On Non-Invasive Measurement of Gastric Motility from Finger Photoplethysmographic Signal (original) (raw)
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Extraction of Gastric Myoelectric Activity from Finger Photoplethysmographic Signal
2010
This paper is an experimental study to examine the possibility of extracting gastric myoelectric activity (GMA) from photoplethysmographic (PPG) signals. Diagnosing GMA is a clinically challenging task because of its invasive/cumbersome methods. It is known that the PPG consists of information related to heart rate, respiratory rate and phenomena. Here we take this thread further and see whether GMA can
Medical & Biological Engineering & Computing, 2011
Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the networkreconstructed slow wave provided significantly higher (P \ 0.0001) correlation (C0.9) with the subject's EGG slow wave than the correlation obtained (&0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.
Fingertip Pulse Wave (PPG signal) Analysis and Heart Rate Detection
To study heart beat pulse wave propagation in real time and to evaluate the vascular blood flow resistance an important physiological parameter for vascular diagnostics. Photoplethysmography is a non-invasive technique that measures relative blood volume changes in the blood vessels close to the skin. PPG analysis emphasizes the importance of early evaluation of the diseases We present the results of analysis of photoplethysmography (PPG) signal having motion artifacts which are as alike Gaussian noise in nature. We have proposed a methodology in detecting Heart rate and respiration rate after performing noise cancellation i.e. removing the motion artifacts of the PPG signal. Significance of different Wavelets such as db4, bior3.3, coif1, sym2, haar discussed for removing the motion artifacts. A novel beat rate extraction algorithm (BREA) is implemented monitor the heart rate and respiratory rate of peripheral pulse which has a steep rise and notch on falling slope in the subjects and a more gradual rise and fall and very small dicrotic notch.
Pulse rate variability and gastric electric power in fasting and postprandial conditions
2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009
Photoplethysmography (PPG) is typically used to extract cardiac-related information like heart rate and cardiac output, though extra-cardiac information like respiratory rate can also be extracted from PPG. The aim of the current study is to advance this approach further and investigate existence of gastric-related activity in PPG. To this end, we consider pulse rate variability (PRV), which provides information analogous to heart rate variability (HRV). Finger PPG and electrogastrography (EGG) signals were recorded from 8 healthy volunteers in fasting and postprandial state for 30 minutes. Peak-to-peak interval (PPI) analysis shows that the power of high frequency (HF) component in fasting and postprandial state changes significantly. The power ratio (PR), which is the ratio between powers of low frequency band (LF, 0.04-0.15 Hz) to that of high frequency band (HF, 0.15-0.4 Hz) and the EGG power were calculated in fasting and postprandial state. PR was positively correlated with EGG power (r = 0.46; P< 0.05). PR indicates the balancing sympathovagal modulation and vagal nervous activity. The significance of this study is that PR from PRV analysis could be used as a tool for diagnosing gastric system instead of the present invasive/cumbersome methods.
Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review
Healthcare
Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual’s ...
A Review on Diagnostic Features and Potential Applications of PPG Signal in Healthcare
Healthcare, 2022
Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual's quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.
Effect of Gastric Myoelectric Activity on Photoplethysmographic Signals
2000
Understanding the hemodynamic interactions of a non-cardiac system with the main circulatory system is a challenging task in modern medicine. In this study the effect of gastric myoelectric activity (GMA) on peripheral blood volume pulse, acquired by photoplethysmographic (PPG) technique during fasting and postprandial states are analyzed using wavelets. PPG and Electrogastrogram (EGG) signals were acquired simultaneously from 8 healthy
Sensors (Basel, Switzerland), 2021
Background: Feature extraction from photoplethysmography (PPG) signals is an essential step to analyze vascular and hemodynamic information. Different morphologies of PPG waveforms from different measurement sites appear. Various phenomena of missing or ambiguous features exist, which limit subsequent signal processing. Methods: The reasons that cause missing or ambiguous features of finger and wrist PPG pulses are analyzed based on the concept of component waves from pulse decomposition. Then, a systematic approach for missing-feature imputation and ambiguous-feature resolution is proposed. Results: From the experimental results, with the imputation and ambiguity resolution technique, features from 35,036 (98.7%) of 35,502 finger PPG cycles and 36307 (99.1%) of 36,652 wrist PPG cycles can be successfully identified. The extracted features became more stable and the standard deviations of their distributions were reduced. Furthermore, significant correlations up to 0.92 were shown b...
Adaptive, autoregressive spectral estimation for analysis of electrical signals of gastric origin
Physiological Measurement, 2003
The electrical activity of the human stomach, which normally shows a frequency of about 0.05 Hz, may be studied non-invasively by either cutaneous electrogastrography (EGG) or surface magnetogastrography (MGG). Detection of changes in frequency with time may be useful to characterize gastric disorders. The fast Fourier transform (FFT) has been the most commonly used method for the automated spectral analysis of the signals obtained from the EGG or the MGG. We have used an autoregressive (AR) parametric spectrum estimator to analyse simulated signals of gastric electrical activity, and to evaluate the results of human studies using EGG and MGG. In comparison with the FFT, our results showed that the AR spectrum estimator provided more detailed qualitative information about frequency variations of short duration simulated signals than the FFT. In the human studies, the AR estimator was as good as the conventional FFT methods in detecting physiological changes in frequency and in identifying abnormal recordings. We conclude that the AR spectral estimator may provide a better qualitative analysis of frequency variations in small portions of the signal, and is as useful as the FFT to analyse human EGG or MGG studies.
Time-frequency methods for detecting spike activity of stomach
1999
It has been hypothesised by many researchers that the spike activity signals of the stomach are responsible for triggering peristaltic contractions. Since most gastric motilffy disorders include an abnormality in the contraction pattern, ff is very important to access this information non-invasively. The aim in this study is to use abdominal electrogastrogram (EGG) signals to detect the spike activity signals generated by the serosa of the stomach, and hence provide clinicians with a better method to monitor the motilffy state of the stomach. Through second and third-order spectral estimations performed on the serosal data obtained from canine experiments, it was concluded that the spike activity in serosal signals occupies a frequency range of 50-80 cycles per minute. An increase in this frequency range during strong antral contractions was observed both in the serosal and cutaneous power spectra. By using the "continuous wavelet transform" with respect to a modified Morlet wavelet, the spike activity signals generated from the serosa of the stomach can be detected and quantified in time from the cutaneous EGG records. During phase III contraction episodes, a detection accuracy of up to 96% from the cutaneous EGG recordings was calculated based on the scored serosal spike activities simultaneously recorded.