A Novel Feature Level Fusion for Heart Rate Variability Classification Using Correntropy and Cauchy-Schwarz Divergence

Heart rate variability (HRV) analysis has become a widely used tool for monitoring pathological and psychological states in medical applications. In a typical classification problem, information fusion is a process whereby the effective combination of the data can achieve a more accurate system. The purpose of this article was to provide an accurate algorithm for classifying HRV signals in various psychological states. Therefore, a novel feature level fusion approach was proposed. First, using the theory of information, two similarity indicators of the signal were extracted, including correntropy and Cauchy-Schwarz divergence. Applying probabilistic neural network (PNN) and k-nearest neighbor (kNN), the performance of each index in the classification of meditators and non-meditators HRV signals was appraised. Then, three fusion rules, including division, product, and weighted sum rules were used to combine the information of both similarity measures. For the first time, we propose an algorithm to define the weights of each feature based on the statistical p-values. The performance of HRV classification using combined features was compared with the non-combined features. Totally, the accuracy of 100% was obtained for discriminating all states. The results showed the strong ability and proficiency of division and weighted sum rules in the improvement of the classifier accuracies.     

基于多尺度熵和支持向量机的人体脑疲劳状态检测研究

目的探索多尺度熵结合支持向量机的方法是否可以有效检测人体脑疲劳状态,进而比较不同脑皮层位置的电极检测脑疲劳的效果。方法通过持续认知负荷任务建立脑疲劳模型,采用一款便携式脑电设备采集12名实验对象清醒和疲劳状态的脑电信号,以多尺度熵为特征,结合支持向量机算法对两种状态的脑电进行分类。结果在进行持续认知负荷任务后,实验对象的疲劳程度明显上升,NASA-TLX和KSS量表结果均具有显著的统计学差异(P<0.01);在额叶Fpz、顶叶Pz和枕叶Oz三个电极,实验对象清醒和疲劳状态脑电信号的平均分类准确率分别为92.16%、81.63%和90.54%,其中Fpz和Oz电极之间没有统计学差异(P>0.05),二者和Pz电极之间有显著的统计学差异(P<0.05)。结论多尺度熵结合支持向量机可以有效地对人体清醒和脑疲劳状态进行检测,Fpz和Oz电极比Pz电极的检测效果更好。     

A System for Automated Determination of Perioperative Patient Acuity

The widely used American Society of Anesthesiologists Physical Status (ASA PS) classification is subjective, requires manual clinician review to score, and has limited granularity. Our objective was to develop a system that automatically generates an ASA PS with finer granularity by creating a continuous ASA PS score. Supervised machine learning methods were used to create a model that predicts a patient’s ASA PS on a continuous scale using the patient’s home medications and comorbidities. Three different types of predictive models were trained: regression models, ordinal models, and classification models. The performance and agreement of each model to anesthesiologists were compared by calculating the mean squared error (MSE), rounded MSE and Cohen’s Kappa on a holdout set. To assess model performance on continuous ASA PS, model rankings were compared to two anesthesiologists on a subset of ASA PS 3 case pairs. The random forest regression model achieved the best MSE and rounded MSE. A model consisting of three random forest classifiers (split model) achieved the best Cohen’s Kappa. The model’s agreement with our anesthesiologists on the ASA PS 3 case pairs yielded fair to moderate Kappa values. The results suggest that the random forest split classification model can predict ASA PS with agreement similar to that of anesthesiologists reported in literature and produce a continuous score in which agreement in accurately judging granularity is fair to moderate.     

Feature Extraction Using Dominant Local Texture-Color Patterns (DLTCP) and Classification of Color Images

Feature extraction and classification are considered to be the major tasks in image processing applications. This paper proposes a novel method to extract the features of a color image for classification. The proposed method, Dominant Local Texture-Color Patterns (DLTCP) is based on the Dominant Texture and Dominant Color channels in a RGB color space. The dominant texture pattern represents a channel among RGB with maximum variations in the texture and the dominant color pattern represents the color channel with the maximum pixel intensity. The combination of channels with dominant texture pattern and dominant color pattern is assigned a unique value which is used to extract the features of an image. The proposed texture-color features is tested for rotational, illumination and scale invariance property using the color images taken from Outex and Vistex databases. It is experimentally shown that the proposed method achieves the highest accuracy in classification using K-Nearest Neighbor (KNN) classifier under various challenges.     

Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features

The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG.     

胎儿心电信号的无创提取：基于时间卷积编解码网络

目的 实现从孕妇腹壁混合心电信号中提取微弱的胎儿心电信号,为准确估计胎儿心率、分析胎儿心电波形等提供基础。方法 利用深度卷积网络（deep CNN）优越的非线性映射能力,本文提出了一种基于时间卷积编解码网络的非线性自适应噪声消除（nonlinear ANC）提取框架,以实现胎儿心电信号的有效提取。首先构建适用于处理胎儿心电信号的深度时间卷积网络（TCED-Net）模型作为非线性映射工具;然后以孕妇胸部心电信号为参考,利用该模型估计孕妇腹壁混合心电信号中的母体心电成分;最后从腹壁混合信号中减去所估计的母体心电成分,以得到完整的胎儿心电信号。实验利用合成心电数据（FECGSYNDB）和临床心电数据（NIFECGDB、PCDB）对方法性能进行测试与对比。结果 本文方法在FECGSYNDB上的胎儿R峰检测精度（[F1]值）、均方误差（MSE）和质量信噪比（qSNR）分别达到98.89 %,0.20和7.84;在NIFECGDB上的[F1]值达到99.1%;在 PCDB 上的[F1]值达到 98.61%。在不同数据集中较之 EKF（[F1=]93.84%）、ES-RNN([F1] =97.20% )和 AECG-DecompNet（[F1]=95.43%）等现有性能最佳的算法,本文方法的R峰检测精度指标分别高出5.05%、1.9%和3.18%,均优于现有最佳方法。结论 与现有算法相比,本文方法可以提取出更为清晰的胎儿心电信号,对孕期进行有效的胎儿健康监护具有一定的应用价值。     

Classification of Epilepsy Using High-Order Spectra Features and Principle Component Analysis

The classification of epileptic electroencephalogram (EEG) signals is challenging because of high nonlinearity, high dimensionality, and hidden states in EEG recordings. The detection of the preictal state is difficult due to its similarity to the ictal state. We present a framework for using principal components analysis (PCA) and a classification method for improving the detection rate of epileptic classes. To unearth the nonlinearity and high dimensionality in epileptic signals, we extract principal component features using PCA on the 15 high-order spectra (HOS) features extracted from the EEG data. We evaluate eight classifiers in the framework using true positive (TP) rate and area under curve (AUC) of receiver operating characteristics (ROC). We show that a simple logistic regression model achieves the highest TP rate for class “preictal” at 97.5% and the TP rate on average at 96.8% with PCA variance percentages selected at 100%, which also achieves the most AUC at 99.5%.     

基于肌音信号的虚拟假肢控制

将肌音（Mechanomyography, MMG）信号作为假肢控制的生理信号源,实现了对于虚拟假肢的抓放控制。针对手部在握紧张开动作过程中前臂肌肉声音信号,提取动作信号的7种时域特征并利用线性分类器进行分类识别,用以分辨手部动作类型,正确率为(95.63±2.55)%,并利用辨识结果产生控制信号实现对虚拟手的控制。结果表明肌音信号的动作判断具有很高的正确率,为利用肌音信号控制假肢提供了依据。     

An Intelligent Ballistocardiographic Chair using a Novel SF-ART Neural Network and Biorthogonal Wavelets

This paper presents a comparative analysis of novel supervised fuzzy adaptive resonance theory (SF-ART), multilayer perceptron (MLP) and Multi Layer Perceptrons (MLP) neural networks over Ballistocardiogram (BCG) signal recognition. To extract essential features of the BCG signal, we applied Biorthogonal wavelets. SF-ART performs classification on two levels. At first level, pre-classifier which is self-organized fuzzy ART tuned for fast learning classifies the input data roughly to arbitrary (M) classes. At the second level, post-classification level, a special array called Affine Look-up Table (ALT) with M elements stores the labels of corresponding input samples in the address equal to the index of fuzzy ART winner. However, in running (testing) mode, the content of an ALT cell with address equal to the index of fuzzy ART winner output will be read. The read value declares the final class that input data belongs to. In this paper, we used two well-known patterns (IRIS and Vowel data) and a medical application (Ballistocardiogram data) to evaluate and check SF-ART stability, reliability, learning speed and computational load. Initial tests with BCG from six subjects (both healthy and unhealthy people) indicate that the SF-ART is capable to perform with a high classification performance, high learning speed (elapsed time for learning around half second), and very low computational load compared to the well-known neural networks such as MLP which needs minutes to learn the training material. Moreover, to extract essential features of the BCG signal, we applied Biorthogonal wavelets. The applied wavelet transform requires no prior knowledge of the statistical distribution of data samples.     

基于睡眠脑电信号和深度学习的抑郁症识别研究

  目的  基于睡眠脑电信号,探索深度学习Vision Transformer（ViT）结合Transformer网络对抑郁症患者识别的有效性。  方法  首先对28例抑郁症患者和37例正常对照的睡眠脑电信号进行预处理,并将信号转为图像格式,保留其频域及空间域特征信息,之后将图像输送到ViT-Transformer编码网络,分别学习抑郁症患者和正常对照的快速眼动（rapid eye movement, REM）睡眠期和非快速眼动（non-rapid eye movement, NREM）睡眠期的脑电信号特征,并对抑郁症进行识别。  结果  基于ViT-Transformer网络,从不同脑电频率角度,发现delta、theta和beta波的组合对抑郁症识别具有比较好的结果。其中,REM期delta-theta-beta波组合的脑电信号特征对抑郁症识别的准确率达92.8%,精准率为93.8%,抑郁症患者的召回率为84.7%,F_0.5值为0.917±0.074;NREM期delta-theta-beta波组合的脑电信号特征对抑郁症的识别准确率为91.7%,精准率为90.8 %,召回率为85.2%,F_0.5值为0.914±0.062。此外,通过对整夜睡眠脑电的睡眠分期进行可视化,发现分类错误通常发生在睡眠期转期时。  结论  应用深度学习ViT-Transformer网络,本研究发现基于delta-theta-beta波组合的REM期睡眠脑电信号特征对抑郁症识别更有效。     

基于CLSTM的步态分类方法

步态分类在人体运动能量消耗评估等应用中具有重要意义,提高分类精度和降低对统计特征的依赖是步态分类的研究热点。采用传统的步态分类方法提取的步态特征用于细分化步态时不能得到较好的效果。考虑到步态的连续性和不同轴之间信号的相关性,本文提出了基于CLSTM的步态分类方法：采用卷积神经网络（CNN）操作,通过计算多轴步态数据提取步态特征;基于长短期记忆（LSTM）构建步态时间序列模型,学习步态特征图时间维度上的长期依赖性。基于USC-HAD数据集的实验结果表明,用此方法提取了步态序列特征,很好地利用了步态时间序列特点,提升了11种步态的分类精度。     

Human Identification Using Compressed ECG Signals

As a result of the increased demand for improved life styles and the increment of senior citizens over the age of 65, new home care services are demanded. Simultaneously, the medical sector is increasingly becoming the new target of cybercriminals due the potential value of users’ medical information. The use of biometrics seems an effective tool as a deterrent for many of such attacks. In this paper, we propose the use of electrocardiograms (ECGs) for the identification of individuals. For instance, for a telecare service, a user could be authenticated using the information extracted from her ECG signal. The majority of ECG-based biometrics systems extract information (fiducial features) from the characteristics points of an ECG wave. In this article, we propose the use of non-fiducial features via the Hadamard Transform (HT). We show how the use of highly compressed signals (only 24 coefficients of HT) is enough to unequivocally identify individuals with a high performance (classification accuracy of 0.97 and with identification system errors in the order of 10^?2).     

Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier

In this study, Random Forests (RF) classifier is proposed for ECG heartbeat signal classification in diagnosis of heart arrhythmia. Discrete wavelet transform (DWT) is used to decompose ECG signals into different successive frequency bands. A set of different statistical features were extracted from the obtained frequency bands to denote the distribution of wavelet coefficients. This study shows that RF classifier achieves superior performances compared to other decision tree methods using 10-fold cross-validation for the ECG datasets and the obtained results suggest that further significant improvements in terms of classification accuracy can be accomplished by the proposed classification system. Accurate ECG signal classification is the major requirement for detection of all arrhythmia types. Performances of the proposed system have been evaluated on two different databases, namely MIT-BIH database and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database. For MIT-BIH database, RF classifier yielded an overall accuracy 99.33 % against 98.44 and 98.67 % for the C4.5 and CART classifiers, respectively. For St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, RF classifier yielded an overall accuracy 99.95 % against 99.80 % for both C4.5 and CART classifiers, respectively. The combined model with multiscale principal component analysis (MSPCA) de-noising, discrete wavelet transform (DWT) and RF classifier also achieves better performance with the area under the receiver operating characteristic (ROC) curve (AUC) and F-measure equal to 0.999 and 0.993 for MIT-BIH database and 1 and 0.999 for and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, respectively. Obtained results demonstrate that the proposed system has capacity for reliable classification of ECG signals, and to assist the clinicians for making an accurate diagnosis of cardiovascular disorders (CVDs).     

Measuring Saliency of Features Using Signal-to-noise Ratios for Detection of Electrocardiographic Changes in Partial Epileptic Patients

Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. The feature vector, which is comprised of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of probabilistic neural networks (PNNs) used in classification of two types of electrocardiogram (ECG) beats (normal and partial epilepsy). In order to extract features representing the ECG signals, discrete wavelet transform was used. The PNNs used in the ECG signals classification were trained for the SNR screening method. The application results of the SNR screening method to the ECG signals demonstrated that classification accuracies of the PNNs with salient input features are higher than that of the PNNs with salient and non-salient input features.     

MRI在股骨头缺血性坏死分期中的价值

目的探讨股骨头缺血性坏死中X线分期与MRI信号分型之间的关系和MRI在评价早期股骨头缺血性坏死中的价值。方法40例确诊为股骨头缺血性坏死患者共64髋分别进行了X线和MRI评价,并对照了X线分期和MRI分型以明确二者之间的关系。结果MRI分期中A型中以0期和I期为主;B型中以Ⅰ期和Ⅱ期为主;C型中以Ⅱ期和Ⅲ期为主;D型中以Ⅲ期和Ⅳ期为主。结论X线分期和MRI分型之间有明显相关性,可以用MRI分型标准来进行股骨头缺血性坏死的MRI分期。并且在评价早期股骨头缺血性坏死方面MRI明显优于X线。     

EEG Signal Analysis: A Survey

The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.     

A New QRS Detection Method Using Wavelets and Artificial Neural Networks

We present a new method for detection and classification of QRS complexes in ECG signals using continuous wavelets and neural networks. Our wavelet method consists of four wavelet basis functions that are suitable in detection of QRS complexes within different QRS morphologies in the signal and thresholding technique for denoising and feature extraction. The results demonstrate that the proposed method is not only efficient for normal ECG signal analysis but also for various types of arrhythmic cardiac signals embedded in noise. For the classification stage, a feedforward neural network was trained with standard backpropagation algorithm. The classifier input features consisted of compact wavelet coefficients of QRS complexes that resulted in higher classification rates. We demonstrate the efficiency of our method with the average accuracy 97.2% in classification of normal and abnormal QRS complexes.     

Pairwise ANFIS Approach to Determining the Disorder Degree of Obstructive Sleep Apnea Syndrome

Obstructive sleep apnea syndrome (OSAS) is an important disease that affects both the right and the left cardiac ventricle. This paper presents a novel classification method called pairwise ANFIS based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and one against all method for detecting the obstructive sleep apnea syndrome. In order to extract the features related with OSAS, we have used the clinical features obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea (OSA) in patients clinically suspected of suffering from this disease. The clinical features obtained from Polysomnography Reports are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO₂ minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO₂ intervals bigger than 89%. Since ANFIS has output with one class, we have extended the output of ANFIS to multi class by means of one against all method to diagnose the OSAS that has four classes consisting of normal (25 subjects), mild OSAS (AHI = 5–15 and 14 subjects), middle OSAS (AHI < 15–30 and 18 subjects), and heavy OSAS (AHI > 30 and 26 subjects). The classification accuracy, sensitivity and specifity analysis, mean square error, and confusion matrix have been used to test the performance of proposed method. The obtained classification accuracies are 82.92%, 82.92%, 85.36%, and 87.80% for each class including normal, mild OSAS, middle OSAS, and heavy OSAS using ANFIS with one against all method with 50–50% train-test split, respectively. Combining ANFIS and one against all method that is firstly proposed by us was firstly applied for diagnosing the OSAS. The proposed method has produced very promising results in the detecting the obstructive sleep apnea syndrome.     

基于集成神经网络的类风湿关节炎中医证候分类研究

目的 针对基于AI技术的类风湿关节炎（RA）中医证候多标签分类中存在标签关联性差、泛化性能低等问题,提出构建一种集成神经网络模型来实现RA中医证候分类,并探究其中的特征重要性和风险因素,为RA的诊断和治疗提供参考。方法 本文提出一种集成神经网络模型实现RA中医证候分类。该模型采用一种基于多层神经网络的基分类器提取临床RA多标签样本的深层特征,增强RA特征区分度,根据协方差理论衡量标签相关性,调节分类器链的输入空间,减少RA错误信息传播和冗余度,采用集成学习方法减小分类器链中不合理标签序列对RA特征分类的影响。结果 该模型在10折交叉验证性能参数中表现出优秀的性能,其中汉明损失、1-错误率、准确率和F1值分别为0.0036、0.0248、97.52%、99.18%。与其他常用多标签分类器相比,该模型的性能更为优秀具有更好的分类性能。此外,本文分析了RA中医证候特征重要性,并挖掘了潜在的风险因素。结论 基于集成神经网络模型的RA中医证候分类器具有较高的分类精度和效率,对于RA的临床诊断和治疗具有重要参考价值。