Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1989-1992. doi: 10.1109/EMBC48229.2022.9871111.
Cardiac auscultation is the key exam to screen cardiac diseases both in developed and developing countries. A heart sound auscultation procedure can detect the presence of murmurs and point to a diagnosis, thus it is an important first-line assessment and also cost-effective tool. The design automatic recommendation systems based on heart sound auscultation can play an important role in boosting the accuracy and the pervasiveness of screening tools. One such as step, consists in detecting the fundamental heart sound states, a process known as segmentation. A faulty segmentation or a wrong estimation of the heart rate might result in an incapability of heart sound classifiers to detect abnormal waves, such as murmurs. In the process of understanding the impact of a faulty segmentation, several common heart sound segmentation errors are studied in detail, namely those where the heart rate is badly estimated and those where S1/S2 and Systolic/Diastolic states are swapped in comparison with the ground truth state sequence. From the tested algorithms, support vector machine (SVMs) and random forest (RFs) shown to be more sensitive to a wrong estimation of the heart rate (an expected drop of 6% and 8% on the overall performance, respectively) than to a swap in the state sequence of events (an expected drop of 1.9% and 4.6%, respectively).
心脏听诊是在发达国家和发展中国家筛查心脏疾病的关键检查。心脏听诊程序可以检测到杂音的存在，并指向诊断，因此它是一种重要的一线评估工具，也是具有成本效益的工具。基于心脏听诊的自动推荐系统的设计可以在提高筛查工具的准确性和普及性方面发挥重要作用。其中一步是检测基本心音状态，这一过程称为分段。分段错误或心率估计错误可能导致心音分类器无法检测到异常波，如杂音。在了解分段错误的影响的过程中，详细研究了几种常见的心脏声音分段错误，即心率估计错误和 S1/S2 与收缩/舒张状态与真实状态序列交换的错误。在所测试的算法中，支持向量机 (SVM) 和随机森林 (RF) 对心率估计错误更为敏感（整体性能分别下降 6%和 8%），而对事件状态序列的交换不敏感（分别下降 1.9%和 4.6%）。
