Wang Meiqing, Youssef Ali, Larsen Mona, Rault Jean-Loup, Berckmans Daniel, Marchant-Forde Jeremy N, Hartung Joerg, Bleich André, Lu Mingzhou, Norton Tomas
Faculty of Bioscience Engineering, Katholieke Universiteit Leuven (KU LEUVEN), 3001 Heverlee/Leuven, Belgium.
Institute of Animal Welfare Science (ITT), University of Veterinary Medicine (Vetmeduni) Vienna, A-1210 Vienna, Austria.
Animals (Basel). 2021 Feb 8;11(2):442. doi: 10.3390/ani11020442.
Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism's state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm. The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in . The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.
心率（HR）是一种重要的生物信号，使用接触式传感器相对容易监测，并且与生物体的健康、压力和幸福状态相关。本研究的目的是开发一种算法，从原始视频数据中提取麻醉猪和静息猪的心率（每分钟心跳数），作为朝着持续监测猪的健康和福利迈出的第一步。数据来自两个实验，其中猪在佩戴心电图（ECG）监测系统作为金标准（GS）的同时进行视频记录。为了开发该算法，本研究使用带通滤波器去除噪声。然后，通过评估不同的窗口大小和窗口函数来测试短时傅里叶变换（STFT）方法，以准确识别心率。所得算法首先在心率相对恒定的麻醉猪视频上进行测试。麻醉猪的金标准心率测量值平均值为71.76次/分钟，标准差（SD）为3.57次/分钟。所开发的算法平均绝对误差（MAE）为2.33次/分钟，均方根误差（RMSE）为3.09次/分钟，心率估计误差低于3.5次/分钟的比例为67%。然后在未麻醉的静息猪视频上测试该算法的灵敏度，因为处于这种状态的动物心率波动比麻醉猪更大，同时由于静息，运动伪影仍可降至最低。静息猪的金标准心率测量值平均值为161.43次/分钟，标准差为10.11次/分钟。视频提取的心率平均绝对误差为4.69次/分钟，均方根误差为6.43次/分钟，[此处原文缺失部分内容]为57%。结果表明，仅使用视频信号的绿色通道进行心率监测比使用三个颜色通道更好，这降低了计算复杂度。通过比较不同的感兴趣区域（ROI），发现腹部周围区域在生理上比面部和前腿部更好。总之，基于视频数据开发的算法有潜力用于非接触式心率测量，并可应用于静息猪，以实时监测它们的健康和福利状况，这对兽医和农民具有重要意义。
