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 often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed a new PPG signal quality assessment by fusing multi-class features with multi-scale series information to address the problems of traditional machine learning methods with low accuracy and deep learning methods requiring a large number of samples for training. The multi-class features were extracted to reduce the dependence on the number of samples, and the multi-scale series information was extracted by a multi-scale convolutional neural network and bidirectional long short-term memory to improve the accuracy. The proposed method obtained the highest accuracy of 94.21%. It showed the best performance in all sensitivity, specificity, precision, and F1-score metrics, compared with 6 quality assessment methods on 14 700 samples from 7 experiments. This paper provides a new method for quality assessment in small samples of PPG signals and quality information mining, which is expected to be used for accurate extraction and monitoring of clinical and daily PPG physiological information.
Photoplethysmography ; Machine Learning ; Neural Networks, Computer

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. Early diagnosis and effective management are important to reduce atrial fibrillation-related adverse events. Photoplethysmography (PPG) is often used to assist wearables for continuous electrocardiograph monitoring, which shows its unique value. The development of PPG has provided an innovative solution to AF management. Serial studies of mobile health technology for improving screening and optimized integrated care in atrial fibrillation have explored the application of PPG in screening, diagnosing, early warning, and integrated management in patients with AF. This review summarizes the latest progress of PPG analysis based on artificial intelligence technology and mobile health in AF field in recent years, as well as the limitations of current research and the focus of future research.
Humans ; Atrial Fibrillation/therapy* ; Photoplethysmography ; Artificial Intelligence ; Electrocardiography ; Biomedical Technology

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

Breathing is of great significance in the monitoring of patients with obstructive sleep apnea hypopnea syndrome, perioperative monitoring and intensive care. In this study, a respiratory monitoring and verification system based on optical capacitance product pulse wave (PPG) is designed, which can synchronously collect human PPG signals. Through algorithm processing, the characteristic parameters of PPG signal are calculated, and the respiratory signal and respiratory frequency can be extracted in real time. In order to verify the accuracy of extracting respiratory signal and respiratory rate by the algorithm, the system adds the nasal airflow respiratory signal acquisition module to synchronously collect the nasal airflow respiratory signal as the standard signal for comparison and verification. Finally, the root mean square error between the respiratory rate extracted by the algorithm from the pulse wave and the standard respiratory rate is only 1.05 times/min.
Algorithms ; Electrocardiography ; Heart Rate ; Humans ; Photoplethysmography ; Respiration ; Respiratory Rate ; Signal Processing, Computer-Assisted ; Sleep Apnea, Obstructive

Oxygen saturation and respiratory signals are important physiological signals of human body, respiratory monitoring plays an important role in clinical and daily life. A system was established to extract respiratory signals from photoplethysmography in this study. Including the collection of pulse wave signal, the extraction of respiratory signal, and the calculation of respiratory rate and pulse rate transmitted from the slave computer to the host computer in real time.
Heart Rate ; Humans ; Monitoring, Physiologic ; Photoplethysmography ; Respiratory Rate ; Signal Processing, Computer-Assisted

Emotion is a series of reactions triggered by a specific object or situation that affects a person's physiological state and can, therefore, be identified by physiological signals. This paper proposes an emotion recognition model. Extracted the features of physiological signals such as photoplethysmography, galvanic skin response, respiration amplitude, and skin temperature. The SVM-RFE-CBR(Recursive Feature Elimination-Correlation Bias Reduction-Support Vector Machine) algorithm was performed to select features and support vector machines for classification. Finally, the model was implemented on the DEAP dataset for an emotion recognition experiment. In the rating scale of valence, arousal, and dominance, the accuracy rates of 73.5%, 81.3%, and 76.1% were obtained respectively. The result shows that emotional recognition can be effectively performed by combining a variety of physiological signals.
Arousal ; Emotions ; Galvanic Skin Response ; Humans ; Photoplethysmography ; Support Vector Machine

OBJECTIVE: To study the effect of bronchopulmonary dysplasia (BPD) on lung function in preterm infants. METHODS: According to the presence/absence or the severity of BPD, 72 preterm infants were divided into non-BPD group (n=44), mild BPD group (n=15) and moderate BPD group (n=13). Lung function was assessed by plethysmography on days 7, 14 and 28 after birth. RESULTS: The preterm infants in the three groups had gradual increases in tidal volume per kilogram (TV/kg), functional residual capacity (FRC), ratio of time to peak tidal expiratory flow to total expiratory time (%T-PF) and ratio of volume to peak tidal expiratory flow to total expiratory volume (%V-PF) on days 7, 14 and 28 after birth, while there were gradual reductions in effective airway resistance per kilogram (Reff/kg) and respiratory rate (RR) (P<0.05). Compared with the non-BPD group on days 7, 14 and 28 after birth, the mild and moderate BPD groups had significantly lower TV/kg, FRC, %T-PF, and %V-PF and significantly higher Reff/kg and RR (P<0.05). On day 7 after birth, the moderate BPD group had significantly higher airway resistance, Reff/kg and FRC/kg than the mild BPD group (P<0.05). CONCLUSIONS There is a certain degree of pulmonary function impairment in preterm infants with BPD. Dynamic monitoring of lung function by plethysmography is useful for assessing lung development in the neonatal period in these infants.
Bronchopulmonary Dysplasia ; Humans ; Infant, Newborn ; Infant, Premature ; Lung ; Plethysmography ; Respiratory Function Tests

A photoplethysmograph (PPG) is a simple medical device for monitoring blood fl ow and transportation of substances in the blood. It consists of a light source and a photodetector for measuring transmitted and refl ected light signals. Clinically, PPGs are used to monitor the pulse rate, oxygen saturation, blood pressure, and blood vessel stiff ness. Wearable unobtrusive PPG monitors are commercially available. Here, we review the principle issues and clinical applications of PPG for monitoring oxygen saturation.
Blood Pressure ; Blood Vessels ; Heart Rate ; Oxygen ; Photoplethysmography ; Respiratory Rate ; Transportation

BACKGROUND: The assessment of intravascular volume status is very important especially in children during anesthesia. Pulse pressure variation (PPV) and pleth variability index (PVI) are well known parameters for assessing intravascular volume status and fluid responsiveness. We compared PPV and PVI for children aged less than two years who underwent surgery in the prone position. METHODS: A total of 27 children were enrolled. We measured PPV and PVI at the same limb during surgery before and after changing the patients’ position from supine to prone. We then compared PPV and PVI at each period using Bland-Altman plot for bias between the two parameters and for any correlation. We also examined the difference between before and after the position change for each parameter, along with peak inspiratory pressure, heart rate and mean blood pressure. RESULTS: The bias between PPV and PVI was −2.2% with a 95% limits of agreement of −18.8% to 14.5%, not showing significant correlation at any period. Both PPV and PVI showed no significant difference before and after the position change. CONCLUSIONS: No significant correlation between PVI and PPV was observed in children undergoing surgery in the prone position. Further studies relating PVI, PPV, and fluid responsiveness via adequate cardiac output estimation in children aged less than 2 years are required.
Anesthesia ; Arterial Pressure ; Bias (Epidemiology) ; Blood Pressure ; Cardiac Output ; Child ; Extremities ; Fluid Therapy ; Heart Rate ; Humans ; Plethysmography ; Prone Position