Power Spectral Entropy Research Articles

A hybrid health condition monitoring method in milling operations

Cutting chatter has been a very important issue in the milling operations due to its unexpected and uncontrollable characteristics. Developing an effective healthy condition monitoring method is critical to identify cutting chatter exactly. In this paper, a hybrid healthy condition monitoring (HHCM) method, that combines variational mode decomposition (VMD) with genetic algorithm-based back propagation neural network (BPNN) model, is developed for cutting chatter detection and state classification in complex and non-stationary milling operations. First, cutting chatter vibration signal is decomposed into multiple mode components by the VMD. Then, Shannon power spectral entropy is adopted to extract features from decomposed vibration signals. Furthermore, BPNN model is optimized by traditional genetic algorithm to identify and classify machine states in milling operations. Last, impeller milling experiments are conducted and results show that the proposed HHCM method can effectively realize cutting chatter detection and state classification during milling operations.

EEMD-based online milling chatter detection by fractal dimension and power spectral entropy

Chatter is a kind of self-excited unstable vibration during machining process, which always leads to multiple negative effects such as poor surface quality, dimension accuracy error, excessive noise, and tool wear. For purposes of monitoring the processing state of milling process and detecting chatter timely, a novel online chatter detection method was proposed. In the proposed method, the acceleration signals acquired by sensor were decomposed into a series of intrinsic mode functions (IMFs) by the adaptive analysis method named ensemble empirical mode decomposition (EEMD), and the IMFs which contain the feature information of milling process were selected as the analyzed signals. The two indicators power spectral entropy and fractal dimension which is obtained by morphological covering method are introduced to detect the chatter features. Then, both the frequency characteristic and morphological feature of the extracted signals can be reflected by the two indicators. To verify the approach, milling experiments were performed; the experiment results show that the proposed method can detect chatter timely and effectively, which is important in the aspect of improving the milling quality. And finally, in order to detect milling chatter timely, an online milling chatter monitoring system was developed.

Power spectral entropy as an information-theoretic correlate of manner of articulation in American English.

While all languages differentiate speech sounds by manner of articulation, none of the acoustic correlates proposed to date seem to account for how these contrasts are encoded in the speech signal. The present study describes power spectral entropy (PSE), which quantifies the amount of potential information conveyed in the power spectrum of a given sound. Results of acoustic analyses of speech samples extracted from the Texas Instruments-Massachusetts Institute of Technology database reveal a statistically significant correspondence between PSE and American English major classes of manner of articulation. Thus, PSE accurately captures an acoustic correlate of manner of articulation in American English.

Automatic snoring sounds detection from sleep sounds via multi-features analysis.

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a serious respiratory disorder. Snoring is the most intuitively characteristic symptom of OSAHS. Recently, many studies have attempted to develop snore analysis technology for diagnosing OSAHS. The preliminary and essential step in such diagnosis is to automatically segment snoring sounds from original sleep sounds. This study presents an automatic snoring detection algorithm that detects potential snoring episodes using an adaptive effective-value threshold method, linear and nonlinear feature extraction using maximum power ratio, sum of positive/negative amplitudes, 500Hz power ratio, spectral entropy (SE) and sample entropy (SampEn), and automatic snore/nonsnore classification using a support vector machine. The results show that SampEn provides higher classification accuracy than SE. Furthermore, the proposed automatic detection method achieved over 94.0% accuracy when identifying snoring and nonsnoring sounds despite using small training sets. The sensitivity and accuracy of the results demonstrate that the proposed snoring detection method can effectively classify snoring and nonsnoring sounds, thus enabling the automatic detection of snoring.

Identifying montages that best detect the electroencephalogram power spectrum alteration during freezing of gait in Parkinson's disease patients.

Our research team has previously used four Electroencephalography (EEG) leads to successfully detect and predict Freezing of Gait (FOG) in Parkinson's disease (PD). However, it remained to be determined whether these four sensor locations that were arbitrarily chosen based on their role in motor control are indeed the most optimal for FOG detection. The aim of this study was therefore to determine the most optimal location and combination of sensors to detect FOG amongst a 32-channel EEG montage using our EEG classification system. EEG measures, including power spectral density, centroid frequency and power spectral entropy, were extracted from 7 patients with PD and FOG during a series of Timed up and Go tasks. By applying a feed-forward neural networks to classify EEG data, the obtained results showed that even a small number of electrodes suffice to construct a FOG detector with expected performance, which is comparable to the use of a full 32 channels montage. This finding therefore progresses the realization of a FOG detection system that can be effectively implemented on a daily basis for FOG prevention, improving the quality of life for many patients with PD.

Quantifying the complexity of human colonic pressure signals using an entropy measure.

Studying the complexity of human colonic pressure signals is important in understanding this intricate, evolved, dynamic system. This article presents a method for quantifying the complexity of colonic pressure signals using an entropy measure. As a self-adaptive non-stationary signal analysis algorithm, empirical mode decomposition can decompose a complex pressure signal into a set of intrinsic mode functions (IMFs). Considering that IMF2, IMF3, and IMF4 represent crucial characteristics of colonic motility, a new signal was reconstructed with these three signals. Then, the time entropy (TE), power spectral entropy (PSE), and approximate entropy (AE) of the reconstructed signal were calculated. For subjects with constipation and healthy individuals, experimental results showed that the entropies of reconstructed signals between these two classes were distinguishable. Moreover, the TE, PSE, and AE can be extracted as features for further subject classification.

Comparing features extractors in EEG-based cognitive fatigue detection of demanding computer tasks.

An electroencephalography (EEG)-based classification system could be used as a tool for detecting cognitive fatigue from demanding computer tasks. The most widely used feature extractor in EEG-based fatigue classification is power spectral density (PSD). This paper investigates PSD and three alternative feature extraction methods, in order to find the best feature extractor for the classification of cognitive fatigue during cognitively demanding tasks. These compared methods are power spectral entropy (PSE), wavelet, and autoregressive (AR). Bayesian neural network was selected as the classifier in this study. The results showed that the use of PSD and PSE methods provide an average accuracy of 60% for each computer task. This finding is slightly improved using the wavelet method which has an average accuracy of 61%. The AR method is the best feature extractor compared with the PSD, PSE and wavelet in this study with accuracy of 75.95% in AX-continuous performance test (AX-CPT), 75.23% in psychomotor vigilance test (PVT) and 76.02% in Stroop task (p-value <; 0.05).

Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators

Vibration analysis is widely used to reveal the fundamental cutting mechanics in machining condition monitoring. In this work, vibration signals generated in different chatter conditions as well as stable cutting are studied to understand chatter characteristics. Considering the nonlinear and non-stationary properties of chatter vibration in milling process, a self-adaptive analysis method named ensemble empirical mode decomposition (EEMD) is adopted to analyze vibration signals and two nonlinear indices are extracted as chatter indicators. Firstly, the vibration signal is preprocessed with a comb filter to eliminate the interference of rotation frequency, tooth passing frequency and their harmonics. Secondly, EEMD is applied to decompose the filtered signal into a set of intrinsic mode functions (IMFs). Sensitive IMFs containing rich chatter information are selected. With the development of chatter, an accumulation phenomenon appears in the spectrum of sensitive IMFs and chatter frequencies are modulated by the rotation frequency and tooth passing frequency. Finally, two nonlinear dimensionless indices within the range of [0, 1], i.e., C0 complexity and power spectral entropy, are extracted from the sensitive IMFs in both time domain and frequency domain. The proposed method is verified with well-designed cutting tests. It is found that, the stochastic noise dominates in the sensitive IMFs of stable cutting and both the C0 complexity value and power spectral entropy are the largest; with the increase of chatter severity level, the periodic chatter components dominate gradually and the proportion of stochastic noise decreases, and thus these two indicators decrease.

Assessment visual fatigue of watching 3DTV using EEG power spectral parameters

Although the three-dimensional television is popular for its stereoscopy, the fatigue caused by the prolonged watching of 3DTV should not be underestimated. Electroencephalogram (EEG) has been widely used for monitoring the brain’s functional activities. Based on our previous research of 3DTV fatigue, one more objective and effective 3DTV fatigue evaluation model is proposed on gravity frequency of power spectrum and power spectral entropy. As the fatigue changes, the gravity frequency reflects the transition of EEG power spectrum and the power spectral entropy describes the level of chaos of EEG. 16 channels of EEG data of twenty-five subjects watching 2DTV and 3DTV were collected, and gravity frequency of power spectrum and power spectral entropy were then calculated and analyzed. These two parameters of the 3D group changed more significantly comparing with that of the 2D group on several electrodes. There are significant decreases in gravity frequency and power spectral entropy in several brain regions after long time of watching 3DTV, which indicates the decline of subjects’ alertness level. Based on the subjective evaluation and two significant parameters, gravity frequency and power spectral entropy, an accurate evaluation model for 3DTV fatigue was established using the regression equation.

Rolling Bearing Safety Region Estimation Based on Information Entropy

The basic idea of safety region is introduced into roller bearing condition monitoring. Power Spectral Entropy, Singular value Entropy are used comprehensively for the estimation of the safety region and the identification of normal state and faulty state for the roller bearing operational status. First, the vibration acceleration data was segmented according to a certain time interval and then establish Power Spectral Entropy, Singular value Entropy as characteristics of roller bearings. Finally, SVM was used for the estimation of the safety region of the roller bearing operation state, and multi-class SVM was used of the identification of the four states. The results show that both the safety region estimation and state identification are accurate, and confirm the validity of the method.

Development of a system for the automatic detection of air embolism using a precordial Doppler.

Venous air embolism (VAE) is the air bubble accumulation in the right side of the heart. Changes in Doppler heart sound (DHS) are characteristic of VAE, and the anesthesiologist has to pay attention to this event continuously, which may not always be possible. This work aims to study different features of the heart sound through a precordial Doppler, that may provide useful information on VAE episodes. A clinical protocol was designed, and DHS was collected at baseline and following infusions of saline with 4 distinct volumes (1ml, 5ml, 8ml and 10ml), and two infusion rates (slow and fast), given by central and peripheric catheters. Signal was pre-processed, the envelope of each signal was extracted, and five features were implemented and evaluated: frequency corresponding to 95% of Welch periodogram power (f95), frequency corresponding to 50% of Welch periodogram power (f50), frequency corresponding to maximum power spectral density (fm), entropy (E), and frequency corresponding to maximum energy of a wavelet transform (freqwav). Relation between extracted features and saline infusions were studied and compared to baseline values. A Graphical User Interface (GUI) with a database of Doppler heart sounds and annotations was also developed. Although features present a high variability between patients, E presents a better performance showing an increase in response to saline injections (in 75% injections), followed by f95 (62%), fm (56.3%), freqwav (37.5%) and f50 (0%).

Activity analysis of depression electroencephalogram based on modified power spectral entropy

A method is proposed to calculate and analyze electro-encephalogram signal to improve the situation that there is an urgent need for an effective quantitative indicator to describe brain mental disorders. The method defines a spectral entropy in terms of the power spectrum division of time series. Then, the entropy is applied to numerical calculation of electroencephalogram signals of depression patients and normal control group. Meanwhile, the differences are compared between them. Experimental results show that the power spectral entropy in depression patients is significantly weaker than the normal healthy people's in some brain regions. Further analysis proves two facts. One is that the entropy is positively correlated to brain electrical physiological activity, and the other tells that the entropy can be used as a parameter to measure brain electrical activity, to characterize brain electrical physiological activities, and to provide the activity intensity information. This paper determines that the power spectral entropy for electroencephalogram plays an important role in diagnosis of brain mental disorder.

Study on Spectrum Estimation in Biophoton Emission Signal Analysis of Wheat Varieties

The photon emission signal in visible range (380 nm–630 nm) was measured from various wheat kernels by means of a low noise photomultiplier system. To study the features of the photon emission signal, the spectrum estimation method of the photon emission signal is described for the first time. The biophoton emission signal, belonging to four varieties of wheat, is analyzed in time domain and frequency domain. It shows that the intensity of the biophoton emission signal for four varieties of wheat kernels is relatively weak and has dramatic changes over time. Mean and mean square value are obviously different in four varieties; the range was, respectively, 3.7837 and 74.8819. The difference of variance is not significant. The range is 1.1764. The results of power spectrum estimation deduced that the biophoton emission signal is a low frequency signal, and its power spectrum is mostly distributed in the frequency less than 0.1 Hz. Then three parameters, which are spectral edge frequency, spectral gravity frequency, and power spectral entropy, are adopted to explain the features of the kernels’ spontaneous biophoton emission signal. It shows that the parameters of the spontaneous biophoton emission signal for different varieties of wheat are similar.

EEG feature comparison and classification of simple and compound limb motor imagery

BackgroundMotor imagery can elicit brain oscillations in Rolandic mu rhythm and central beta rhythm, both originating in the sensorimotor cortex. In contrast with simple limb motor imagery, less work was reported about compound limb motor imagery which involves several parts of limbs. The goal of this study was to investigate the differences of the EEG patterns between simple limb motor imagery and compound limb motor imagery, and discuss the separability of multiple types of mental tasks.MethodsTen subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet), three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot) and rest state. Event-related spectral perturbation (ERSP), power spectral entropy (PSE) and spatial distribution coefficient were adopted to analyze these seven EEG patterns. Then three algorithms of modified multi-class common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM).ResultsThe induced event-related desynchronization (ERD) affects more components within both alpha and beta bands resulting in more broad ERD bands at electrode positions C3, Cz and C4 during left/right hand combined with contralateral foot imagery, whose PSE values are significant higher than that of simple limb motor imagery. From the topographical distribution, simultaneous imagination of upper limb and contralateral lower limb certainly contributes to the activation of more areas on cerebral cortex. Classification result shows that multi-class stationary Tikhonov regularized CSP (Multi-sTRCSP) outperforms other two multi-class CSP methods, with the highest accuracy of 84% and mean accuracy of 70%.ConclusionsThe work implies that there exist the separable differences between simple limb motor imagery and compound limb motor imagery, which can be utilized to build a multimodal classification paradigm in motor imagery based brain-computer interface (BCI) systems.

Visuomotor and Audiomotor Processing in Continuous Force Production of Oral and Manual Effectors

ABSTRACT The authors examined force control in oral and manual effectors as a function of sensory feedback (i.e., visual and auditory). Participants produced constant isometric force via index finger flexion and lower lip elevation to 2 force levels (10% and 20% maximal voluntary contraction) and received either online visual or online auditory feedback. Mean, standard deviation, and coefficient of variation of force output were used to quantify the magnitude of force variability. Power spectral measures and approximate entropy of force output were calculated to quantify the structure of force variability. Overall, it was found that the oral effector conditions were more variable (e.g., coefficient of variation) than the manual effector conditions regardless of sensory feedback. No effector differences were found for the structure of force variability with visual or auditory feedback. Oral and manual force control appears to involve different control mechanisms regulating continuous force production in the presence of visual or auditory feedback.

Changes in the power spectrum of electrical signals in maize leaf induced by osmotic stress

To quantify the characteristics of the power spectrum of plant electrical signals, we defined the following concepts: spectral edge frequency (SEF), spectral center frequency (SCF), power index (PI) and power spectral entropy (PSE). These parameters were used to examine and quantify changes in the power spectrum of electrical signals in maize leaves under osmotic stress. In the absence of osmotic stress, the SEF of the electrical signal in maize leaves was approx. 0.2 Hz and the SCF was approx. 0.1 Hz. The electrical signal in maize leaves was mainly a slow wave signal with a frequency of 0–0.1 Hz. After 2 h osmotic stress, the SEF and SCF of the electrical signal increased to higher frequencies. The proportion of the fast wave frequency also increased to 0.1–0.2 Hz, resulting in a dramatic increase in PSE. We also found that the changes in PSE and SCF were significantly correlated during osmotic stress. We propose that the changes in the PSE and SCF in maize leaves can be used as a sensitive signal indicating water deficit in leaf cells under osmotic stress. Thus, measurement of SCF or PSE of electrical signals in maize leaves could be used to develop early warning and rapid diagnosis techniques for the water demands of plants.

Differences of Heart Rate Variability during Sevoflurane Anesthesia in Children by Age

Background: The child’s central nervous system develops with aging, and heart rate variability (HRV), which is con-trolled by the brain, differs from that of adults. We investigated changes in HRV during sevoflurane anesthesia in children. Methods: One 138 children aged from 2 - 12 years without major underlying problems were enrolled. During maintenance with 2 - 2.5 vol% sevoflurane anesthesia, electrocardiographic data were obtained and power spectral analysis, approximate entropy (ApEn) or Hurst exponent were analyzed and compared in three groups (age 2 - 5 years, 6 - 9 and 10 - 12 years of age). Results: The RR interval increased with aging, but low-frequency powers did not. High- frequency power was greater in the oldest children (P < 0.05), while ApEn and Hurst exponents were lower (P < 0.05). Conclusion: Change in HRV is one of the characteristics of development in children.

Detection of lapses in responsiveness from the EEG

A system capable of reliably detecting lapses in responsiveness (‘lapses’) has the potential to increase safety in many occupations. We have developed an approach for detecting the state of lapsing with second-scale temporal resolution using data from 15 subjects performing a one-dimensional (1D) visuomotor tracking task for two 1 h sessions while their electroencephalogram (EEG), facial video, and tracking performances were recorded. Lapses identified using a combination of facial video and tracking behaviour were used to train the classification models. Linear discriminant analysis was used to form detection models based on individual subject data and stacked generalization was utilized to combine the outputs of multiple classifiers to obtain the final prediction. The performance of detectors estimating the lapse/not-lapse state at 1 Hz based on power spectral features, approximate entropy, fractal dimension, and Lempel-Ziv complexity of the EEG was compared. Best lapse state estimation performance was achieved using the detector model created using power spectral features with an area under the curve from receiver operating characteristic analysis of 0.86 ± 0.03 (mean±SE) and an area under the precision-recall curve of 0.43 ± 0.09. A novel technique was developed to provide improved estimation of accuracy of detection of variable-duration events. Via this approach, we were able to show that the detection of lapse events from spectral power features was of moderate accuracy (sensitivity = 73.5%, selectivity = 25.5%).

Changes in EEG measurements in intractable epilepsy patients with neurofeedback training

To assess the effects of neurofeedback on brain electrophysiology and to determine how biofeedback works, power spectral density (PSD) and approximate entropy (ApEn) analyses are applied to the EEGs of six patients with intractable epilepsy who received neurofeedback training. After sessions of treatment, the EEG sensorimotor rhythm to theta PSD ratio calculated from the C4 electrode site becomes larger than that before the treatment, which is consistent with the biofeedback protocol. The ApEn over 16-channel EEG recordings all increase to different degrees. Larger increases occur in channels located near the training position (C4). All these results suggest that these EEG measurements are new criteria that can be used to evaluate the effect of neurofeedback.

Sevoflurane anesthesia decreases cardiac vagal activity and heart rate variability

We evaluated cardiac vagal activity during sevoflurane anesthesia in neurosurgical patients. Heart rate variability was determined by power spectral analysis and entropy with the patient awake and during sevoflurane anesthesia. High frequency power (0.15-0.50 Hz) and heart rate entropy decreased during sevoflurane and these effects were significantly correlated (r = 0.71 +/- 0.12, P < 0.05). The results confirm that cardiac vagal activity was the primary determinant of heart rate variability, which was attenuated by sevoflurane.