Jain Sanjeev Kumar, Bhaumik Basabi
IEEE Trans Biomed Circuits Syst. 2017 Apr;11(2):314-323. doi: 10.1109/TBCAS.2016.2592382. Epub 2016 Sep 26.
A novel disease diagnostic algorithm for ECG signal processing based on forward search is implemented in Application Specific Integrated Circuit (ASIC) for cardiovascular disease diagnosis on smartphone. An ASIC is fabricated using 130-nm CMOS low leakage process technology. The area of our PQRST ASIC is 1.21 mm. The energy dissipation of PQRST ASIC is 96 pJ with a supply voltage of 0.9 V. The outputs from the ASIC are fed to an Android application that generates diagnostic report and can be sent to a cardiologist via email. The ASIC and Android application are verified for the detection of bundle branch block, hypertrophy, arrhythmia and myocardial infarction using Physionet PTB diagnostic ECG database. The failed detection rate is 0.69%, 0.69%, 0.34% and 1.72% for bundle branch block, hypertrophy, arrhythmia and myocardial infarction respectively. The AV block is detected in all the three patients in the Physionet St. Petersburg arrhythmia database. Our proposed ASIC together with our Android application is the most suitable for an energy efficient wearable cardiovascular disease detection system.
一种基于前向搜索的用于心电图信号处理的新型疾病诊断算法在专用集成电路（ASIC）中实现，用于智能手机上的心血管疾病诊断。该ASIC采用130纳米CMOS低泄漏工艺技术制造。我们的PQRST ASIC面积为1.21平方毫米。PQRST ASIC在0.9伏电源电压下的能量耗散为96皮焦。ASIC的输出被输入到一个安卓应用程序中，该程序生成诊断报告并可通过电子邮件发送给心脏病专家。使用Physionet PTB诊断心电图数据库对ASIC和安卓应用程序进行了束支传导阻滞、肥厚、心律失常和心肌梗死检测的验证。束支传导阻滞、肥厚、心律失常和心肌梗死的漏检率分别为0.69%、0.69%、0.34%和1.72%。在Physionet圣彼得堡心律失常数据库的所有三名患者中均检测到了房室传导阻滞。我们提出的ASIC与我们的安卓应用程序最适合用于节能型可穿戴心血管疾病检测系统。
