Robust algorithm to locate heart beats from multiple physiological waveforms by individual signal detector voting
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Alarm fatigue is a top medical device hazard in patient monitoring that could be reduced by merging physiological information from multiple sensors, minimizing the impact of a single sensor failing. We developed a heart beat detection algorithm that utilizes multi-modal physiological signals (e.g. electrocardiogram, blood pressure, stroke volume, photoplethysmogram and electro-encephalogram) by merging the heart beats obtained from signal-specific detectors. We used the PhysioNet/Computing in Cardiology Challenge 2014 training set to develop the algorithm, and we refined it with a mix of signals from the multiparameter intelligent monitoring in intensive care (MIMIC II) database and artificially disrupted waveforms. The algorithm had an average sensitivity of 95.67% and positive predictive value (PPV) of 92.28% when applied to the PhysioNet/Computing in Cardiology Challenge 2014 200 record training set. On a refined dataset obtained by removing 5 records with arrhythmias and inconsistent reference annotations we obtained an average sensitivity of 97.43% and PPV of 94.17%. Algorithm performance was assessed with the Physionet Challenge 2014 test set that consisted of 200 records (each up to 10 min length) containing multiple physiological signals and reference annotations verified by the PhysioNet/Computing in Cardiology Challenge 2014 organizers. Our algorithm had a sensitivity of 92.74% and PPV of 87.37% computed over all annotated beats, and a record average sensitivity of 91.08%, PPV of 86.96% and an overall score (average of all 4 measures) of 89.53%. Our algorithm is an example of a data fusion approach that can improve patient monitoring and reduce false alarms by reducing the effect of individual signal failures.Keywords:
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This paper presents a new method to detect deformed Photoplethysmogram (PPG) waveforms for sufficient accuracy of signal processing. The PPG waveforms have been applied in many health indicators, such as blood pressure, blood viscosity and blood vessel elasticity. Usually, the measurements using a sensitive signals require user awareness so that all the PPG waveforms are kept accurate. Namely, accuracy of the calculated indicators becomes lower when the PPG waveform is deformed due to motion artifacts. In particular, detection methods of deformed PPG waveforms are important for incorporating the health indicators into general fitness trackers to find the correct waveform or to remove deformed PPG waveforms from the measurement. Therefore, we propose a new method which detects a badly formed PPG waveform by monitoring a ratio of average accelerations. Experimental results show the effectiveness of the method for detecting a deformed PPG waveform.
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The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described:
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Pulse transit time (PTT) is important in the study of arterial viscoelastic properties, wave reflections and sympathetic activities. PTT is usually defined as the time interval between the R-wave in the electrocardiogram (ECG) and a characteristic point in the photoplethysmogram. However, it was found that photoplethysmographic signals are affected by the contacting force between the photoplethysmographic sensor and the measurement site. Therefore, the aim of this study is to examine the effect of contacting force on PTT. Four characteristic points in photoplethysmogram were selected to define PTT: (1) the point at 90% of the pulse amplitude (PTT1), (2) the point at 10% of the pulse amplitude (PTT2), (3) the peak of the first derivative of photoplethysmogram (PTT3), and (4) the peak of the second derivative of photoplethysmogram (PTT4). Fifteen healthy subjects participated in the experiment and ECG and photoplethysmographic signals were recorded under different contacting forces ranging from 0.1 N to 0.5 N. It was found that the PTT defined by the point near the foot (PTT2 and PTT4) or on the rising limb (PTT3) of photoplethysmogram increased with the contacting force till near the zero transmural pressure, after that PTT kept at an almost constant level. Whereas such changing trend was not found in PTT defined by the point near the peak of photoplethysmogram
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A photoplethysmography (PPG) signal can provide very useful information about a subject's hemodynamic status in a hospital or home environment. A newly developed portable multi-spectral photoplethysmography device has been used for studies of 11 healthy subjects. Multi-spectral photoplethysmography (MS-PPG) biosensor intended for analysis of peripheral blood volume pulsations at different vascular depths has been designed and experimentally tested. Multispectral monitoring was performed by means of a three-wavelengths (405 nm, 660 nm and 780 nm) laser diode and a single photodiode with multi-channel signal output processing. The proposed methodology and potential clinical applications are discussed.
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A photoplethysmography (PPG) signal can provide very useful information about a subject's hemodynamic status in a hospital or home environment. A newly developed portable multi-spectral photoplethysmography device has been used for studies of 11 healthy subjects. Multi-spectral photoplethysmography (MS-PPG) biosensor intended for analysis of peripheral blood volume pulsations at different vascular depths has been designed and experimentally tested. Multi-spectral monitoring was performed by means of a three–wavelengths (405 nm, 660 nm and 780 nm) laser diode and a single photodiode with multi-channel signal output processing. The proposed methodology and potential clinical applications are discussed.
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Using real-time data to measure blood pressure from single photoplethysmography (PPG) is proposed. Due to an increase in publicly available datasets, the application of machine learning techniques in medical research studies has expanded in recent years. The Datasets utilized in this work were taken from the Queensland Vital Signs Dataset. Five feature vectors from the Photoplethysmography (PPG) signal are extracted and sampled at a rate of 25Hz. The R2 score value and Mean Square Error (MSE) are used to measure the performance of the various models. The best results for Systolic Blood Pressure are 0.91 and 7.76, respectively. While for Diastolic Blood Pressure the best results achieved were 0.87 and 7.06, respectively.
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The photoplethysmogram is a noninvasive circulatory signal related to the pulsatile volume of blood in tissue and is displayed by many pulse oximeters and bedside monitors, along with the computed arterial oxygen saturation. The photoplethysmogram is similar in appearance to an arterial blood pressure waveform. Because the former is noninvasive and nearly ubiquitous in hospitals whereas the latter requires invasive measurement, the extraction of circulatory information from the photoplethysmogram has been a popular subject of contemporary research. The photoplethysmogram is a function of the underlying circulation, but the relation is complicated by optical, biomechanical, and physiologic covariates that affect the appearance of the photoplethysmogram. Overall, the photoplethysmogram provides a wealth of circulatory information, but its complex etiology may be a limitation in some novel applications.
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Oxygen Saturation
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Photoplethysmography is a method to visualize the variation in blood volume within tissues with light. The signal obtained has been used for the monitoring of patients, interpretation for diagnosis or for extracting other physiological variables (e.g., pulse rate and blood oxygen saturation). However, the photoplethysmography signal can be perturbed by external and physiological factors. Implementing methods to evaluate the quality of the signal allows one to avoid misinterpretation while maintaining the performance of its applications. This paper provides an overview on signal quality index algorithms applied to photoplethysmography. We try to provide a clear view on the role of a quality index and its design. Then, we discuss the challenges arising in the quality assessment of imaging photoplethysmography.
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Photoplethysmography (PPG) is a method which is used to extract physiological parameters such as heart pulse rate, respiratory rate, and their variation with respect to time by optically measuring the blood volume change in the tissue. Photoplethysmography methods can be categorized into two groups: contact and non-contact. An example of contact photoplethysmography is the fingertip pulse oximetre which is widely used in medical centers. In non-contact photoplethysmography, with the use of special or commonly used cameras, these parameters are extracted from the color changes especially around the face caused by heart beat. In this survey, we aimed to provide information about the important studies in the literature, as well as to introduce the areas for improvement.
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Respiratory Rate
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