STUDY OF SPECTRAL CHARACTERISTICS OF MOTION ARTIFACTS IN AMBULATORY ECG BY ANALYZING PCA RESIDUAL ERRORS

2014 ◽  
Vol 26 (02) ◽  
pp. 1450017
Author(s):  
Rahul Kher ◽  
Tanmay Pawar ◽  
Vishvjit Thakar ◽  
Dipak Patel
Keyword(s):  
Spectral Characteristics ◽  
Principal Component ◽  
Peak Frequency ◽  
Motion Artifacts ◽  
Statistical Parameters ◽  
Ecg Signals ◽  
Ambulatory Ecg ◽  
Power Spectral ◽  
Left And Right ◽  
Residual Errors

In this paper, the spectral characteristics of motion artifacts occurring in an ambulatory ECG signal have been studied using principal component analysis (PCA). The PCA residual errors characterize the spectral behavior of the motion artifacts occurring in ambulatory ECG signals. The ECG signals have been acquired from Biopac MP-36 system and a self-developed wearable ECG recorder. The performance is evaluated by power spectral density (PSD) plots of PCA residual errors as well as statistical parameters like mean, median and variance of PCA errors. The PSD plots clearly indicate that the peak frequency of the motion artifacts occurring due to various body movements (like left and right arms up–down, left and right legs up–down, waist twist, walking and sitting up–down) is located around 20–25 Hz against the ECG peak frequency around 5–10 Hz.

NONINVASIVE POWER SPECTRUM ANALYSIS OF RADIAL PRESSURE WAVEFORM FOR ASSESSMENT OF VASCULAR SYSTEM

2012 ◽  
Vol 12 (01) ◽  
pp. 1250021 ◽  
Author(s):  
QUAN-YU WU ◽  
ZU-CHANG MA ◽  
YI-NING SUN
Keyword(s):  
Power Spectrum ◽  
Systematic Approach ◽  
Vascular System ◽  
Spectral Characteristics ◽  
Radial Pressure ◽  
Peak Frequency ◽  
Pressure Waveform ◽  
Power Spectral ◽  
Insight Into ◽  
Second Peak

Wrist pulse diagnosis has been used in traditional Chinese medicine (TCM) for thousands of years, because pulse pressure signal contains a large number of physiological and pathological information of people. In this research, a systematic approach was proposed to analyze the computerized radial pressure waveform, with the focus placed on the power spectrum. We gained the power spectrum by using a modified fast Fourier transform, and the power-spectral characteristics were analyzed and compared. The analyzing program calculated the first peak frequency (F1) and the second peak (F2) automatically, and gained the time of phase shift between two frequencies. They could provide a simple noninvasive means for studying changes in the elastic properties of the vascular system depending on the age and the disease. Namely, the frequency analysis of radial pressure waveform gives new insight into the dynamics of cardiovascular system.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Characterization of Tissue Microstructure Scatterer Distribution with Spectral Correlation

1993 ◽  
Vol 15 (3) ◽  
pp. 238-254 ◽  
Author(s):  
Tomy Varghese ◽  
Kevin D. Donohue
Keyword(s):  
Formation Process ◽  
Biological Tissue ◽  
Statistical Parameters ◽  
Spectral Correlation ◽  
Tissue Microstructure ◽  
Power Spectral ◽  
Spectral Peaks ◽  
Ultrasound Signal

Characterization of tissue microstructure from the backscattered ultrasound signal using the spectral autocorrelation (SAC) function provides information about the scatterer distribution in biological tissue. This paper demonstrates SAC capabilities in characterizing periodicities in A-scans due to regularity in the scatterer distribution. The A-scan is modelled as a cyclostationary signal, where the statistical parameters of the signal vary in time with single or multiple periodicities. This periodicity manifests itself as spectral peaks both in the power spectral density (PSD) and in the SAC. Periodicity in the PSD will produce a well defined dominant peak in the cepstrum, which has been used to determine the scatterer spacing. The relationship between the scatterer spacing and the spacing of the spectral peaks is established using a stochastic model of the echo-formation process from biological tissue. The distribution of the scatterers within the microstructure is modelled using a Gamma function, which offers a flexible method of simulating parametric regularity in the scatterer spacing. Simulations of the tissue microstructure for lower orders of regularity indicate that the SAC components reveal information about the scatterer spacing that are not seen in the PSD and the cepstrum. The echo-formation process is tested by simulating microstructure of varying regularity and analyzing their effect on the SAC, PSD and cepstrum. Experimental validation of the simulation results are provided using in vivo scans of the breast and liver tissue that show the presence of significant spectral correlation components in the SAC.

scholarly journals Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing

2021 ◽  
Vol 13 (2) ◽  
pp. 318
Author(s):  
Jae-Jin Park ◽  
Kyung-Ae Park ◽  
Pierre-Yves Foucher ◽  
Philippe Deliot ◽  
Stephane Le Floch ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spectral Characteristics ◽  
Chemical Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Noise Removal ◽  
Vertex Component Analysis ◽  
Shortwave Infrared ◽  
Spectral Mixture ◽  
Physical And Chemical

With an increase in the overseas maritime transport of hazardous and noxious substances (HNSs), HNS-related spill accidents are on the rise. Thus, there is a need to completely understand the physical and chemical properties of HNSs. This can be achieved through establishing a library of spectral characteristics with respect to wavelengths from visible and near-infrared (VNIR) bands to shortwave infrared (SWIR) wavelengths. In this study, a ground HNS measurement experiment was conducted for artificially spilled HNS by using two hyperspectral cameras at VNIR and SWIR wavelengths. Representative HNSs such as styrene and toluene were spilled into an outdoor pool and their spectral characteristics were obtained. The relative ratio of HNS to seawater decreased and increased at 550 nm and showed different constant ratios at the SWIR wavelength. Noise removal and dimensional compression procedures were conducted by applying principal component analysis on HNS hyperspectral images. Pure HNS and seawater endmember spectra were extracted using four spectral mixture techniques—N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA). The accuracy of detection values of styrene and toluene through the comparison of the abundance fraction were 99.42% and 99.56%, respectively. The results of this study are useful for spectrum-based HNS detection in marine HNS accidents.

Transmission to the chest of sound introduced at the mouth

1989 ◽  
Vol 66 (1) ◽  
pp. 278-281 ◽  
Author(s):  
S. S. Kraman ◽  
A. B. Bohadana
Keyword(s):  
Peak Frequency ◽  
Audio Signals ◽  
Physiological Variables ◽  
Lung Capacity ◽  
Lung Physiology ◽  
Lower Lung ◽  
Residual Capacity ◽  
Left And Right ◽  
Technical Factors ◽  
The Right

We examined the transmission to the chest wall of white noise and 25-Hz square-wave-generated noise introduced at the mouth of five healthy subjects. The output audio signals were recorded over the left and right upper and lower lung zones, posteriorly. Sound measurements were made during apnea at functional residual capacity, total lung capacity, and residual volume both after breathing air and an 80% He-20% O2 (heliox) gas mixture. We calculated the peak-to-peak amplitude, the peak frequency, and the midpower frequency of the output sound. We found no consistent variations in the values of these indexes due to lung volume or resident gas density. In all cases, the transmitted sound was most intense at the right upper zone. This could not be explained on the basis of technical factors but was probably the result of normal asymmetry of the mediastinal anatomy. These data suggest that sound introduced through the mouth of healthy individuals excites intrathoracic structures but is transmitted through the parenchyma in such a manner that it is not markedly affected by familiar physiological variables. This must be taken into account if objective acoustical tests of lung physiology are to be developed.

scholarly journals The local Universe in the era of large surveys – I. Spectral classification of S0 galaxies

2020 ◽  
Vol 495 (4) ◽  
pp. 4135-4157 ◽  
Author(s):  
J L Tous ◽  
J M Solanes ◽  
J D Perea
Keyword(s):  
Dimensional Space ◽  
Spectral Characteristics ◽  
Principal Component ◽  
Morphological Type ◽  
Physical Parameters ◽  
Star Forming ◽  
Interesting Possibility ◽  
Low Dimensional ◽  
Free Machine

ABSTRACT This is the first paper in a series devoted to review the main properties of galaxies designated S0 in the Hubble classification system. Our aim is to gather abundant and, above all, robust information on the most relevant physical parameters of this poorly understood morphological type and their possible dependence on the environment, which could later be used to assess their possible formation channel(s). The adopted approach combines the characterization of the fundamental features of the optical spectra of $68\, 043$ S0 with heliocentric z ≲ 0.1 with the exploration of a comprehensive set of their global attributes. A principal component analysis is used to reduce the huge number of dimensions of the spectral data to a low-dimensional space facilitating a bias-free machine-learning-based classification of the galaxies. This procedure has revealed that objects bearing the S0 designation consist, despite their similar morphology, of two separate subpopulations with statistically inconsistent physical properties. Compared to the absorption-dominated S0, those with significant nebular emission are, on average, somewhat less massive, more luminous with less concentrated light profiles, have a younger, bluer, and metal-poorer stellar component, and avoid high-galaxy-density regions. Noteworthy is the fact that the majority of members of this latter class, which accounts for at least a quarter of the local S0 population, show star formation rates and spectral characteristics entirely similar to those seen in late spirals. Our findings suggest that star-forming S0 might be less rare than hitherto believed and raise the interesting possibility of identifying them with plausible progenitors of their quiescent counterparts.

Accurate Detection of ECG Signals in ECG Monitoring Systems by Eliminating the Motion Artifacts and Improving the Signal Quality Using SSG Filter with DBE

2019 ◽  
Vol 29 (02) ◽  
pp. 2050024
Author(s):  
Mahesh B. Dembrani ◽  
K. B. Khanchandani ◽  
Anita Zurani
Keyword(s):  
Signal Analysis ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Signal Quality ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Ecg Signals ◽  
Cardiac Disorders ◽  
Digital Processor ◽  
Ica Algorithm

The automatic recognition of QRS complexes in an Electrocardiography (ECG) signal is a critical step in any programmed ECG signal investigation, particularly when the ECG signal taken from the pregnant women additionally contains the signal of the fetus and some motion artifact signals. Separation of ECG signals of mother and fetus and investigation of the cardiac disorders of the mother are demanding tasks, since only one single device is utilized and it gets a blend of different heart beats. In order to resolve such problems we propose a design of new reconfigurable Subtractive Savitzky–Golay (SSG) filter with Digital Processor Back-end (DBE) in this paper. The separation of signals is done using Independent Component Analysis (ICA) algorithm and then the motion artifacts are removed from the extracted mother’s signal. The combinational use of SSG filter and DBE enhances the signal quality and helps in detecting the QRS complex from the ECG signal particularly the R peak accurately. The experimental results of ECG signal analysis show the importance of our proposed method.

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