《心电与循环》杂志2017年第一、二期连续两期报道了国内学者对心电图诊断心力衰竭方法长达15页的初步研究 中文 报告 ：提出了凭心电图“掉头”现象诊断心力衰竭的心电图依据（作者：贾卫滨 肖印武；贾卫滨 兼任中华医学会心血管病分会第八届肺血管病专业学组委员 ）。 心电图技术诞生已逾百年，其对于提示心律失常、心肌缺血等疾病的诊断已有成熟经验，但通过心电图诊断心力衰竭的观点国内外往往持否定态度。 《 2016 ESC 急、慢性心力衰竭诊断和治疗指南 》 对于心电图与心力衰竭的关联也仅做了这样描述：An abnormal electrocardiogram(ECG) increases the likelihood of the diagnosis of HF ,but has low specificity（心电图异常可提高心力衰竭诊断的概率，但特异性低）。 2017 年第1期讲解心电图r波递增不良新概念，诊断左心衰、右心衰。第2期讲解心电图诊断全心衰等。感兴趣者可以在中国知网（ http://mall.cnki.net/magazine/Article/XDXZ201701013.htm ； http://mall.cnki.net/magazine/Article/XDXZ201702022.htm ）、万 方、维普 等数据库下载阅读、讨论。 附《心电与循环》杂志编者按及论文摘要： 编者按 探讨心电图能否判断心力衰竭存在这个话题具有一定的现实意义，有关通过心电图可以提示心力衰竭的观点鲜有报道。本文观点新，有一定的临床意义，尤其在心电图数字化存储、信息化管理时代，心电时间线性记录或许能够给心电技术带来新的诊治评估指标。本研究有助于鉴别心源性抑或肺源性导致的呼吸困难，对于广大临床工作者诊断心力衰竭具有参考意义。 本研究选择2013 年10 月至2015 年12 月胸闷、气喘患者，收集其心电图、超声心动图、N 末端脑钠肽前体（NT-proBNP）等检查资料，提出心力衰竭的心电图特点，即胸导联r 波递增不良新概念：“掉头”现象，以及“掉头延迟”、“掉头迟钝”等规律，以期是对于Zema提出的R 波递增不良概念涉及到临床意义的进一步延伸解释。 参考文献 贾卫滨，肖印武.r波递增不良新概念：“掉头”现象——通过心电图诊断心力 衰竭探讨（1） .心电与循环，2017,36（1）：44-49,57. 贾卫滨，肖印武.r波递增不良新概念：“掉头”现象——通过心电图诊断心力 衰竭探讨（2） .心电与循环，2017,36（2）：117-124. Ponikowski P, Voors A A, Anker S D, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology(ESC) Developed with the special contribution of the Heart Failure Association (HFA) of the ESC . Eur Heart J, 2016, 37(27): 2129-2200.
最近我们另外一片关于心电压缩传感的文章也被IEEE T-BME接收了。文章如下： Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Non-Invasive Fetal ECG via Block Sparse Bayesian Learning , by Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Bhaskar D. Rao, accepted by IEEE Trans. Biomedical Engineering. 文章下载地址: http://arxiv.org/abs/1205.1287 Matlab代码下载地址: http://dsp.ucsd.edu/~zhilin/BSBL.html , or https://sites.google.com/site/researchbyzhang/bsbl 关于心电图的压缩传感多说两句： Note that there are two groups of works on compressed sensing of ECG. One is the ECG compression (just like video compression, image compression, etc). Most works actually belong to this group. They generally use some MIT-BIH datasets, which are very clean (noise is removed). Another group is compressed sensing of ECG for energy-efficient wireless telemonitoring . There are only few works in this group. Our work belongs to this group. In this group the ECG data is always contaminated by noise and artifacts ('signal noise'). This is because the goal of telemonitoring is to allow people to walk and even exercise freely, and thus strong noise and artifacts caused by muscle and electrode movement are not evitable. Consequently, the raw ECG recordings are not sparse in the time domain and also not sparse in the transformed domains (e.g. the wavelet domain, the DCT domain). However, the strict constraint on energy consumption (and design issues, etc) of telemonitoring systems does not encourage filtering or other preprocessing before compression. Or, put in another way, if energy consumption and design issues are not problems, CS may have no advantages over traditional methods. Thus, CS algorithms have to recover non-sparse signals for this application. It turns out that the problem is very challenging. Our work not only solves this challenging problem, but also has some interesting mathematical meanings: By linear algebra, there are infinite solutions to the underdetermined problem y=Ax. When the true solution x0 is sparse, using CS algorithms it is possible to find it. But when the true solution x0 is non-sparse, finding it is more challenging and new constraints/assumptions are called for. This work shows that when exploiting the unknown block structure and the intra-block correlation of x0, it is possible to find a solution x_est which is very close to the true solution x0. These findings raise new and interesting possibilities for signal compression as well as theoretical questions in the subject of sparse and non-sparse signal recovery from a small number of measurements y. 文章的摘要如下: Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as non-sparsity and strong noise contamination, current CS algorithms generally fail in this application. This work proposes to use the block sparse Bayesian learning (BSBL) framework to compress/reconstruct non-sparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.
标签: 心电图
