Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91058, Erlangen, Germany.
Department of Palliative Medicine, Universitätsklinikum Erlangen, Comprehensive Cancer Center CCC Erlangen - EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054, Erlangen, Germany.
Sci Rep. 2018 Jul 26;8(1):11551. doi: 10.1038/s41598-018-29984-5.
This paper introduces heart sound detection by radar systems, which enables touch-free and continuous monitoring of heart sounds. The proposed measurement principle entails two enhancements in modern vital sign monitoring. First, common touch-based auscultation with a phonocardiograph can be simplified by using biomedical radar systems. Second, detecting heart sounds offers a further feasibility in radar-based heartbeat monitoring. To analyse the performance of the proposed measurement principle, 9930 seconds of eleven persons-under-tests' vital signs were acquired and stored in a database using multiple, synchronised sensors: a continuous wave radar system, a phonocardiograph (PCG), an electrocardiograph (ECG), and a temperature-based respiration sensor. A hidden semi-Markov model is utilised to detect the heart sounds in the phonocardiograph and radar data and additionally, an advanced template matching (ATM) algorithm is used for state-of-the-art radar-based heartbeat detection. The feasibility of the proposed measurement principle is shown by a morphology analysis between the data acquired by radar and PCG for the dominant heart sounds S1 and S2: The correlation is 82.97 ± 11.15% for 5274 used occurrences of S1 and 80.72 ± 12.16% for 5277 used occurrences of S2. The performance of the proposed detection method is evaluated by comparing the F-scores for radar and PCG-based heart sound detection with ECG as reference: Achieving an F1 value of 92.22 ± 2.07%, the radar system approximates the score of 94.15 ± 1.61% for the PCG. The accuracy regarding the detection timing of heartbeat occurrences is analysed by means of the root-mean-square error: In comparison to the ATM algorithm (144.9 ms) and the PCG-based variant (59.4 ms), the proposed method has the lowest error value (44.2 ms). Based on these results, utilising the detected heart sounds considerably improves radar-based heartbeat monitoring, while the achieved performance is also competitive to phonocardiography.
本文介绍了通过雷达系统进行心音检测,实现了对心音的非接触式和连续监测。所提出的测量原理在现代生命体征监测方面有两个增强:首先,通过使用生物医学雷达系统,可以简化常见的基于触诊的听诊器听诊。其次,检测心音为基于雷达的心跳监测提供了进一步的可行性。为了分析所提出的测量原理的性能,使用多个同步传感器(连续波雷达系统、心音图(PCG)、心电图(ECG)和基于温度的呼吸传感器)在数据库中获取并存储了 11 名测试对象的 9930 秒生命体征数据。利用隐半马尔可夫模型(HSM)在 PCG 和雷达数据中检测心音,并使用先进的模板匹配(ATM)算法进行基于雷达的最新心跳检测。通过对雷达和 PCG 采集的主要心音 S1 和 S2 数据进行形态分析,证明了所提出的测量原理的可行性:S1 的相关系数为 82.97±11.15%,S2 的相关系数为 80.72±12.16%,共使用了 5274 和 5277 个心音。通过将雷达和 PCG 心音检测的 F 分数与 ECG 作为参考进行比较,评估了所提出的检测方法的性能:雷达系统的 F1 值为 92.22±2.07%,接近 PCG 的 94.15±1.61%。通过均方根误差(Root Mean Square Error,RMSE)分析检测心跳发生时间的准确性:与 ATM 算法(144.9 ms)和基于 PCG 的变体(59.4 ms)相比,所提出的方法具有最低的误差值(44.2 ms)。基于这些结果,利用检测到的心音可以显著提高基于雷达的心跳监测性能,同时所达到的性能也与心音图相当。