In this paper, we use imaging photoplethysmography (IPPG) to realize non-contact measurement of blood volume change of human fingertip, which can avoid distortion of blood vessel wall caused by pressure applied to fingertip. We use CMOS color camera to collect signals and white LED as light source. In the process of signal processing, we abandon the traditional morphological filtering algorithm in the form of double-layer cascade, and use single-layer morphological filtering algorithm. Experiments show that the single-layer morphological filtering algorithm has a good effect of eliminating baseline drift of signals, and can perfectly retain the detail components of signals without shifting the transverse components. We proposed a peak-to-valley value detection algorithm to calculate the heart rate by detecting the time interval between the adjacent peaks value. The experiment compared the accuracy of calculating the heart rate by using the traditional fast Fourier transform and the heart rate based on peak-to-valley value detection. The respiration rate was detected by using the third-order Butterworth filter. The accuracy of heart rate monitoring can be achieved at 97.86% and the accuracy of respiration monitoring can be achieved at 95.02%.
In recent years, the number of patients with hypertension has increased. Hypertension is an invisible killer. Long-term hypertension can cause a series of cardiovascular diseases such as angina pectoris, stroke, and heart failure. Therefore, early evaluation and grade assessment of blood pressure (BP) are essential to human health. The seventh report of the National Joint Committee for the Prevention, Detection, Evaluation, and Treatment of Hypertension in the United States (JNC7) classified BP levels into normotension (NT), prehypertension (PHT) and hypertension (HT). In this paper, we adopted a deep learning model (ResNet18) based on the ensemble empirical mode decomposition (EEMD) and the Hilbert Transform (HT) to predict the risk level of BP only using photoplethysmography (PPG) signals. We collected 582 data records from the Multiparameter Intelligent Monitoring in Intensive Care database (MIMIC), and each file contained arterial BP signals as the labels for inputs and the corresponding PPG signals as the inputs. Besides, the last fully connected layer of the model was initialized. We conducted three classification experiments: HT vs. NT, HT vs. PHT, and (HT + PHT) vs. NT, the F1 score of these three classification experiments is 88.03%, 70.94%, and 84.88%, respectively. A quick and accessible noninvasive BP evaluation method was offered to low- and middle-income countries.
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