Check Your Biosignals Here: A new dataset for off-the-person ECG biometrics

The Check Your Biosignals Here initiative (CYBHi) was developed as a way of creating a dataset and consistently repeatable acquisition framework, to further extend research in electrocardiographic (ECG) biometrics. In particular, our work targets the novel trend towards off-the-person data acquisition, which opens a broad new set of challenges and opportunities both for research and industry. While datasets with ECG signals collected using medical grade equipment at the chest can be easily found, for off-the-person ECG data the solution is generally for each team to collect their own corpus at considerable expense of resources. In this paper we describe the context, experimental considerations, methods, and preliminary findings of two public datasets created by our team, one for short-term and another for long-term assessment, with ECG data collected at the hand palms and fingers.     

急性酒精中毒患者对心电图的影响

目的探讨急性酒精中毒对心电图的变化以及其临床意义。方法通过对我院2010年7～10月收治的48例急性酒精中毒患者进行心电图动态观察,并检测血压和心率情况。结果急性酒精中毒病人中心电图变化表现为PR和Q-T间期延长,对血压和心率变化并无明显影响。结论急性酒精中毒患者可导致PR间期及Q-T间期延长。     

Automated ECG diagnostic P-wave analysis using wavelets

P-wave characteristics in the human ECG are an important source of information in the diagnosis of atrial conduction pathology. However, diagnosis by visual inspection is a difficult task since the P-wave is relatively small and noise masking is often present. This paper introduces novel wavelet characteristics derived from the continuous wavelet transform (CWT) which are shown to be potentially effective discriminators in an automated diagnostic process. Characteristics of the 12-lead ECG P-wave were derived using CWT and statistical methods. A normal control group and an abnormal (atrial conduction pathology) group were compared. The wavelet characteristics captured frequency, magnitude and variance components of the P-wave. The best individual characteristics (i.e. ones that significantly discriminated the groups) were entered into a linear discriminant analysis (LDA) for four different models: two-lead ECG, three-lead ECG, a derived three-lead ECG and a factor analysis solution consisting of wavelet characteristic loadings on the factors. A comparison was also made between wavelet characteristics derived form individual P-waves verses wavelet characteristics derived from a signal-averaged P-wave for each participant. These wavelet models were also compared to standard cardiological measures of duration, terminal force and duration divided by the PR segment. Results for the individual P-wave approach generally outperformed the standard cardiological measures and the signal-averaged P-wave approach. The best wavelet model on the basis of both classification performance and simplicity was the two-lead model that uses leads II and V1. It was concluded that the wavelet approach of automating classification is worth pursuing with larger samples to validate and extend the present study.     

Atrial fibrillation classification with artificial neural networks

The acquired 72 normal sinus rhythm ECGs and 80 ECGs with atrial fibrillation (AF) are decomposed with ‘db10’ Daebauchies wavelets at level 6 and power spectral density was calculated for each decomposed signal with Welch method. Average power spectral density was calculated for six subbands and normalized to be used as input to the neural network. Levenberg-Marquart backpropagation feed forward neural network was built from logarithmic sigmoid transfer functions in three-layer form. The trained network was tested on 24 normal and 28 AF state ECGs. The classification performance was accomplished as 100% accurate.     

PEDOT:PSS-modified cotton conductive thread for mass manufacturing of textile-based electrical wearable sensors by computerized embroidery

The textile industry has advanced processes that allow computerized manufacturing of garments at large volumes with precise visual patterns. The industry, however, is not able to mass fabricate clothes with seamlessly integrated wearable sensors, using its precise methods of fabrication (such as computerized embroidery). This is due to the lack of conductive threads compatible with standard manufacturing methods used in industry. In this work, we report a low-cost poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS)-modified cotton conductive thread (PECOTEX) that is compatible with computerized embroidery. The PECOTEX was produced using a crosslinking reaction between PEDOT:PSS and cotton thread using divinyl sulfone as the crosslinker. We extensively characterized and optimized our formulations to create a mechanically robust conductive thread that can be produced in large quantities in a roll-to-roll fashion. Using PECOTEX and a domestic computerized embroidery machine, we produced a series of wearable electrical sensors including a facemask for monitoring breathing, a t-shirt for monitoring heart activity and textile-based gas sensors for monitoring ammonia as technology demonstrators. PECOTEX has the potential to enable mass manufacturing of new classes of low-cost wearable sensors integrated into everyday clothes.     

Empirical Mode Decomposition and Principal Component Analysis implementation in processing non-invasive cardiovascular signals

Biomedical signals are relentlessly superimposed with interferences. The nonlinear processes which generate the signals and the interferences regularly exclude or limit the usage of classical linear techniques, and even of wavelet transforms, to decompose the signal.Empirical Mode Decomposition (EMD) is a nonlinear and adaptive technique to decompose data. Biomedical data has been one of its most active fields. EMD is fully data-driven, thus producing a variable number of modes. When applied to cardiovascular signals, the modes expressing cardiac-related information vary with the signal, the subject, and the measurement conditions. This makes problematic to reconstruct a noiseless signal from the modes EMD generates.To synthesize and recompose the results of EMD, Principal Component Analysis (PCA) was used. PCA is optimal in the least squares sense, removing the correlations between the modes EMD discovers, thus generating a smaller set of orthogonal components. As EMD-PCA combination seems profitable its impact is evaluated for non-invasive cardiovascular signals: ballistocardiogram, electrocardiogram, impedance and photo plethysmogram.These cardiovascular signals are very meaningful physiologically. Sensing hardware was embedded in a chair, thus acquiring also motion artefacts and interferences, which EMD-PCA aims at separating. EMD is seen to be important, because of its data adaptability, while PCA is a good approach to synthesize EMD outcome, and to represent only the cardiovascular portion of the signals.     

Invariant trajectory classification of dynamical systems with a case study on ECG

An invariant pattern recognition framework for classification of phase space trajectories of nonlinear dynamical systems is presented. Using statistical shape theory, known external influences can be discriminated from true changes of the system. The external effects are modeled as a transformation group acting on the phase space, and variation of the trajectories not explained by the transformations is accounted for using principal component analysis. The approach suggested is highly adaptable to a wide range of situations and individual differences.The methodology presented is applied to detect abnormalities in electrocardiograms. Results based on measured data indicate that the model developed is resistant to the effects of respiration and body position changes, which are abundant in ambulatory conditions and cause significant morphological artifacts in the signal. The results also show that the detection of an artificially induced acute myocardial infarction is achieved with high performance. Due to its low computational complexity, the method developed can be implemented in real-time. The method developed also adapts to morphological changes caused by various heart conditions.     

老年男性心室流出道室性早搏的临床特点及与冠心病的关系

目的探讨老年男性频发流出道室性早搏心电图特征及与冠心病的关系。方法回顾性分析111例检出频发室性早搏患者的24h动态心电图,分析起源于心室流出道室性早搏的QRS波群特征及所占比例。结果111例频发室性早搏中45例起源于心室流出道,41例为非流出道起源,25例为频发多源室性早搏,流出道室性早搏患者冠心病发生率明显低于其他两组(P<0.05)。结论部分老年男性室性早搏起源于心室流出道,其电生理特性与普通心肌细胞不同,具有明显的自律性和电生理各向异性,起源于心室流出道的室性早搏与心肌缺血无相关性。     

小波变换和支持向量机相融合的ECG身份识别

为了提高心电图（ECG）信号的身份识别正确率,提出一种小波变换和支持向量机相融合的ECG身份识别方法（IWT-ABC-SVM）。采用一种小波阈值函数对ECG进行去噪处理,提取ECG特征,将ECG特征输入到支持向量机中进行学习,采用人工蜂群算法优化支持向量机参数,建立ECG的身份识别模型,采用MIT-BIH心电图数据进行仿真测试。仿真结果表明,相对于其他识别方法,IWT-ABC-SVM提高了ECG身份识别的正确率和可靠性。