Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring. Remote photoplethysmography (rPPG) provides a non-contact method for measuring heart rate and other physiological indicators by analyzing blood volume pulse signals. This approach is non-invasive, does not require direct contact, and allows for long-term healthcare monitoring. Deep learning has emerged as a powerful tool for processing complex image and video data, and has been increasingly employed to extract heart rate signals remotely. This article reviewed the latest research advancements in rPPG-based heart rate measurement using deep learning, summarized available public datasets, and explored future research directions and potential advancements in non-contact heart rate measurement.
Humans ; Deep Learning ; Heart Rate/physiology* ; Photoplethysmography/methods* ; Video Recording ; Face ; Monitoring, Physiologic/methods* ; Signal Processing, Computer-Assisted

To address the current issues of data imbalance and scarcity in photoplethysmography (PPG) data for type 2 diabetes mellitus (T2DM) prediction, this study proposes an improved conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP). The algorithm integrated gated recurrent unit (GRU) networks and self-attention mechanisms to construct a generator, aiming to produce high-quality PPG signals. Various data augmentation methods, including the improved CWGAN-GP, were employed to expand the PPG dataset, and multiple classifiers were applied for T2DM prediction analysis. Experimental results showed that the model trained on data generated by the improved CWGAN-GP achieved the optimal prediction performance. The highest accuracy reached 0.895 0, and compared with other data enhancement methods, this approach exhibited significant advantages in terms of precision and F1-score. The generated data notably enhances the accuracy and generalization ability of T2DM prediction models, providing a more reliable technical basis for non-invasive early T2DM screening based on PPG signals.
Photoplethysmography/methods* ; Diabetes Mellitus, Type 2/diagnosis* ; Humans ; Algorithms ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Prediction Algorithms

OBJECTIVE: To use the low-cost anesthesia monitor for realizing anesthesia depth monitoring, effectively assist anesthesiologists in diagnosis and reduce the cost of anesthesia operation. METHODS: Propose a monitoring method of anesthesia depth based on artificial intelligence. The monitoring method is designed based on convolutional neural network (CNN) and long and short-term memory (LSTM) network. The input data of the model include electrocardiogram (ECG) and pulse wave photoplethysmography (PPG) recorded in the anesthesia monitor, as well as heart rate variability (HRV) calculated from ECG, The output of the model is in three states of anesthesia induction, anesthesia maintenance and anesthesia awakening. RESULTS: The accuracy of anesthesia depth monitoring model under transfer learning is 94.1%, which is better than all comparison methods. CONCLUSIONS The accuracy of this study meets the needs of perioperative anesthesia depth monitoring and the study reduces the operation cost.
Artificial Intelligence ; Neural Networks, Computer ; Heart Rate ; Electrocardiography ; Photoplethysmography/methods* ; Anesthesia

Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: -0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.
Algorithms ; Heart Rate/physiology* ; Photoplethysmography/methods* ; Signal Processing, Computer-Assisted ; Wearable Electronic Devices

Based on the imaging photoplethysmography (iPPG) and blind source separation (BSS) theory the author put forward a method for non-contact heartbeat frequency estimation. Using the recorded video images of the human face in the ambient light with Webcam, we detected the human face through software, separated the detected facial image into three channels RGB components. And then preprocesses i.e. normalization, whitening, etc. were carried out to a certain number of RGB data. After the independent component analysis (ICA)'theory and joint approximate diagonalization of eigenmatrices (JADE) algorithm were applied, we estimated the frequency of heart rate through spectrum analysis. Taking advantage of the consistency of Bland-Altman theory analysis and the commercial Pulse Oximetry Sensor test results, the root mean square error of the algorithm result was calculated as 2. 06 beat/min. It indicated that the algorithm could realize the non-contact measurement of heart rate and lay the foundation for the re- mote and non-contact measurement of multi-parameter physiological measurements.
Algorithms ; Face ; Heart Rate ; Humans ; Monitoring, Physiologic ; methods ; Oximetry ; Photoplethysmography ; Software ; Video Recording

The electro-optic technique of measuring the cardiovascular pulse wave, known as photoplethysmography (PPG), is clinically utilised for noninvasive characterisation of physiological components by dynamic monitoring of tissue optical absorption. Non-invasive PPG technology has been used in a wide range of individual, home or public health monitoring. The application of PPG has become one of the hot topics in the fields of biomedical engineering recently. This paper reviews the optical origins of PPG signal, the feature of PPG technology, the applications of PPG in physiological measurements and its development in the future.
Cardiovascular Physiological Phenomena ; Humans ; Monitoring, Physiologic ; methods ; Photoplethysmography ; instrumentation ; methods ; Signal Processing, Computer-Assisted

This paper proposes a new method of non-contact pulse measurement by analyzing a clip of human facial video. The method is based on photo plethysmography (PPG) and independent component analysis (ICA) model. A clip of color facial video shot under normal lighting condition is firstly discomposed into RGB channel sequences. Secondly, by applying ICA to the 3 channel sequences, 3 new independent signals are obtained, among which one signal is close to human pulse wave. Thus the pulse can be measured. In this paper, the principles of PPG and ICA are briefly described and the measurement framework is proposed. The experimental results showed that this novel approach was reasonable and feasible.
Algorithms ; Analysis of Variance ; Facies ; Humans ; Models, Statistical ; Photoplethysmography ; methods ; Principal Component Analysis ; methods ; Pulse ; Video Recording ; methods

PURPOSE: Photoplethysmography (PPG) is a noninvasive optical technology that detects changes in blood volume in the vascular system. This study aimed to investigate the possibilities of monitoring the cardiovascular system status by using PPG. MATERIALS AND METHODS: Forced hemodynamic changes were induced using cardiac stimulants; dopamine and epinephrine, and PPG components were recorded by a noninvasive method at the peripheral blood vessels. The results were compared among 6 dogs. Endotracheal intubation was performed after an intramuscular injection of 25 mg/kg ketamine sulfate, and anesthesia was maintained with 2% enflurane. After stabilizing the animals for 15 min, 16 mg/mL diluted dopamine was injected into a vein for 2 min at 20 microgram/kg.min(-1) by using an infusion pump. Thereafter, the infusion pump was stopped, and 1 mg epinephrine was injected intravenously. Fluid administration was controlled to minimize preload change in blood pressure. RESULTS: After stimulant administration, systolic blood pressure (SBP) and diastolic blood pressures (DBP) increased. The direct current (DC) component, which reflects changes in blood volume, decreased while the alternating current (AC) component, which reflects changes in vascular compliance and resistance, increased. The correlation coefficient between SBP and the foot of the DC component was 0.939 (p < 0.01), while it was 0.942 (p < 0.01) for DBP and the peak of the DC component. The AC component could predict the increase in vascular resistance from a stable pulse blood volume, even with increased pulse pressure. Conclusions: These results support the possibility that PPG components may be used for easy and noninvasive measurement of hemodynamic changes in the cardiovascular system.
Animals ; Blood Pressure/*physiology ; *Blood Pressure Monitors ; Dogs/*physiology ; Humans ; Models, Animal ; Photoplethysmography/*methods