Muscle Fatigue Detection in EMG Using Time–Frequency Methods, ICA and Neural Networks

The electromyography (EMG) signals give information about different features of muscle function. Real-time measurements of EMG have been used to observe the dissociation between the electrical and mechanical measures that occurs with fatigue. The purpose of this study was to detect fatigue of biceps brachia muscle using time–frequency methods and independent component analysis (ICA). In order to realize this aim, EMG activity obtained from activated muscle during a phasic voluntary movement was recorded for 14 healthy young persons and EMG signals were observed in time–frequency domain for determination of fatigue. Time–frequency methods are used for the processing of signals that are non-stationary and time varying. The EMG contains transient signals related to muscle activity. The proposed method for the detection of muscle fatigue is automated by using artificial neural networks (ANN). The results show that ANN with ICA separates EMG signals from fresh and fatigued muscles, hence providing a visualization of the onset of fatigue over time. The system is adaptable to different subjects and conditions since the techniques used are not subject or workload regime specific.     

A Biomedical System Based on Artificial Neural Network and Principal Component Analysis for Diagnosis of the Heart Valve Diseases

Listening via stethoscope is a primary method, being used by physicians for distinguishing normally and abnormal cardiac systems. Listening to the voices, coming from the cardiac valves via stethoscope, upon the flow of the blood running in the heart, physicians examine whether there is any abnormality with regard to the heart. However, listening via stethoscope has got a number of limitations, for interpreting different heart sounds depends on hearing ability, experience, and respective skill of the physician. Such limitations may be reduced by developing biomedical based decision support systems. In this study, a biomedical-based decision support system was developed for the classification of heart sound signals, obtained from 120 subjects with normal, pulmonary and mitral stenosis heart valve diseases via stethoscope. Developed system was mainly comprised of three stages, namely as being feature extraction, dimension reduction, and classification. At feature extraction stage, applying Discrete Fourier Transform (DFT) and Burg autoregressive (AR) spectrum analysis method, features, representing heart sounds in frequency domain, were obtained. Obtained features were reduced in lower dimensions via Principal Component Analysis (PCA), being used as a dimension reduction technique. Heart sounds were classified by having the features applied as input to Artificial Neural Network (ANN). Classification results have shown that, dimension reduction, being conducted via PCA, has got positive effects on the classification of the heart sounds.     

新活素对急性心衰患者心率变异性的影响

目的观察新活素——冻干重组人脑利钠肽(Lyophilized Recombinant Human Brain Natriuretic Peptide,Lrh-BNP)治疗急性失代偿心力衰竭(Acute decompensated heart failure,ADHF)对心脏自主神经功能的影响。方法对照组49例,进行基础抗心衰治疗;Lrh-BNP组45例,在基础抗心衰治疗上静脉注射Lrh-BNP 24 h。所有患者治疗前后均进行24 h Holter检查,对Lrh-BNP治疗前后及对照组进行心率功率谱时域和频域分析。结果Lrh-BNP组治疗后心率变异性(HRV)值较对照组显著改善,其中时域指标:全部正常R-R间期的标准差(SDNN)、每5 min R-R间期均值的标准差(SDANNI)、SDNN的均值(SDNNI)、正常相邻R-R间期差值的均方根RMSSD、正常相邻R-R间期差值>50 ms的百分比(PNN50)、心率均显著改善(P<0.05);频域指标:总频谱(TF)、低频(LF)和高频(HF)亦明显改善(P<0.05)。结论Lrh-BNP可改善ADHF患者的心率变异性。     

Application of Classical and Model-Based Spectral Methods to Describe the State of Alertness in EEG

Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. In this study, EEG signals recorded from 30 subjects were processed by PC-computer using classical and model-based methods. The classical method (fast Fourier transform) and three model-based methods (Burg autoregresse, moving average, least-squares modified Yule–Walker autoregressive moving average methods) were selected for processing EEG signals to discriminate the alertness level of subject. Power spectra of EEG signals were obtained by using these spectrum analysis techniques. These EEG spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of vigilance state of subject. It is found that, FFT and MA methods have low spectral resolution, these two methods are not appropriate for the analysis of the a wake–sleep correlation. Burg AR and least-squares modified Yule–Walker ARMA methods' performance characteristics have been found extremely valuable for the determination of vigilance state of a healthy subject, because of their clear spectra.     

Comparison of AR and Welch Methods in Epileptic Seizure Detection

Brain is one of the most critical organs of the body. Synchronous neuronal discharges generate rhythmic potential fluctuations, which can be recorded from the scalp through electroencephalography. The electroencephalogram (EEG) can be roughly defined as the mean electrical activity measured at different sites of the head. EEG patterns correlated with normal functions and diseases of the central nervous system. In this study, EEG signals were analyzed by using autoregressive (parametric) and Welch (non-parametric) spectral estimation methods. The parameters of autoregressive (AR) method were estimated by using Yule–Walker, covariance and modified covariance methods. EEG spectra were then used to compare the applied estimation methods in terms of their frequency resolution and the effects in determination of spectral components. The variations in the shape of the EEG power spectra were examined in order to epileptic seizures detection. Performance of the proposed methods was evaluated by means of power spectral densities (PSDs). Graphical results comparing the performance of the proposed methods with that of Welch technique were given. The results demonstrate consistently superior performance of the covariance methods over Yule–Walker AR and Welch methods.     

Order determination in autoregressive modeling of diastolic heart sounds

Previous studies demonstrated that spectral analysis of diastolic heart sounds may provide valuable information for the detection of coronary artery disease. Although parametric modeling methods were successfully used to achieve this goal, and showed considerable performance, the accuracy and precision of the analysis is strongly dependent on the model order selected. In order to investigate the effects of model order selection on the analysis, diastolic heart sounds recorded from both normal and diseased patients were analyzed using the AR modeling, which is computationally the most efficient parametric spectral analysis method. The model orders were determined by using four different model order selection criteria. The results showed that the four criteria yielded different orders for the same data set. On the other hand, different criteria showed different performance in different measurement conditions. Effect of arbitrary order selection was also discussed. As a result, an optimal AR model order that may be used for every case was determined.     

妊高征孕妇压力反射功能的改变

目的 研究妊高征孕妇心率变异性（HRV）、血压变异性（BPV）和压力反射敏感性（BRS）的改变。方法 用无创伤测定和频谱分析方法,检测31名正常妊娠孕妇和19名中、重度妊高征孕妇的HRV、BPV和自发性BRS,数据用SPSS 10．0软件分析。结果 妊高征组和正常妊娠组相比,HRV各指标有下降趋势,但无统计学意义（P〉0．05）。妊高征孕妇BPV的低频成分显著高于正常妊娠孕妇（P〈0．05）,BRS较正常妊娠孕妇降低（P〈0．05）。结论 妊高征孕妇交感神经对血压调节的活动性较正常妊娠孕妇增强;压力反射功能受损可能是妊高征发病过程中一重要环节。     

呼吸音信号采集分析系统研制及临床应用

目的：研制呼吸音信号采集分析系统,以探讨正常人和呼吸系疾病患者呼吸音的频谱特征,及其与呼吸生理和病理生理之间的关系。方法：运用声电传感器技术和计算机软硬件技术建立起呼吸音信号计算机采集分析系统,对呼吸音信号进行采集、储存、显示和分析处理。结果：发作期哮喘的呼吸音频率增高,强度增大;哮喘缓解期与正常对照组无显著差别。这些与哮喘的病理生理特点。结论：呼吸音信号采集分析系统,能精确采集呼吸音信号;能储存、显示呼吸音波形;对正常人和哮喘患者呼吸音的频谱特征的分析,反映了呼吸生理和病理生理变化的特征;呼吸音信号采集分析系统的研制有助于临床疾病的诊断和疗效观察。     

Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree

In order to assist the diagnosis procedure of heart sound signals, this paper presents a new automated method for classifying the heart status using a rule-based classification tree into normal and three abnormal cases; namely the aortic valve stenosis, aortic insufficient, and ventricular septum defect. The developed method includes three main steps as follows. First, one cycle of the heart sound signals is automatically detected and segmented based on time properties of the heart signals. Second, the segmented cycle is preprocessed with the discrete wavelet transform and then largest Lyapunov exponents are calculated to generate the dynamical features of heart sound time series. Finally, a rule-based classification tree is fed by these Lyapunov exponents to give the final decision of the heart health status. The developed method has been tested successfully on twenty-two datasets of normal heart sounds and murmurs with success rate of 95.5%. The resulting error can be easily corrected by modifying the classification rules; consequently, the accuracy of automated heart sounds diagnosis is further improved.     

Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study

Heart rate variability (HRV) is often used to assess the risk of cardiovascular disease, and data on this can be obtained via electrocardiography (ECG). However, collecting heart rate data via photoplethysmography (PPG) is now a lot easier. We investigate the feasibility of using the PPG-based heart rate to estimate HRV and predict diseases. We obtain three months of PPG-based heart rate data from subjects with and without hypertension, and calculate the HRV based on various forms of time and frequency domain analysis. We then apply a data mining technique to this estimated HRV data, to see if it is possible to correctly identify patients with hypertension. We use six HRV parameters to predict hypertension, and find SDNN has the best predictive power. We show that early disease prediction is possible through collecting one’s PPG-based heart rate information.     

Biophysics of Heart Sounds and Its Application to Clinical Auscultation

Much research has been carried out recently into the means by which heart sounds and murmurs reach the stethoscope from their point of origin. Heart sounds originate as vibrations of the cardiac valves and travel as transverse vibrations with low velocity over the walls of the ventricles and great vessels. Where these structures are in contact with the thoracic surface they emerge, at the `auscultatory areas', and spread like ripples over the chest surface. Murmurs originate in the cavities receiving the blood stream, and are loudest in the cavity that is less distensible. Frequency, damping in transit and the possible misinterpretation of apparent `splitting' seen in phonocardiographic records are discussed. This basic knowledge of modes of transmission allows the interpretation of unusual locations of auscultatory areas in disease states, and explains some puzzling findings obtained with microphones mounted on cardiac catheters.     

A Simple Computer-Based Measurement and Analysis System of Pulmonary Auscultation Sounds

Listening to various lung sounds has proven to be an important diagnostic tool for detecting and monitoring certain types of lung diseases. In this study a computer-based system has been designed for easy measurement and analysis of lung sound using the software package DasyLAB. The designed system presents the following features: it is able to digitally record the lung sounds which are captured with an electronic stethoscope plugged to a sound card on a portable computer, display the lung sound waveform for auscultation sites, record the lung sound into the ASCII format, acoustically reproduce the lung sound, edit and print the sound waveforms, display its time-expanded waveform, compute the Fast Fourier Transform (FFT), and display the power spectrum and spectrogram.     

Detection of Abnormalities for Diagnosing of Children with Autism Disorders Using of Quantitative Electroencephalography Analysis

Quantitative electroencephalography (qEEG) has been used as a tool for neurophysiologic diagnostic. We used spectrogram and coherence values for evaluating qEEG in 17 children (13 boys and 4 girls aged between 6 and 11) with autism disorders (ASD) and 11 control children (7 boys and 4 girls with the same age range). Evaluation of qEEG with statistical analysis demonstrated that alpha frequency band (8–13 Hz) had the best distinction level of 96.4% in relaxed eye-opened condition using spectrogram criteria. The ASD group had significant lower spectrogram criteria values in left brain hemisphere, (p < 0.01) at F3 and T3 electrodes and (p < 0.05) at FP1, F7, C3, Cz and T5 electrodes. Coherence values at 171 pairs of EEG electrodes indicated that there are more abnormalities with higher values in the connectivity of temporal lobes with other lobes in gamma frequency band (36–44 Hz).     

心元胶囊对冠心病心率变异性的影响

目的观察心元胶囊治疗冠心病对心脏自主神经功能的影响。方法对照组94例,进行基础治疗;心元胶囊组94例,在基础治疗上加用心元胶囊3个月。所有患者治疗前后均进行24hHolter检查,对心元胶囊治疗前后及对照组进行心率功率谱时域和频域分析。结果心元胶囊组治疗后心率变异性（heart rate variability,HRV）值较对照组显著改善,其中时域指标：全部正常R—R间期的标准差（SDNN）、每5minR—R间期均值的标准差（SDANNI）、SDNN的均值（SDNNI）、正常相邻R—R间期差值的均方根（RMSSD）、正常相邻R-R间期差值〉50ms的百分比（PNN50）、心率均显著改善（P〈0．05）;频域指标：总频谱（TF）、低频（LF）和高频（HF）亦明显改善（P〈0．05）。结论心元胶囊可改善冠心病患者的HRV。     

慢性心衰中西医结合生存质量量表与SF-36量表的效度对比研究

目的以简明健康状况量表(以下简称SF-36量表)为标准参照,评价慢性心衰中西医结合生存质量量表的效度。方法采用横断面调查方法,在北京市5家医院内纳入63名慢性心力衰竭住院患者,在患者知情同意基础上采用慢性心衰中西医结合生存质量量表、SF-36量表调查其生存质量,对慢性心衰中西医结合生存质量量表的效度进行评价。结果相关系数法、逐步回归分析证实慢性心衰中西医结合生存质量量表具有较好的内容效度,因子分析显示其具有良好的结构效度,以SF-36量表作为标准参照显示其具有良好的标准效度(r=0.79,P0.01)。结论慢性心衰中西医结合生存质量量表具有较好的效度,可以作为心衰中西医结合生存质量的评价工具。     

Determining the Amount of Anesthetic Medicine to Be Applied by Using Elman’s Recurrent Neural Networks via Resilient Back Propagation

In this study, Elman recurrent neural networks have been defined by using conjugate gradient algorithm in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment. The feed forward neural networks are also used for comparison. The conjugate gradient algorithm is compared with back propagation (BP) for training of the neural Networks. The applied artificial neural network is composed of three layers, namely the input layer, the hidden layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. EEG data has been recorded with Nihon Kohden 9200 brand 22-channel EEG device. The international 8-channel bipolar 10–20 montage system (8 TB-b system) has been used in assembling the recording electrodes. EEG data have been recorded by being sampled once in every 2 milliseconds. The artificial neural network has been designed so as to have 60 neurons in the input layer, 30 neurons in the hidden layer and 1 neuron in the output layer. The values of the power spectral density (PSD) of 10-second EEG segments which correspond to the 1–50 Hz frequency range; the ratio of the total power of PSD values of the EEG segment at that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia.     

Low-Cost Compact ECG With Graphic LCD and Phonocardiogram System Design

Till today, many different ECG devices are made in developing countries. In this study, low cost, small size, portable LCD screen ECG device, and phonocardiograph were designed. With designed system, heart sounds that take synchronously with ECG signal are heard as sensitive. Improved system consist three units; Unit 1, ECG circuit, filter and amplifier structure. Unit 2, heart sound acquisition circuit. Unit 3, microcontroller, graphic LCD and ECG signal sending unit to computer. Our system can be used easily in different departments of the hospital, health institution and clinics, village clinic and also in houses because of its small size structure and other benefits. In this way, it is possible that to see ECG signal and hear heart sounds as synchronously and sensitively. In conclusion, heart sounds are heard on the part of both doctor and patient because sounds are given to environment with a tiny speaker. Thus, the patient knows and hears heart sounds him/herself and is acquainted by doctor about healthy condition.     

Design and Development of a Heart Rate Variability Analyzer

Heart rate variability (HRV), analysis gives an insight into the state of the autonomic nervous system which modulates the cardiac activity. Here a digital signal controller based handy device is developed which acquires the beat to beat time interval, processes it using techniques based on non-linear dynamics, fractal time series analysis, and information theory. The technique employed, that can give reliable results by assessing heart beat signals fetched for a duration of a few minutes, is a huge advantage over the already existing methodologies of assessing cardiac health, those being dependant on the tedious task of acquiring Electro Cardio Gram(ECG) signals, which in turn requires the subject to lie down at a stretch for a couple of hours. The sensor used, relies on the technique of Photoplethysmography, rendering the whole approach as noninvasive. The device designed, calculates parameters like, Largest Lyapunov Exponent, Fractal dimension, Correlation Dimension, Approximate Entropy and α-slope of Poincare plots, which based on the range in which they fall, the cardiac health condition of the subject can be assessed to even the extend of predicting upcoming disorders. The design of heart beat sensor, the technique used in the acquisition of heart beat data, the relevant algorithm developed for the analysis purpose, are presented here.     

慢性喘息型支气管炎患者的肺音分析

目的：探讨慢性喘息型支气管炎（慢喘支）患者喘鸣音的频谱特点。方法：利用传感器和微机等设备录取６例慢喘支患者治疗前后和８例单纯型慢性支气管炎患者及１０例正常人的呼吸音，并对其进行ＦＦＴ（ｆａｓｔ－Ｆｏｕｒｉｅｒｔｒａｎｓｆｏｒｍ）频谱分析。结果：发作期慢喘支吸气的频率比缓解期慢喘支、正常对照和单纯型慢支低，但呼气时的频率却较之有不同程度的增高，呼吸音强度也增大。呼气时的频率比吸气时高，强度无差异；缓解期与正常对照和单纯型慢支的呼吸音频率和强度无明显差异。研究中尚未发现慢喘支的呼吸音频率和强度与肺功能存在相关性。结论：慢喘支患者的频谱特点反映了疾病的演变过程，并有助于对慢喘支的临床疗效观察和进一步认识本病的病理生理变化。     

The recent advance in heart tissue engineering research

冠心病是现代社会危害公众健康的主要疾病之一,而现有的治疗手段并不能从根本上治愈冠心病所引起的心肌损害和心功能下降,心肌组织工程概念的提出有望为解决这一难题提供新途径。本文结合近年来的文献对心肌组织工程的国内外研究进展作一综述。