Age-specific insomnia detection models using short-time EOG and multiscale entropy analysis

Comparative analysis of resting-state EEG-based multiscale entropy between schizophrenia and bipolar disorder

The present study explored multi-scale entropy (MSE) analysis to investigate the entropy of resting state fMRI signals across multiple time scales. MSE analysis was developed to distinguish random noise from complex signals since the entropy of the former decreases with longer time scales while the latter signal maintains its entropy due to a "self-resemblance" across time scales. A long resting state BOLD fMRI (rs-fMRI) scan with 1000 data points was performed on five healthy young volunteers to investigate the spatial and temporal characteristics of entropy across multiple time scales. A shorter rs-fMRI scan with 240 data points was performed on a cohort of subjects consisting of healthy young (age 23 ± 2 years, n = 8) and aged volunteers (age 66 ± 3 years, n = 8) to investigate the effect of healthy aging on the entropy of rs-fMRI. The results showed that MSE of gray matter, rather than white matter, resembles closely that of f (-1) noise over multiple time scales. By filtering out high frequency random fluctuations, MSE analysis is able to reveal enhanced contrast in entropy between gray and white matter, as well as between age groups at longer time scales. Our data support the use of MSE analysis as a validation metric for quantifying the complexity of rs-fMRI signals.

Complexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: a multiscale entropy analysis

Objective. This study explored the feasibility of detecting hidden muscle activity in surface electromyogram (EMG) baseline. Approach. Power spectral density (PSD) analysis and multi-scale entropy (MSE) analysis were used. Both analyses were applied to computer simulations of surface EMG baseline with the presence (representing activity data) or absence (representing reference data) of hidden muscle activity, as well as surface electrode array EMG baseline recordings of healthy control and amyotrophic lateral sclerosis (ALS) subjects. Main results. Although the simulated reference data and the activity data yielded no distinguishable difference in the time domain, they demonstrated a significant difference in the frequency and signal complexity domains with the PSD and MSE analyses. For a comparison using pooled data, such a difference was also observed when the PSD and MSE analyses were applied to surface electrode array EMG baseline recordings of healthy control and ALS subjects, which demonstrated no distinguishable difference in the time domain. Compared with the PSD analysis, the MSE analysis appeared to be more sensitive for detecting the difference in surface EMG baselines between the two groups. Significance. The findings implied the presence of a hidden muscle activity in surface EMG baseline recordings from the ALS subjects. To promote the presented analysis as a useful diagnostic or investigatory tool, future studies are necessary to assess the pathophysiological nature or origins of the hidden muscle activity, as well as the baseline difference at the individual subject level.

The Electrooculogram (EOG) signal is often contaminated with artifacts and power-line while recording. It is very much essential to denoise the EOG signal for quality diagnosis. The present study deals with denoising of noisy EOG signals using Stationary Wavelet Transformation (SWT) technique by two different approaches, namely, increasing segments of the EOG signal and different equal segments of the EOG signal. For performing the segmental denoising analysis, an EOG signal is simulated and added with controlled noise powers of 5 dB, 10 dB, 15 dB, 20 dB, and 25 dB so as to obtain five different noisy EOG signals. The results obtained after denoising them are extremely encouraging. Root Mean Square Error (RMSE) values between reference EOG signal and EOG signals with noise powers of 5 dB, 10 dB, and 15 dB are very less when compared with 20 dB and 25 dB noise powers. The findings suggest that the SWT technique can be used to denoise the noisy EOG signal with optimum noise powers ranging from 5 dB to 15 dB. This technique might be useful in quality diagnosis of various neurological or eye disorders.

Cardiovascular systems essentially have multiscale control mechanisms. Multiscale entropy (MSE) analysis permits the dynamic characterization of the cardiovascular time series for both short-term and long-term processes, and thus can be more illuminating. The traditional MSE analysis for heart rate variability (HRV) is performed on the original RR interval time series (named as MSE_RR). In this study, we proposed an MSE analysis for the differential RR interval time series signal, named as MSE_dRR. The motivation of using the differential RR interval time series signal is that this signal has a direct link with the inherent non-linear property of electrical rhythm of the heart. The effectiveness of the MSE_RR and MSE_dRR were tested and compared on the long-term MIT-Boston’s Beth Israel Hospital (MIT-BIH) 54 normal sinus rhythm (NSR) and 29 congestive heart failure (CHF) RR interval recordings, aiming to explore which one is better for distinguishing the CHF patients from the NSR subjects. Four RR interval length for analysis were used ( N = 500 , N = 1000 , N = 2000 and N = 5000 ). The results showed that MSE_RR did not report significant differences between the NSR and CHF groups at several scales for each RR segment length type (Scales 7, 8 and 10 for N = 500 , Scales 3 and 10 for N = 1000 , Scales 2 and 3 for both N = 2000 and N = 5000 ). However, the new MSE_dRR gave significant separation for the two groups for all RR segment length types except N = 500 at Scales 9 and 10. The area under curve (AUC) values from the receiver operating characteristic (ROC) curve were used to further quantify the performances. The mean AUC of the new MSE_dRR from Scales 1–10 are 79.5%, 83.1%, 83.5% and 83.1% for N = 500 , N = 1000 , N = 2000 and N = 5000 , respectively, whereas the mean AUC of MSE_RR are only 68.6%, 69.8%, 69.6% and 67.1%, respectively. The five-fold cross validation support vector machine (SVM) classifier reports the classification Accuracy ( A c c ) of MSE_RR as 73.5%, 75.9% and 74.6% for N = 1000 , N = 2000 and N = 5000 , respectively, while for the new MSE_dRR analysis accuracy was 85.5%, 85.6% and 85.6%. Different biosignal editing methods (direct deletion and interpolation) did not change the analytical results. In summary, this study demonstrated that compared with MSE_RR, MSE_dRR reports better statistical stability and better discrimination ability for the NSR and CHF groups.

The aim of this study was to present electrooculogram (EOG) signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from amyotrophic lateral sclerosis (ALS) or other diseases that prevent correct limb and facial muscular responses. Using EOG signals, it is possible to improve the communication abilities of those patients who can move their eyes. Investigating the possible usage of the EOG for human---computer interface, a relation between sight angle and EOG is determined. In other methodology, most famous approaches involve the use of a camera to visually track the eye. However, this method has problems that the eyes of user must always be open. In this paper, we propose the mouse cursor control system for ALS patients using EOG and electroencephalograph (EEG) signals. We introduced the algorithm using alternating current and direct current of EOG corresponding to the drift. Therefore, EOG measurement system we proposed improved the problems of artifacts caused by eye blinking which was not accepted for other systems, the displacement of electrode positions and the drift. In addition, we introduced the EEG measurement to examine whether the subject could control their eye movement consciously. The EEG signals were not used to control the mouse movement, but to determine the subject's control state. In order to test whether our system works well, we prepared a questionnaire and asked the subjects to operate our system, and answer with YES or NO by moving the mouse cursor. During the task, we also recorded the subjects' EEG by MYNDPLAY [7] and checked their conscious level. Three subjects participated in this experiment, and they had never operated this system before. In this experiment, we measured 30 states of EEG signals while EOG was also measuring for one eye movement and analyzed the EEG signals. The results of analysis of the EEG signal changes and the answers to questions indicated that at 26 of 30 states, the subjects' conscious level while they were moving the cursor by EOG signals was correctly determined from the EEG signals. From these results, we could know that the EEG signals can be used to adjust the EOG system whether it works according to patients' mind or just a misjudgment.

In this paper, we present a comparison Support Vector Machine (SVM) and Artificial Neural Network (ANN) for classification of electrooculogram (EOG) signals acquired under specific eye movements. These methods that are required for an eye controlled system are compared by means of their accuracy and response time. Acquired EOG signals consist of 5 different eye movements - being horizontal (right and left), vertical (up and down) and blink. EOG signal acquisition was achieved from 20 different subjects by using two EOG channels (vertical and horizontal) and 3 element feature vectors were extracted. The first two elements of the feature vectors are the peak amplitudes of two channels whereas the third element, being our proposed parameter, is the kurtosis value of the active channel. 10 of 20 randomly selected feature vectors were used for training of the classifiers whereas the rest was used for performance tests. Offline tests yield 100 % success rate for both of the classifiers. The response times of both methods make them suitable for real-time usage.

Multi-scale entropy analysis and Hurst exponent

It is known that in pathological conditions, physiological systems develop changes in the multiscale properties of physiological signals. However, in real life, little is known about how changes in the function of one of the two coupled physiological systems induce changes in function of the other one, especially on their multiscale behavior. Hence, in this work we aimed to examine the complexity of cardio-respiratory coupled systems control using multiscale entropy (MSE) analysis of cardiac intervals MSE (RR), respiratory time series MSE (Resp), and synchrony of these rhythms by cross multiscale entropy (CMSE) analysis, in the heart failure (HF) patients and healthy subjects. We analyzed 20 min of synchronously recorded RR intervals and respiratory signal during relaxation in the supine position in 42 heart failure patients and 14 control healthy subjects. Heart failure group was divided into three subgroups, according to the RR interval time series characteristics (atrial fibrillation (HFAF), sinus rhythm (HFSin), and sinus rhythm with ventricular extrasystoles (HFVES)). Compared with healthy control subjects, alterations in respiratory signal properties were observed in patients from the HFSin and HFVES groups. Further, mean MSE curves of RR intervals and respiratory signal were not statistically different only in the HFSin group (p = 0.43). The level of synchrony between these time series was significantly higher in HFSin and HFVES patients than in control subjects and HFAF patients (p < 0.01). In conclusion, depending on the specific pathologies, primary alterations in the regularity of cardiac rhythm resulted in changes in the regularity of the respiratory rhythm, as well as in the level of their asynchrony.

Because of the complexity and randomness of the gas-solid two-phase flow, it is still hard to fully understand the dynamic behavior of the flow. The electrostatic signal contains rich information of the particulate flow. Multiscale entropy (MSE) analysis of electrostatic fluctuation signal was conducted in this paper. The electrostatic signals were obtained from the horizontal pipeline in dilute pneumatic conveying system. The influence of sequence length on MSE of electrostatic signal was analyzed at first. Then the influence of solids concentration and superficial air velocity on MSE was shown, which reflects that it is helpful for understanding of the dynamics of two phase flow.

Validating and improving the correction of ocular artifacts in electro-encephalography

In this paper, the Multiscale Entropy (MSE) analysis of acceleration data collected from a wearable inertial sensor was compared with other features reported in the literature to observe falling behavior from the acceleration data, and traditional clinical scales to evaluate falling behavior. We use a fall risk assessment over a four-month period to examine &gt;65 year old participants in a community service context using simple clinical tests, including the Short Form Berg Balance Scale (SFBBS), Timed Up and Go test (TUG), and the Short Portable Mental Status Questionnaire (SPMSQ), with wearable accelerometers for the TUG test. We classified participants into fallers and non-fallers to (1) compare the features extracted from the accelerometers and (2) categorize fall risk using statistics from TUG test results. Combined, TUG and SFBBS results revealed defining features were test time, Slope(A) and slope(B) in Sit(A)-to-stand(B), and range(A) and slope(B) in Stand(B)-to-sit(A). Of (1) SPMSQ; (2) TUG and SPMSQ; and (3) BBS and SPMSQ results, only range(A) in Stand(B)-to-sit(A) was a defining feature. From MSE indicators, we found that whether in the X, Y or Z direction, TUG, BBS, and the combined TUG and SFBBS are all distinguishable, showing that MSE can effectively classify participants in these clinical tests using behavioral actions. This study highlights the advantages of body-worn sensors as ordinary and low cost tools available outside the laboratory. The results indicated that MSE analysis of acceleration data can be used as an effective metric to categorize falling behavior of community-dwelling elderly. In addition to clinical application, (1) our approach requires no expert physical therapist, nurse, or doctor for evaluations and (2) fallers can be categorized irrespective of the critical value from clinical tests.

Objective: Intractable epilepsy and uncontrolled seizures could affect cardiac function and the autonomic nerve system with a negative impact on children's growth. The aim of this study was to investigate the variability and complexity of cardiac autonomic function in pre-school children with pediatric intractable epilepsy (PIE).Methods: Twenty four-hour Holter electrocardiograms (ECGs) from 93 patients and 46 healthy control subjects aged 3–6 years were analyzed by the methods of traditional heart rate variability (HRV), multiscale entropy (MSE), and Kurths–Wessel symbolization entropy (KWSE). Receiver operating characteristic (ROC) curve analysis was used to estimate the overall discrimination ability. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) models were also analyzed.Results: Pre-school children with PIE had significantly lower HRV measurements than healthy controls in time (Mean_RR, SDRR, RMSSD, pNN50) and frequency (VLF, LF, HF, LF/HF, TP) domains. For the MSE analysis, area 1_5 in awake state was lower, and areas 6_15 and 6_20 in sleep state were higher in PIE with a significant statistical difference. KWSE in the PIE group was also inferior to that in healthy controls. In ROC curve analysis, pNN50 had the greatest discriminatory power for PIE. Based on both NRI and IDI models, the combination of MSE indices (wake: area1_5 and sleep: area6_20) and KWSE (m = 2, τ = 1, α = 0.16) with traditional HRV measures had greater discriminatory power than any of the single HRV measures.Significance: Impaired HRV and complexity were found in pre-school children with PIE. HRV, MSE, and KWSE could discriminate patients with PIE from subjects with normal cardiac complexity. These findings suggested that the MSE and KWSE methods may be helpful for assessing and understanding heart rate dynamics in younger children with epilepsy.

The Campi Flegrei caldera, a high-risk volcanic region in southern Italy, is currently experiencing an unrest phase characterized by significant ground deformation and increasing seismic activity, including events with magnitudes up to Md 4.2 recorded in September 2023. The continuous availability of seismic data provides a valuable framework for evaluating and developing novel event detection methods. However, in regions characterized by extensive natural and anthropogenic noise, the resulting low signal-to-noise ratio poses a significant challenge to seismic event detection. This limitation is sharpened when analyses are based on data from a single seismic station.To address this challenge, the present study introduces an innovative methodology designed for single-station analysis that combines the Multiscale Entropy (MSE) algorithm with Self-Organizing Maps (SOM) and the Short-Term Average/Long-Term Average (STA/LTA) technique for seismic signal detection and clustering. Linear Predictive Coding (LPC) algorithm is also employed in conjunction with the SOM map for a preliminary stage to certify the quality of the data and check for anomalies.The analysis uses continuous seismic data recorded over six months in the Pisciarelli area of the Campi Flegrei caldera, segmented into one-minute windows. Key features, including STA/LTA ratios (computed with 1s and 30s windows) and MSE values (computed over 20 time scales using a coarse-graining operation), are extracted to encode the input vectors for SOM training. The resulting 6x6 SOM map effectively clusters the seismic traces, revealing hidden patterns and distinguishing seismic events from background seismic noise. Notably, approximately 20% of the transient signals within the nodes of the seismic event cluster were identified as uncatalogued events, demonstrating the ability of the method to detect previously unrecorded activity. In addition, the map includes different clusters that highlight the influence of environmental factors, such as precipitation occurrences or volcanic fluid emissions, on the seismic waveforms.The integration of complexity-based analysis of the MSE alongside conventional STA/LTA techniques enables improved single-station event detection, even in a noisy environment, and hints at the correlation between seismic signal complexity and volcano dynamics. These results highlight the potential of advanced clustering and feature extraction techniques to refine seismic monitoring in active volcanic environments.