Analysis Of Biological Signals Research Articles

모바일 환경에서의 심층학습을 통한 심장 순환기 생체신호 분석 진단 도움 기술

인체는 다양한 생체기관으로 구성되었으며, 각 기관들은 독특한 생체신호들을 발생하며 기능을 수행한다. 수많은 생체신호들 중에서 소리는 전문적으로 훈련된 의료진에 의해 해당 기관의 이상 유무 혹은 질병 진단의 중요한 정보가 된다. 우리는 심장 순환기 청진음을 분석할 수 있는 Deep Learning 기반의 생체신호 분석 진단 도움 기술을 제안하였고, 공개된 심음 데이터를 통해서 방법의 타당성을 확인하였다. 결과적으로 제안한 방법은 대표적인 생체신호인 심음의 Deep Learning 분석 활용에 대한 가능성을 확인하였을 뿐만 아니라 향후, 스마트폰과 같은 모바일 환경에서의 의료 진단 도움 기술 발전에 중요한 연구 토대가 될 것으로 예상한다.

Efficient implementation of Stockwell Transform for real-time embedded processing of physiologic signals.

Physiologic monitoring enables scientists and physicians to study both normal and pathologic signals of the body. While wearable technologies are available today, many of these technologies are limited to data collection only. Embedded processors have minimal computational capabilities. We propose an efficient implementation of the Stockwell Transform which can enable real-time time-frequency analysis of biological signals in a microcontroller. The method is built upon the fact that the Stockwell Transform can be implemented as a compact filter bank with pre-computed filter taps. Additionally, due to the long tails of the gaussian windowing function, low amplitude filter taps can be removed. The method was implemented on a TI MSP430 processor. Simulated ECG data was fed into the processor to demonstrate performance and evaluate computational efficiency.

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner.

It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error.

4-6 生体生理信号の共分散構造分析によるサイバー酔いの強度の推定(第4部門 ヒューマンインフォメーション1)

4-6 生体生理信号の共分散構造分析によるサイバー酔いの強度の推定(第4部門 ヒューマンインフォメーション1)

Caracterización del patrón respiratorio de pacientes con insuficiencia cardíaca crónica mediante análisis de dinámica simbólica conjunta

This work proposes a method to characterize the respiratory pattern of patients with chronic heart failure (CHF) to determine non-periodic breathing (nPB), periodic breathing (PB) and Cheyne-Stokes respiration (CSR) through non-linear, symbolic analysis of biological signals. A total of 43 patients were examined for their cardiorespiratory profiles, their ECG and respiratory pattern signals were processed, analyzed and studied for parameters that could be of potential use in clinical decision making, specifically in patient classification. Patients in the study were characterized through their cardiorespiratory signals, applying joint symbolic dynamics (JSD) analysis to cardiac beat and respiratory interval durations. The most statistically significant parameters across all groups were identified through a Kruskal-Wallis two tailed test (α = 0.05) and a linear discriminant analysis (LDA) classification method based on such parameters was developed. The best result achieved with this classification method uses 10 features to discriminate patients with a 97.67% Accuracy (Acc). The best features to discriminate among groups are related to cardiorespiratory interaction rather than just respiration patterns alone. Results further support the idea that abnormal breathing patterns derive from physiological abnormalities in chronic heart failure.

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis.

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies.

Application of IT Methods for the Identification and Analysis of EEG Signals

This paper presents the possibility of using advanced IT tools for the identification and analysis of biological signals on the example of electroencephalographic tests. Identification and numerical analysis of EEG signals from a person suffering from epilepsy during and between seizures is presented.

Vasodilative effect and the underlying mechanism of gingerol on isolated thoracic aorta ring of rabbits

Objective To observe the effect of gingerol on the isolated thoracic aorta ring of rabbits and investigate its underlying mechanisms. Methods The thoracic aortic ring muscle strips with intact and denuded endothelium were prepared and placed in an organ chamber containing physiological salt solution(PSS).Every strip was connected to the tension transducer and the change of the vascular ring tension was recorded and analyzed by the biological signal collection and analysis computer system.Th isotonic tension of thoracic aortic rings with intact and denuded endothelium precontracted by norepinephrine(NE,1×10-5 mol/L)or KCl(20 mmol/L)was recorded before and after administrating gingerol.Meanwhile, we also observed the effects of N-nitro-L-arginine methyl ester(L-NAME,1×10-4 mol/L)and indomethacin(1×10-8 mol/L)on the action of gingerol in the rings. Results (1) Administration of 1×10-5 mol/L NE or 20 mmol/L KCl could produce the contraction effect in the thoracic aortic rings with intact endothelium.gingerol induced the muscle relaxation of aorta in both intact and denuded endothelium groups in a dose-dependant manner, meanwhile, the relaxation effect of gingerol was stronger in intact endothelium group than that in denuded endothelium group(P<0.05).(2)L-NAME could inhibit the vasodilative role caused by gingerol in intact endothelium group(%,94.83±3.63 vs.96.83±1.82, 91.17±2.89 vs.94.83±2.68, 83.83±2.23 vs.94.67±2.7, 78.00±3.54 vs.92.33±2.91,67.83±2.37 vs.90.83±3.77, 62.83±3.42 vs.89.67±3.15 respectively in NE-treated group and 90.33±8.33 vs.98.17±7.56,80.83±7.24 vs.96.83±7.28,70.83±7.57 vs.92.17 ±7.31,67.83±7.68 vs.89.83±7.23,62.83±7.49 vs.90.33±8.87,57.33±8.32 vs.87.67±8.97 respectively in KCl-treated group, P<0.05).(3)Indomethacin could inhibit the vasodilative role caused by gingerol in intact endothelium group(%,93.83±7.61 vs.97.83±7.45,92.50±10.13 vs.95.33±8.36,86.67 ±8.24 vs.93.83±7.28, 79.83±7.36 vs.89.83±7.63, 67.83±7.46 vs.85.33±8.94, 61.83±7.55 vs. 81.83±7.25 respectively in NE-treated group and 90.17±7.77 vs.96.17±7.31,79.67±8.65 vs.91.33± 8.62,72.17±7.48 vs.85.67±8.76,70.83±7.78 vs.83.17±7.79,56.83±7.62 vs.77.83±7.29,54.17± 7.68 vs.74.83±7.34 respectively in KCl-treated group, P< 0.05). Conclusion (1)Gingero preferentially antagonized the contraction effect induced by NE or KCl in a dose-dependent manner.(2)The vasorelaxation effect of gingerol on the thoracic aortic ring muscle strips was relevant to the production of NO and prostacyclin. Key words: Gingerol; Thoracic aorta; Endothelium; Nitric oxide; Prostacyclin

Biomedical Signal Processing and Modeling Complexity of Living Systems 2014

Biomedical Signal Processing and Modeling Complexity of Living Systems 2014

Effects of placental growth factor on revascularization after acute myocardial infarction in rats

To investigate the effect of placental growth factor (PlGF) on revascularization after acute myocardial infarction. Myocardial infarction model was established by ligation of left anterior descending coronary artery in Wistar rats. Thirty AMI rats were divided into 3 groups with 10 in each group: PlGF, mouse VEGFR-1/Flt-1 antibody, or saline (control group) was injected in the infarcted border zone for each group, respectively. Two weeks later the hemodynamics parameters were measured with a left ventricular needle catheter and a biological signal analysis system; left ventricular remodeling was observed by HE staining; angiogenesis was examined by immunohistochemistry and cardiomyocyte apoptosis rate was detected by TUNEL. The stroke volume, systolic pressure and left ventricular developed pressure in PlGF group were all higher than those in control group (112±7 vs 65.63±8.50 μl, P<0.01; 131.61±9.26 vs 94.84±8.53 mm Hg, P<0.01 and 175.85±11.36 vs 105.50±15.83 mm Hg, P<0.01; respectively). PlGF animals had less ventricular dilation (left ventricular diameter 8.20±0.14 vs 9.25±0.32 mm, P<0.01) and increased left ventricular wall thickness (1.81±0.10 vs 1.35±0.10 mm, P<0.01) compared to controls. The geometry parameter of anti-VEGFR1 and control animals was almost the same. PlGF animals had increased angiogenesis compared to controls (29.44±5.75 vs 15.88±2.42 endothelial cells/high-powered field, P<0.01); the alpha smooth muscle actin (α-SMA) showed that PlGF animal had a higher density of artery than others (25.14±1.83 vs 19.70±2.52 arteries/mm(2), P<0.01), and the density of artery in anti-VEFGR1 group was less than the controls. The apoptosis rate of cardiomyocytes in PlGF animals was significantly lower than that in controls (9.51%±2.75% vs 37.81%±8.74%, P<0.01). Regional delivery of PlGF following acute myocardial infarction can improve cardiac function and left ventricular remodeling, enhance angiogenesis and reduce cardiomyocyte apoptosis rate. PlGF may be a potential agent in adjuvant therapy for acute myocardial infarction.

Glucose series complexity in hypertensive patients

Nonlinear methods have been applied to the analysis of biological signals. Complexity analysis of glucose time series may be a useful tool for the study of the initial phases of glucoregulatory dysfunction. This observational, cross-sectional study was performed in patients with essential hypertension. Glucose complexity was measured with detrended fluctuation analysis (DFA), and glucose variability was measured by the mean amplitudes of glycemic excursion (MAGE). We included 91 patients with a mean age of 59 ± 10 years. We found significant correlations for the number of metabolic syndrome (MS)-defining criteria with DFA (r = 0.233, P = .026) and MAGE (r = 0.396, P < .0001). DFA differed significantly between patients who complied with MS and those who did not (1.44 vs. 1.39, P = .018). The MAGE (f = 5.3, P = .006), diastolic blood pressures (f = 4.1, P = .018), and homeostasis model assessment indices (f = 4.2, P = .018) differed between the DFA tertiles. Multivariate analysis revealed that the only independent determinants of the DFA values were MAGE (β coefficient = 0.002, 95% confidence interval: 0.001–0.004, P = .001) and abdominal circumference (β coefficient = 0.002, 95% confidence interval: 0.000015–0.004, P = .048). In our population, DFA was associated with MS and a number of MS criteria. Complexity analysis seemed to be capable of detecting differences in variables that are arguably related to the risk of the development of type 2 diabetes.

The hydraulic mechanism of the unfolding of hind wings in Dorcus titanus platymelus (order: Coleoptera).

In most beetles, the hind wings are thin and fragile; when at rest, they are held over the back of the beetle. When the hind wing unfolds, it provides the necessary aerodynamic forces for flight. In this paper, we investigate the hydraulic mechanism of the unfolding process of the hind wings in Dorcus titanus platymelus (Oder: Coleoptera). The wing unfolding process of Dorcus titanus platymelus was examined using high speed camera sequences (400 frames/s), and the hydraulic pressure in the veins was measured with a biological pressure sensor and dynamic signal acquisition and analysis (DSA) during the expansion process. We found that the total time for the release of hydraulic pressure during wing folding is longer than the time required for unfolding. The pressure is proportional to the length of the wings and the body mass of the beetle. A retinal camera was used to investigate the fluid direction. We found that the peak pressures correspond to two main cross-folding joint expansions in the hind wing. These observations strongly suggest that blood pressure facilitates the extension of hind wings during unfolding.

Prospective observational study on predicting adverse clinical outcomes in patients with implanted defibrillating devices – a study rationale, design and principal methods

The project “Predicting adverse clinical outcomes in patients with implanted defibrillating devices” is a prospective observational study in a group of high risk cardiac patients with already implanted either an implantable cardioverter-defibrillator (ICD) or a cardiac resynchronization therapy device with defibrillating mode (CRT-D). The primary project aim is to build predictive models for an appropriate ICD or CRT-D interventions and other adverse clinical outcomes based on clinical assessment of the cardiovascular system, general clinical status, advanced mathematical and statistical analysis of cardiovascular time series and biological signals, including pressure waveforms, ECG and cardiac impedance. Up to 600 ambulatory adult patients are scheduled to be enrolled and after the first visit they are expected to be followed-up every 6 months for a number of clinical outcomes, including antiarrhythmic therapy intervention, sudden and aborted cardiac death and total mortality. The project launched in March 2010 and is expected to end in February 2014.

LeID1. Time-frequency analysis: Theory and applications

Time-varying spectra are one of the most primitive sensations we experience, since we are surrounded by light of changing color, by sounds of varying pitch, and by many other phenomena whose periodicities change. The development of the physical and mathematical ideas needed to explain and understand time-varying spectra has evolved into the field now called “time-frequency analysis.” Among the many signals whose frequency content has been shown to vary in time are speech and other animal sounds, biomedical signals (e.g., heart sounds, heart rate, the electroencephalogram (EEG), the electromyogram (EMG), and others), music, radar and sonar signals, and machine vibrations, among others. In this tutorial, we give an overview of time-frequency analysis, with a focus on its applications. We describe how these methods impinge on and clarify issues in biomedical and biological signal analysis, wave propagation, random systems, non-linear systems, and other areas. Of particular interest is the application of time-frequency analysis to pulse propagation in dispersive media. We show that time-frequency considerations lead to new approximation methods for dynamic systems and wave propagation. We describe how to transform wave equations into phase-space, where the resulting equation is often more revealing than the original wave equation. We also discuss the applications to random systems and in particular to the propagation of noise fields.

Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer's disease

This study assessed the utility of multiscale entropy (MSE), a complexity analysis of biological signals, to identify changes in dynamics of surface electroencephalogram (EEG) in patients with Alzheimer's disease (AD) that was correlated to cognitive and behavioral dysfunction. A total of 108 AD patients were recruited and their digital EEG recordings were analyzed using MSE methods. We investigate the appropriate parameters and time scale factors for MSE calculation from EEG signals. We then assessed the within-subject consistency of MSE measures in different EEG epochs and correlations of MSE measures to cognitive and neuropsychiatric symptoms of AD patients. Increased severity of AD was associated with decreased MSE complexity as measured by short-time scales, and with increased MSE complexity as measured by long-time scales. MSE complexity in EEGs of the temporal and occipitoparietal electrodes correlated significantly with cognitive function. MSE complexity of EEGs in various brain areas was also correlated to subdomains of neuropsychiatric symptoms. MSE analysis revealed abnormal EEG complexity across short- and long-time scales that were correlated to cognitive and neuropsychiatric assessments. The MSE-based EEG complexity analysis may provide a simple and cost-effective method to quantify the severity of cognitive and neuropsychiatric symptoms in AD patients.

Heart Rate Variability and Electrodermal Activity as Noninvasive Indices of Sympathovagal Balance in Response to Stress

Abstract The autonomic nervous system (ANS) is a principal regulatory system for maintaining homeostasis, adaptability and physiological flexibility of the organism at rest as well as in response to stress. In the aspect of autonomic regulatory inputs on the cardiovascular system, recent research is focused on the study of exaggerated/diminished cardiovascular reactivity in response to mental stress as a risk factor for health complications, e.g. hypertension. Thus, the analysis of biological signals reflecting a physiological shift in sympathovagal balance during stress in the manner of vagal withdrawal associated with sympathetic overactivity is important. The heart rate variability, i.e. “beat-to-beat” oscillations of heart rate around its mean value, reflects mainly complex neurocardiac parasympathetic control. The electrodermal activity could represent “antagonistic” sympathetic activity, the so-called “sympathetic arousal” in response to stress. The detailed study of the physiological parameters under various stressful stimuli and in recovery phase using traditional and novel mathematical analyses could reveal discrete alterations in sympathovagal balance. This article summarizes the importance of heart rate variability and electrodermal activity assessment as the potential noninvasive indices indicating autonomic nervous system activity in response to mental stress.

Entropy in the Analysis of Gait Complexity: A State of the Art

Human gait is a non-linear complex process requiring appropriate mathematical measuring tools. Entropy is a measure that quantifies regularity in time series: the more predictable a series is the lower the entropy value. The mathematical methods used to estimate entropy have evolved over time. At present, three algorithms are the most used to study human gait complexity: the approximate entropy (ApEn), the sample entropy (SampEn), and the multi-scale entropy (MSE). Most studies on human gait complexity have been conducted on elderly subjects or subjects with specific disorders affecting gait patterns and they used ApEn; but, because of a set of conceptual errors, the ApEn is not the most appropriate algorithm for the analysis of biological signals. Very few studies use SampEn or MSE to analyze human gait variability, but they agree that these algorithms might contribute new perspectives in the analysis of human gait and that MSE seems to be the most sensitive algorithm to changes in gait in healthy subjects.

Relaxant effects of Aike Mixture (艾可合剂) on isolated bladder and prostatic urethral smooth muscle of rabbits

To observe the relaxant effect of Aike Mixture (AKM) on isolated bladder and prostatic urethral smooth muscle of rabbits. The isolated bladder and prostatic urethral smooth muscle from male rabbits were placed in a Magnus bath and smooth muscle contraction was measured using a biological signal acquisition and analysis system. The effects of AKM in combination with methoxyamine, carbachol and CaCl2 on the contractile tension of muscle strips were determined by cumulative dosing. AKM dose-dependently reduced contractile tension of bladder trigone smooth muscle (r=0.831, P<0.05), reduced contractile wave amplitude (r=0.837, P<0.05) and decreased contractile frequency (r=-0.917, P<0.01). AKM significantly inhibited the increases in smooth muscle contraction induced by methoxyamine, carbachol and CaCl2. AKM dose-dependently inhibited the contraction of rabbit isolated bladder and prostatic urethral smooth muscle by antagonizing α1-adrenergic receptors and M-cholinergic receptors.

Biomedical Signal Processing and Modeling Complexity of Living Systems

Biomedical Signal Processing and Modeling Complexity of Living Systems

Editorial introductions

Editorial introductions