Luo Jiasai, Zhang Guo, Su Yiwei, Lu Yi, Pang Yu, Wang Yuanfa, Wang Huiqian, Cui Kunfeng, Jiang Yuhao, Zhong Lisha, Huang Zhiwei
Chongqing Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing, China.
School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.
Front Cardiovasc Med. 2022 Jul 25;9:930745. doi: 10.3389/fcvm.2022.930745. eCollection 2022.
Cardiovascular disease not only occurs in the elderly but also tends to become a common social health problem. Considering the fast pace of modern life, quantified heart rate variability (HRV) indicators combined with the convenience of wearable devices are of great significance for intelligent telemedicine. To quantify the changes in human mental state, this article proposes an improved differential threshold algorithm for R-wave detection and recognition of electrocardiogram (ECG) signals.
HRV is a specific quantitative indicator of autonomic nerve regulation of the heart. The recognition rate is increased by improving the starting position of R wave and the time-window function of the traditional differential threshold method. The experimental platform is a wearable sign monitoring system constructed based on body area networks (BAN) technology. Analytic hierarchy process (AHP) is used to construct the mental stress assessment model, the weight judgment matrix is constructed according to the influence degree of HRV analysis parameters on mental stress, and the consistency check is carried out to obtain the weight value of the corresponding HRV analysis parameters.
Experimental results show that the recognition rate of R wave of real-time 5 min ECG data collected by this algorithm is >99%. The comprehensive index of HRV based on weight matrix can greatly reduce the deviation caused by the measurement error of each parameter. Compared with traditional characteristic wave recognition algorithms, the proposed algorithm simplifies the process, has high real-time performance, and is suitable for wearable analysis devices with low-configuration requirements.
Our algorithm can describe the mental stress of the body quantitatively and meet the requirements of application demonstration.
心血管疾病不仅在老年人中出现，而且有成为常见社会健康问题的趋势。考虑到现代生活的快节奏，结合可穿戴设备便利性的量化心率变异性（HRV）指标对智能远程医疗具有重要意义。为了量化人类心理状态的变化，本文提出了一种改进的差分阈值算法用于心电图（ECG）信号的R波检测与识别。
HRV是心脏自主神经调节的一项特定量化指标。通过改进R波起始位置和传统差分阈值法的时间窗函数提高识别率。实验平台是基于体域网（BAN）技术构建的可穿戴体征监测系统。采用层次分析法（AHP）构建心理压力评估模型，根据HRV分析参数对心理压力的影响程度构建权重判断矩阵，并进行一致性检验以获得相应HRV分析参数的权重值。
实验结果表明，该算法采集的实时5分钟ECG数据的R波识别率>99%。基于权重矩阵的HRV综合指标可大大降低各参数测量误差引起的偏差。与传统特征波识别算法相比，该算法简化了流程，具有较高的实时性能，适用于低配置要求的可穿戴分析设备。
我们的算法能够定量描述身体的心理压力，满足应用示范的要求。
