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

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

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

BACKGROUND AND OBJECTIVES: Wrist-worn wearable devices provide heart rate (HR) monitoring function via photoplethysmography technology. Recently, these devices have been used by patients to measure the HR when palpitation occurs, but few validation studies of these instruments have been conducted. We assessed the accuracy of these devices for measuring a HR. METHODS: This study enrolled 51 consecutive patients with a history of paroxysmal supraventricular tachyarrhythmia (SVT) or paroxysmal palpitations who were scheduled to undergo an electrophysiological study (EPS). Three devices were assessed: Apple Watch Series 2 (Apple), Samsung Galaxy Gear S3 (Galaxy), and Fitbit Charge 2 (Fitbit). Patients were randomly assigned to wear 2 different devices. The HR at baseline and induced SVT were measured during the EPS. After successful ablation of SVT, HR measurements was also done during atrial and ventricular pacing study. RESULTS: The mean patient age was 44.4±16.6 years and 27 patients were male (53%). The accuracy (within ±5 beats per minute [bpm] of an electrocardiogram [ECG] measurement) of the baseline HR measurements was 100%, 100%, and 94%, for Apple, Galaxy, and Fitbit, respectively. The HR during induced SVT ranged from 108 bpm to 228 bpm and the accuracy (within ±10 bpm of an ECG) was 100%, 90%, and 87% for the Apple, Galaxy, and Fitbit, respectively. During pacing study, accuracy of these devices was also acceptable but tended to decrease as the HR increased, and showed differences between the devices. CONCLUSIONS: Wrist-worn wearable devices accurately measure baseline and induced SVT HR. TRIAL REGISTRATION: Clinical Research Information Service Identifier: KCT0002282
Electrocardiography ; Galaxies ; Heart Rate ; Heart ; Humans ; Information Services ; Male ; Photoplethysmography ; Tachycardia ; Tachycardia, Supraventricular

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

OBJECTIVE: To evaluate the accuracy of a smartphone application measuring heart rates (HRs), during an exercise and discussed clinical potential of the smartphone application for cardiac rehabilitation exercise programs. METHODS: Patients with heart disease (14 with myocardial infarction, 2 with angina pectoris) were recruited. Exercise protocol was comprised of a resting stage, Bruce stage II, Bruce stage III, and a recovery stage. To measure HR, subjects held smartphone in their hands and put the tip of their index finger on the built-in camera for 1 minute at each exercise stage such as resting stage, Bruce stage II, Bruce stage III, and recovery stage. The smartphones recorded photoplethysmography signal and HR was calculated every heart beat. HR data obtained from the smartphone during the exercise protocol was compared with the HR data obtained from a Holter electrocardiography monitor (control). RESULTS: In each exercise protocol stage (resting stage, Bruce stage II, Bruce stage III, and the recovery stage), the HR averages obtained from a Holter monitor were 76.40±12.73, 113.09±14.52, 115.64±15.15, and 81.53±13.08 bpm, respectively. The simultaneously measured HR averages obtained from a smartphone were 76.41±12.82, 112.38±15.06, 115.83±15.36, and 81.53±13 bpm, respectively. The intraclass correlation coefficient (95% confidence interval) was 1.00 (1.00–1.00), 0.99 (0.98–0.99), 0.94 (0.83–0.98), and 1.00 (0.99–1.00) in resting stage, Bruce stage II, Bruce stage III, and recovery stage, respectively. There was no statistically significant difference between the HRs measured by either device at each stage (p>0.05). CONCLUSION: The accuracy of measured HR from a smartphone was almost overlapped with the measurement from the Holter monitor in resting stage and recovery stage. However, we observed that the measurement error increased as the exercise intensity increased.
Electrocardiography, Ambulatory ; Fingers ; Hand ; Heart Diseases ; Heart Rate* ; Heart* ; Humans ; Male* ; Myocardial Infarction ; Myocardial Ischemia* ; Photoplethysmography ; Rehabilitation ; Smartphone*