Pal Poulomi, Ghosh Sudipta, Chattopadhyay Bhabani Prasad, Kumar Saha Kalyan, Mahadevappa Manjunatha
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5980-5983. doi: 10.1109/EMBC44109.2020.9176447.
The increasing rate of cardiac ailments has led to the rise in the scrutinization of ones cardiac health. The prevalent techniques for detecting heart diseases are costly and require expert supervision as well as modern equipment. Thus there is a need for an alternative low cost and easily available technique. Finger-tip photoplethysmography (PPG) signals can be used for identifying Ischemic Heart Disease (IHD). This technique of screening the disease will be very helpful to the inhabitants of remote, underdeveloped and unprivileged areas. Time-domain analysis of the signal was done for extracting different features. Segregation of diseased and healthy subjects was performed using Decision Trees, Discriminant Analysis, Logistic Regression, Support Vector Machine, KNN, and Boosted trees. Ten different performance metrics was studied using the confusion matrix. After analysis, the accuracy, sensitivity, specificity, and precision of 0.94, 0.95, 0.95 and 0.97 respectively was obtained using Boosted tress classifier. ROC and AUC were calculated to establish the robustness of the classification methods for determining IHD patients.
心脏病发病率的上升导致了对个人心脏健康检查的增加。目前检测心脏病的常用技术成本高昂，需要专家监督以及现代设备。因此，需要一种低成本且易于获得的替代技术。指尖光电容积脉搏波描记术（PPG）信号可用于识别缺血性心脏病（IHD）。这种疾病筛查技术将对偏远、欠发达和贫困地区的居民非常有帮助。对信号进行时域分析以提取不同特征。使用决策树、判别分析、逻辑回归、支持向量机、KNN和增强树对患病和健康受试者进行分类。使用混淆矩阵研究了十种不同的性能指标。经过分析，使用增强树分类器分别获得了0.94、0.95、0.95和0.97的准确率、灵敏度、特异性和精确率。计算了ROC和AUC以确定用于诊断IHD患者的分类方法的稳健性。
