In order to improve the storage and transmission efficiency of dynamic photoplethysmography (PPG) signals in the detection process and reduce the redundancy of signals, the modified adaptive matching pursuit (MAMP) algorithm was proposed according to the sparsity of the PPG signal. The proposed algorithm which is based on reconstruction method of sparse adaptive matching pursuit (SAMP), could improve the accuracy of the sparsity estimation of signals by using both variable step size and the double threshold conditions. After experiments on the simulated and the actual PPG signals, the results show that the modified algorithm could estimate the sparsity of signals accurately and quickly, and had good anti-noise performance. Contrasting with SAMP and orthogonal matching pursuit (OMP), the reconstruction speed of the algorithm was faster and the accuracy was high.
Algorithms ; Humans ; Image Processing, Computer-Assisted ; Photoplethysmography

In this paper, a reflection type photoelectric pulse wave sensor was designed for short-term pulse rate variability analysis. Photoplethysmography (PPG) signals and ECG signals (obtained with the Dimetek MicroECG recorder Dicare-m1CP) were recorded synchronously from 20 healthy subjects. The analytical results show a significant correlation (correlation coefficient r > 0.99) between the PPG-derived peak-to-peak (PP) intervals and the ECG-derived RR intervals. Besides, there are no significant differences (P > 0.05) between the HRV measured by ECG and the PRV quantified by the PPG whether in time domain, frequency domain, or the Poincaré plot parameters. The experimental results suggest that the PPG-based short-term PRV analysis can be consistent with the ECG-based HRV measurement in wearable smart devices.
Electrocardiography ; Heart Rate ; Humans ; Monitoring, Physiologic ; Photoplethysmography

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

OBJECTIVES: Acceleration plethysmograms (APGs) are obtained by taking the second derivative of photoplethysmograms (PPGs) and are noninvasive circulatory signals related to risk factors for atherosclerosis with age. There has been growing interest in the development of mobile devices to collect and analyze PPG single features for ambulatory health monitoring. The present study aimed to extract a new feature from the morphologies of APG and PPG signals to classify the dominant indices related to the pulsatile volume of blood in tissue according to age. METHODS: Ten APG and 14 PPG indices were simultaneously extracted. All indices were compared via Pearson correlation coefficients (r) and a regression analysis. We introduced a combined index extracted from both the PPG and APG indices defined as the inflection point area plus the d_peak (IPAD). The participants included 93 healthy adults aged 36–86 years with a mean ± standard deviation age of 57.43 ± 11.99 years. RESULTS: The d_peak and age index for the APG indices were significantly correlated with age (r = −0.408, p < 0.0001 and r = 0.296, p = 0.0039, respectively). Only the A1 time for PPG indices was moderately correlated with age (r = −0.247, p = 0.017). The stiffness index, including individual height information, was not related to age (r = −0.031, p = 0.7713). However, the combined IPAD index was significantly more correlated with age (r = 0.56, p < 0.001) than the other indices. CONCLUSIONS: The proposed index outperformed the other 24 indices for evaluating vascular aging. We suggest that the IPAD is a significant factor related to the clinical information embedded in the PPG waveform.
Acceleration* ; Adult ; Aging* ; Atherosclerosis ; Humans ; Photoplethysmography ; Risk Factors ; Vascular Stiffness

In comparison with the measurement of heart rate variability from ECG and from photoplethysmographic signal from 46 healthy adults in their spontaneous breathing state. The beat-to-beat intervals in ECG and pulse-to-pulse intervals in photoplethysmographic signal are extracted, and then the parameters of heart rate variability are calculated. Three kinds of algorithms are chosen to get the pulse-to-pulse intervals, which are the intervals of maximum of second derivative, the maximum of PPG signal and the tangent intersection. The results show that the correlation coefficients of the HRV parameters in the two calculation methods are highly correlated. The Bland-Altman scattered plots show the relative bias results from the algorithm of the maximum of PPG signal are smallest and singular points that deviate from the consistent limits are the least compared with the other two algorithms.
Adult ; Algorithms ; Electrocardiography ; Heart Rate ; Humans ; Photoplethysmography ; Reproducibility of Results

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

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

OBJECTIVES: Biosignal data include important physiological information. For that reason, many devices and systems have been developed, but there has not been enough consideration of how to collect and integrate raw data from multiple systems. To overcome this limitation, we have developed a system for collecting and integrating biosignal data from two patient monitoring systems. METHODS: We developed an interface to extract biosignal data from Nihon Kohden and Philips monitoring systems. The Nihon Kohden system has a central server for the temporary storage of raw waveform data, which can be requested using the HL7 protocol. However, the Philips system used in our hospital cannot save raw waveform data. Therefore, our system was connected to monitoring devices using the RS232 protocol. After collection, the data were transformed and stored in a unified format. RESULTS: From September 2016 to August 2017, we collected approximately 117 patient-years of waveform data from 1,268 patients in 79 beds of five intensive care units. Because the two systems use the same data storage format, the application software could be run without compatibility issues. CONCLUSIONS: Our system collects biosignal data from different systems in a unified format. The data collected by the system can be used to develop algorithms or applications without the need to consider the source of the data.
Electrocardiography ; Humans ; Information Storage and Retrieval ; Intensive Care Units ; Monitoring, Physiologic* ; Photoplethysmography

OBJECTIVES: Recent studies have emphasized the potential information embedded in peripheral fingertip photoplethysmogram (PPG) signals for the assessment of arterial wall stiffening during aging. For the discrimination of arterial stiffness with age, the brachial-ankle pulse wave velocity (baPWV) has been widely used in clinical applications. The second derivative of the PPG (acceleration photoplethysmogram [APG]) has been reported to correlate with the presence of atherosclerotic disorders. In this study, we investigated the association among age, the baPWV, and the APG and found a new aging index reflecting arterial stiffness for a healthcare device. METHODS: The APG and the baPWV were simultaneously applied to assess the accuracy of the APG in measuring arterial stiffness in association with age. A preamplifier and motion artifact removal algorithm were newly developed to obtain a high quality PPG signal. In total, 168 subjects with a mean +/- SD age of 58.1 +/- 12.6 years were followed for two months to obtain a set of complete data using baPWV and APG analysis. RESULTS: The baPWV and the B ratio of the APG indices were correlated significantly with age (r = 0.6685, p < 0.0001 and r = -0.4025, p < 0.0001, respectively). A regression analysis revealed that the c and d peaks were independent of age (r = -0.3553, p < 0.0001 and r = -0.3191, p < 0.0001, respectively). CONCLUSIONS: We determined the B ratio, which represents an improved aging index and suggest that the APG may provide qualitatively similar information for arterial stiffness.
Aging* ; Artifacts ; Delivery of Health Care* ; Discrimination (Psychology) ; Photoplethysmography* ; Pulse Wave Analysis* ; Vascular Stiffness

OBJECTIVES: Acceleration plethysmogram (APG) obtained from the second derivative of photoplethysmography (PPG) is used to predict risk factors for atherosclerosis with age. This technique is promising for early screening of atherosclerotic pathologies. However, extraction of the wave indices of APG signals measured from the fingertip is challenging. In this paper, the development of a wave detection algorithm including a preamplifier based on a microcontroller that can detect the a, b, c, and d wave indices is proposed. METHODS: The 4th order derivative of a PPG under real measurements of an APG waveform was introduced to clearly separate the components of the waveform, and to improve the rate of successful wave detection. A preamplifier with a Sallen-Key low pass filter and a wave detection algorithm with programmable gain control, mathematical differentials, and a digital IIR notch filter were designed. RESULTS: The frequency response of the digital IIR filter was evaluated, and a pulse train consisting of a specific area in which the wave indices existed was generated. The programmable gain control maintained a constant APG amplitude at the output for varying PPG amplitudes. For 164 subjects, the mean values and standard deviation of the a wave index corresponding to the magnitude of the APG signal were 1,106.45 and +/-47.75, respectively. CONCLUSIONS: We conclude that the proposed algorithm and preamplifier designed to extract the wave indices of an APG in real-time are useful for evaluating vascular aging in the cardiovascular system in a simple healthcare device.
Acceleration* ; Aging ; Atherosclerosis ; Cardiovascular System ; Delivery of Health Care ; Mass Screening ; Pathology ; Photoplethysmography ; Risk Factors ; Vascular Stiffness