Standard instrument for the clinical diagnosis of sleep apnea is large and based on invasive method, which is not comfortable and not suitable for daily inspection. A video-based measurement method for the respiration rate (RR) is therefore proposed, which is meaningful to the home diagnosis of sleep apnea. We proposed a novel method for the visualization and calculation of RR from a video containing a sleeping person. The video was decomposed by spatio-temporal Laplacian pyramid method into multiresolution image sequences, which were filtered by an infinite-impulse-response bandpass filter to extract the respiration movement in the video. The respiration movement was amplified, and fused into the original video. On the other hand, the signal intensity of the filtering results was compared between layers of Laplacian pyramid to identify the layer with the strongest movement caused by respiration. A morphological calculation was conducted on the image reshaped from the filtered results in this layer, to find the region of interest (ROI) with most significant movement of respiration. The image intensity in the ROI was spatially averaged into a one-dimensional signal, of which the frequency domain was analyzed to obtain RR. The ROI and the calculation results for RR were visualized on the video with enhanced respiration movement. Ten videos lasting 30–60s were recorded by a general webcam. The respiration movement of the subject was successfully extracted and amplified, no matter the posture was supine or side lying. The thoracic and abdominal parts were generally identified as ROI in all postures. RR was calculated by the frequency domain analysis for the averaged image intensity in ROI with the error no more than 1 time per minute, and further, as well as ROI, was fused into the amplified video. The region of respiration movement and RR is calculated by the noncontact method, and well visualized in a video. The method provides a novel screening tool for the population suspected of sleep apnea, and is meaningful to the home diagnosis of sleep illness.

中文翻译：

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基于视频中呼吸运动放大的呼吸频率估计

临床诊断睡眠呼吸暂停的标准仪器体积大，采用有创方法，不舒适，不适合日常检查。因此提出了一种基于视频的呼吸率（RR）测量方法，对睡眠呼吸暂停的家庭诊断具有重要意义。我们提出了一种新方法，用于从包含睡着的人的视频中可视化和计算 RR。通过时空拉普拉斯金字塔方法将视频分解为多分辨率图像序列，并通过无限脉冲响应带通滤波器对其进行滤波，以提取视频中的呼吸运动。呼吸运动被放大，并融合到原始视频中。另一方面，过滤结果的信号强度在拉普拉斯金字塔各层之间进行比较，以确定由呼吸引起的运动最强的层。对该层中过滤结果重构的图像进行形态学计算，以找到呼吸运动最显着的感兴趣区域（ROI）。将 ROI 中的图像强度空间平均为一维信号，对其频域进行分析以获得 RR。ROI 和 RR 的计算结果在视频上以增强的呼吸运动可视化。十个视频持续 30-60 找到呼吸运动最显着的感兴趣区域（ROI）。将 ROI 中的图像强度空间平均为一维信号，对其频域进行分析以获得 RR。ROI 和 RR 的计算结果在视频上以增强的呼吸运动可视化。十个视频持续 30-60 找到呼吸运动最显着的感兴趣区域（ROI）。将 ROI 中的图像强度空间平均为一维信号，对其频域进行分析以获得 RR。ROI 和 RR 的计算结果在视频上以增强的呼吸运动可视化。十个视频持续 30-60s 由通用网络摄像头记录。受试者的呼吸运动被成功提取和放大，无论是仰卧位还是侧卧位。在所有姿势中，胸部和腹部部分通常被识别为 ROI。RR 是通过频域分析计算 ROI 中的平均图像强度，误差不超过每分钟 1 次，并且进一步与 ROI 一样，融合到放大的视频中。呼吸运动和 RR 的区域通过非接触方法计算，并在视频中很好地可视化。该方法为疑似睡眠呼吸暂停人群提供了一种新的筛查工具，对睡眠疾病的家庭诊断具有重要意义。