入层,经过各隐层变换和映射,直到输出层;
采用临床医生诊断标注好的脑卒中疾病的脑电信号,进一步对整个多层学习模型的网络参数优化,在优化过程中进行网络权值更新,得到训练完成后的各类脑卒中疾病深度学习模型,作为脑卒中疾病的数据判断识别信号存储于服务器。
3)判断输出结果即脑卒中发病概率是否超过预定值。
不超过预定值时,云服务器不发送报警信号给预警单元,超过预定值时,云服务器发送报警信号给预警单元。
显示单元,显示单元为手机,手机接收数据传输单元传输的脑电帽的检测数据、云服务器发送的脑卒中风险发病概率并显示,帮助患者实时掌握个人健康数据;
预警单元,用于接收并响应报警信号,预警单元为手机,报警信号为短信,脑卒中发病概率超过预定值时,云服务器向手机发出发出短信,手机接收并显示短信,提醒患者注意身体状况。
实施例二
如图2所示为本发明的第二种实施例,对脑卒中发明概率进行等级划分,将风险概率划分为低等级、中等级和高等级,其中:
低等级的发病概率为21%~50%,中等级的发病概率为51%~80%,高等级发病率为81%~100%。
存储和计算单元判断输出结果超过设定值时,再判断输出结果位于哪一个等级,脑卒中发病概率处于低等级时,存储和计算单元向手机发出报警信号一,报警信号一为向仅对患者的手机发送短信,提醒患者预防发病;
脑卒中发病概率处于中等级时,存储和计算单元向手机发出报警信号二,报警信号二为同时向患者手机、事先存储在存储和计算单元内的相关人员的手机号码发送短信,提醒患者注意的同时提醒相关人员;
脑卒中发病概率处于高等级时,存储和计算单元向手机发出报警信号三,报警信号三为:向相关人员手机发出提示信息,患者手机自动呼叫急救电话,将患者的脑卒中发病率、所在的地址自动播报给接听电话的医护人员。
最后说明的是,以上实施例仅用以说明本发明的技术方案而非限制,本领域普通技术人员对本发明的技术方案所做的其他修改或者等同替换,只要不脱离本发明技术方案的精神和范围,均应涵盖在本发明的权利要求范围当中。
Claims (4)
1.一种脑卒中风险患者用预警系统,其特征在于,包括:
数据获取单元,用于获取患者的表征脑卒中发病风险的检测数据;
数据传输单元,用于将检测数据上传至存储和计算单元;
存储和计算单元,用于对检测数据进行存储和预处理;将预处理后检测数据输入脑卒中疾病深度学习模型,获取输出结果,所述输出结果为脑卒中发病概率;判断输出结果是否超过预定值;所述输出结果超过预定值时,输出报警信号;;
显示单元,用于显示检测数据和对应的输出结果;
预警单元,用于接收并响应报警信号。
2.根据权利要求1所述的一种脑卒中风险患者用预警系统,其特征在于,对检测数据进行预处理的步骤包括:
采用小波分析算法提取数据到的脑电信号进行多尺度分解,实现对生理信号频率节律特征提取。
3.根据权利要求1所述的一种脑卒中风险患者用预警系统,其特征在于,所述脑卒中疾病深度学习模型的建立步骤包括:
将采集并进行预处理后的正常脑电信号以及各类脑卒中疾病脑电信号的节律特征信号作为训练集存储于服务器;
将训练数据直接输入到深度学习神经网络的输入层,经过各隐层变换和映射,直到到输出层;
采用临床医生诊断标注好的脑卒中疾病的脑电信号,进一步对整个多层学习模型的网络参数优化,在优化过程中进行网络权值更新,得到训练完成后的各类脑卒中疾病深度训练模型,作为脑卒中疾病的判断识别信号存储于服务器。
4.根据权利要求1所述的一种脑卒中风险患者用预警系统,其特征在于,对脑卒中发病概率进行等级划分,所述输出结果超过预定值时,存储和计算单元判断所述输出结果处于哪一等级,对不同等级的输出结果发出不同种类的报警信号。
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CN113270196A (zh) * | 2021-05-25 | 2021-08-17 | 郑州大学 | 一种脑卒中复发风险感知与行为决策模型构建系统及方法 |
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CN113421654A (zh) * | 2021-07-12 | 2021-09-21 | 军事科学院系统工程研究院卫勤保障技术研究所 | 创伤后失血性休克动态早期预警深度学习系统 |
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