Optimized Tunable Q Wavelet Transform Based Drowsiness Detection from Electroencephalogram Signals

Early discernment of drivers drowsy state may prevent numerous worldwide road accidents. Electroencephalogram (EEG) signals provide valuable information about the neurological changes for discrimination of alert and drowsy state. A signal is decomposed into multi-components for the analysis of the physiological state. Tunable Q wavelet transform (TQWT) decomposes the signal into low-pass and high-pass sub-bands without a choice of wavelet. The information content captured by these sub-bands depends on the choice of decomposition parameters. Due to the non-stationary nature of EEG signals, the predefined decomposition parameters of TQWT lead to information loss and degrade system performance. Hence it is required to automate the decomposition parameters in accordance with the nature of signals. In this paper, an optimized tunable Q wavelet transform (O-TQWT) is proposed for the adaptive selection of decomposition parameters by using different optimization algorithms. Objective function as a mean square error (MSE) of decomposition is minimized by optimization algorithms. Optimum decomposition parameters are used to decompose the signals into sub-bands. Time-domain based features are excerpted from the sub-bands of O-TQWT. Highly discriminant features selected by using Kruskal Wallis test are used as an input to different classification techniques. Classification accuracy of 96.14% is achieved by least square support vector machine with radial basis function kernel which is better than the other existing methodologies using the same database.     

Segmentation of heart sounds based on dynamic clustering

The heart sound signal is first separated into cycles, where the cycle detection is based on an instantaneous cycle frequency. The heart sound data of one cardiac cycle can be decomposed into a number of atoms characterized by timing delay, frequency, amplitude, time width and phase. To segment heart sounds, we made a hypothesis that the atoms of a heart sound congregate as a cluster in time–frequency domains. We propose an atom density function to indicate clusters. To suppress clusters of murmurs and noise, weighted density function by atom energy is further proposed to improve the segmentation of heart sounds. Therefore, heart sounds are indicated by the hybrid analysis of clustering and medical knowledge. The segmentation scheme is automatic and no reference signal is needed. Twenty-six subjects, including 3 normal and 23 abnormal subjects, were tested for heart sound signals in various clinical cases. Our statistics show that the segmentation was successful for signals collected from normal subjects and patients with moderate murmurs.     

A robust heart sounds segmentation module based on S-transform

This paper presents a new module for heart sounds segmentation based on S-transform. The heart sounds segmentation process segments the PhonoCardioGram (PCG) signal into four parts: S1 (first heart sound), systole, S2 (second heart sound) and diastole. It can be considered one of the most important phases in the auto-analysis of PCG signals. The proposed segmentation module can be divided into three main blocks: localization of heart sounds, boundaries detection of the localized heart sounds and classification block to distinguish between S1 and S2. An original localization method of heart sounds are proposed in this study. The method named SSE calculates the Shannon energy of the local spectrum calculated by the S-transform for each sample of the heart sound signal. The second block contains a novel approach for the boundaries detection of S1 and S2. The energy concentrations of the S-transform of localized sounds are optimized by using a window width optimization algorithm. Then the SSE envelope is recalculated and a local adaptive threshold is applied to refine the estimated boundaries. To distinguish between S1 and S2, a feature extraction method based on the singular value decomposition (SVD) of the S-matrix is applied in this study. The proposed segmentation module is evaluated at each block according to a database of 80 sounds, including 40 sounds with cardiac pathologies.     

心音信号噪声消除的小波变换方法

心音信号幅值小,干扰多,采用常规的时、频域滤波方法往往不能收到良好的效果,本文根据信号和干扰在小波变换下的不同变化特性,利用二进小波变换的模极大值识别出心音信号中的干扰噪声的位置,剔除其相应的小波变换系数后,再通过小波逆变换重构出心音信号,并根据心音信号的特点选取了适当的母小波和分解尺度,给出了利用小波方法去噪前后的实际结果,结果表明,小波变换方法可有效地消除心音信号中的噪声干扰。     

Machine Learning Approach to Detect Cardiac Arrhythmias in ECG Signals: A Survey

Cardiac arrhythmia is a condition when the heart rate is irregular either the beat is too slow or too fast. It occurs due to improper electrical impulses that coordinates the heart beats. Sudden cardiac death may occurs due to some dangerous arrhythmias conditions. Hence the main objective of the electrocardiogram (ECG) analysis is to detect the life-threatening arrhythmias accurately for appropriate treatment in order to save life. Since the last decades, several methods were reported for automatic ECG beat classifications. In this work, we present a systematic review of the current state-of-the-art methods used to detect cardiac arrhythmia using on ECG signals. It includes the signal decomposition, feature extraction and machine learning approaches used for automatic detection and decision making process. The articles covers the pre-processing, detection of QRS complex, feature extraction and classification of ECG beats. Based on the past studies, it is understood that the automated approach using computer-aided decision making process is highly required for real-time detection of cardiac arrhythmias. The advantages and limitations of different methods are discussed and also the future scopes is highlighted in the process of effective detection of cardiac arrhythmias. This study could be beneficial for researchers to analyze the existing state-of-art techniques used in detection of arrhythmia conditions.     

运动后心力及心率同时遥测的心力恢复趋势与心率恢复趋势的对比研究

为了更加全面地研究和评估心肌的变时性和变力性,研制了能够在运动场地同时采集心力和心率的心音信号遥测系统。采集了50名体育系学生和30名其他系学生在完成规定运动量的登梯运动后,5min内连续变化的心音信号。对采集的数据经过3次样条插值、均匀采样和小波变换滤波后,得到了平滑的心力恢复趋势曲线和心率恢复趋势曲线。统计分析表明,大负荷运动量下的心脏储备主要来自于心力储备而不是心率储备(P<0.001),和其他系学生相比,体育系学生具有的高水平心功能不仅在于具有更大的心力储备指数(P<0.001),而且还在于具有更快的心力恢复速度(P<0.001)。同时检测心力恢复趋势与心率恢复趋势会有益于全面评估心脏功能。     

A hybrid on-line ECG segmenting system for long-term monitoring.

This paper introduces a new hybrid ECG beat segmenting system, which can be applied in the processing unit of single-channel, long-term ECG monitors for the on-line segmentation of the ECG signal. Numerous ECG segmentation techniques are already existing and applied, however sufficiently robust and reliable methods currently require more than one ECG signal channel and quite complex computations, which are practically not feasible in stand-alone, low-cost monitors. Our new system approach presents a time domain segmentation technique based on a priori physiological and morphological information of the ECG beat. The segmentation is carried out after classifying the ECG beat, using the linear approximation of the filtered ECG signal and considering the pathophysiological properties as well. The proposed algorithms require moderate computational power, allowing the practical realization in battery powered stand-alone long-term cardiac monitors or small-sized cardiac defibrillators. The prototype version of the system was implemented in Matlab. The test and evaluation of the system was carried out with the help of reference signal databases.     

Non-invasive diagnosis methods of coronary disease based on wavelet denoising and sound analyzing

The heart sound is the characteristic signal of cardiovascular health status. The objective of this project is to explore the correlation between Wavelet Transform and noise performance of heart sound and the adaptability of classifying heart sound using bispectrum estimation. Since the wavelet has multi-scale and multi-resolution characteristics, in this paper, the heart sound signal with different frequency ranges is decomposed through wavelet and displayed on different scales of the resolving wavelet result. According to distribution features of frequency of heart sound signals, the interference components in heart sound signal can be eliminated by selecting reconstruction coefficients. Comparing de-noising effects of four wavelets which are haar, db6, sym8 and coif6, the db6 wavelet has achieved an optimal denoising effect to heart sound signals. The de-noising result of contrasting different layers in the db6 wavelet shows that decomposing with five layers in db6 provide the optimal performance. In practice, the db6 wavelet also shows commendable denoising effects when applying to 51 clinical heart signals. Furthermore, through the clinic analyses of 29 normal signals from healthy people and 22 abnormal heart signals from coronary heart disease patients, this method can fairly distinguish abnormal signals from normal signals by applying bispectrum estimation to denoised signals via ARMA coefficients model.     

基于盒维数的心音信号分形特征研究

在传统盒维数的基础上,从尺度变化的角度,提出一种计算心音信号时域波形分形维数的新的二进盒维数算法,并给出了算法思想和估算方法;然后用该方法对正常心音和几种典型的病态心音的分形维数进行计算,并对其分形特征进行了研究．研究结果表明：心音信号具有明显的分形特征,分形维数能够反映心音信号的复杂程度,并且能够明显地区分正常心音和病态心音．     

A beat-by-beat analysis of electrocardiograms from cardiac transplant recipients

A software system is described for producing a beat-by-beat analysis of the electrocardiograms from patients after heart transplantation. Pacemaker spikes are automatically detected and eliminated from the signals. R waves are located by a robust and accurate two-step algorithm. Based on the variable length of single heart beats, the Fourier coefficients of three orthogonal surface leads and of two intracardiac leads are calculated beat-by-beat. Power spectra are then obtained by combining contributions from the variable fundamental frequency and its multiples (harmonics) into fixed frequency classes of 1 Hz width and averaging over 60–120 cardiac cycles. Additionally, averaged beat-by-beat power spectra are calculated for windowed QRS complexes and T waves. As by-products, single beat quantities, such as R-R and R-T intervals, averaged signals for all leads, and the orientational autocorrelation function of the electrical vector of the heart, are obtained. Following beat-by-beat evaluation, mean values and standard deviations are obtained for all quantities.     

An algorithm for FHR estimation from foetal phonocardiographic signals

The long-term foetal surveillance is often to be recommended. Hence, the fully non-invasive acoustic recording, through maternal abdomen, represents a valuable alternative to the ultrasonic cardiotocography. Unfortunately, the recorded heart sound signal is heavily loaded by noise, thus the determination of the foetal heart rate raises serious signal processing issues. In this paper, we present a new algorithm for foetal heart rate estimation from foetal phonocardiographic recordings. A filtering is employed as a first step of the algorithm to reduce the background noise. A block for first heart sounds enhancing is then used to further reduce other components of foetal heart sound signals. A complex logic block, guided by a number of rules concerning foetal heart beat regularity, is proposed as a successive block, for the detection of most probable first heart sounds from several candidates. A final block is used for exact first heart sound timing and in turn foetal heart rate estimation. Filtering and enhancing blocks are actually implemented by means of different techniques, so that different processing paths are proposed. Furthermore, a reliability index is introduced to quantify the consistency of the estimated foetal heart rate and, based on statistic parameters; [,] a software quality index is designed to indicate the most reliable analysis procedure (that is, combining the best processing path and the most accurate time mark of the first heart sound, provides the lowest estimation errors). The algorithm performances have been tested on phonocardiographic signals recorded in a local gynaecology private practice from a sample group of about 50 pregnant women. Phonocardiographic signals have been recorded simultaneously to ultrasonic cardiotocographic signals in order to compare the two foetal heart rate series (the one estimated by our algorithm and the other provided by cardiotocographic device). Our results show that the proposed algorithm, in particular some analysis procedures, provides reliable foetal heart rate signals, very close to the reference cardiotocographic recordings.     

Neuropeptides regulate the cardiac activity of a prosobranch mollusc,Rapana thomasiana

Summary Involvement of neuropeptides in the regulation of cardiac activity in a prosobranch mollusc, Rapana thomasiana, was studied physiologically as well as immunohistochemically. A catch-relaxing peptide (CARP) showed strong inhibitory effects on the heart with a lower threshold than acetylcholine. The action of CARP was in contrast to that of another neuropeptide, FMRFamide, which has previously been shown to enhance the heart beat. Benzoquinonium blocked the effects of acetylcholine and stimulation of right cardiac nerves 1 and 3b, but not those of CARP, suggesting that the effects of nerve stimulation are mainly due to the release of acetylcholine. Immunohistochemical examinations demonstrated that FMRFamide-like and CARP-like immunoreactive neurons are distributed in the visceral ganglia. Although a neuron appeared to show weak immunoreactivity to both antisera, evidence for the coexistence of peptides in a single neuron was not exhibited. Positive immunoreactivity to FMRFamide and CARP antisera also appeared in right cardiac nerves 1 and 3. In the heart, FMRFamide- and CARP-like immunoreactive fibers were restricted to the atrium and the aortic end of the ventricle, consistent with the morphological observation of innervation. The present results suggest that FMRFamide- and CARP-like peptides are involved in regulating the heart beat.     

Homeotic selector genes control the patterning of seven-up expressing cells in the Drosophila dorsal vessel

The linear cardiac tube of Drosophila, the dorsal vessel, is an important model organ for the study of cardiac specification and patterning in vertebrates. In Drosophila, the Hox segmentation gene abdominal-A (abd-A) is required for the specification of a functionally distinct heart region at the posterior of the dorsal vessel, from which blood is pumped anteriorly through a tube termed the aorta. Since we have previously shown that the posterior part of the aorta is specified during embryogenesis to form the adult heart during metamorphosis, we determined if the embryonic aorta is also patterned by the function of Hox segmentation genes. Using gain- and loss-of-function experiments, we demonstrate that the three Hox genes expressed in the posterior aorta and heart are sufficient to confer heart or posterior aorta fate throughout the dorsal vessel. Additionally, we demonstrate that Ultrabithorax and abd-A, but not Antennapedia, function to control cell number in the dorsal vessel. These studies add robustness to the model that homeotic selector genes pattern the Drosophila dorsal vessel, and further extend our understanding of how the cardiac tube is patterned in animal models.     

Dimensional analysis of nonlinear oscillations in brain, heart, and muscle

We present some numerical studies on the dimensional analysis of temporal oscillations observed in human electroencephalograms (EEG), heart rates, and muscle tremors. We show that it is insufficient to characterize the individual system by a single dimension value alone. We also present some detailed numerical analysis of the scaling structure of the attractors reconstructed from the time signal. Our methods are based on the concept of local gauge functions, which we derive from the raw signals and from transformed signals obtained through singular value decomposition. We are able to confirm and improve earlier results on the change of dimensionality of EEG signals. For heart rates we observe an increase of the dimensional complexity during sleep, and for muscle tremor data we find significant changes in the dimensionality depending on the isometrical contraction of the muscle. We attempt to indicate which factors are important in determining dimension estimates and where specific problems lie in each of the examples.     

Self-adaptive model-based ECG denoising using features extracted by mean shift algorithm

Denoising of electrocardiogram (ECG) is the fundamental technique for manual or automatic ECG diagnosis. Model-based denoising has attracted initial studies since the ECG dynamical model was established in 2003 and been demonstrated to outperform most model-less denoising methods. The focus of this paper is robust denoising of abnormal ECG signals, which do not satisfy the assumption in previous model-based studies that morphological or physiological variations are small from one beat to another. A mean shift based initializer is proposed to provide a much more robust estimation of initial model parameters for each heart beat. Together with physiological knowledge based wave sub-segmentation and enhanced strategies, the novel initializer has been demonstrated to achieve satisfactory performance for both normal and abnormal heart beats under both white and pink noises. Utilizing records from Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) database, this paper also applies various filters to denoise noisy signals and the denoising performances verify the availability and efficacy of the proposed denoising method.     

Dynamical systems analysis of arterial blood pressure signals in relation to heart rate fluctuations in chick embryos

We attempted a new approach based on a modern dynamical system theory to reconstruct the arterial blood pressure signals in relation to heart rate fluctuations of developing chick embryos. The dynamical systems approach in general is to model a phenomenon that is presented by a single time series record and approximate the dynamical property (e.g. heart rate fluctuations) of a system based only on information contained in a single-variable (arterial blood pressure) of the system. The time-series data of the arterial blood pressure was reconstructed in 3-dimensional space to draw characteristic orbits. Since the reconstructed orbits of the blood pressure should retain information contained in the pressure signals, we attempted to derive instantaneous heart rate (IHR) from the reconstructed orbits. The derived IHR presenting HR fluctuations coincided well with the IHR obtained conventionally from the peak-to-peak time intervals of the maximum blood pressure. Movements of the reconstructed orbits of the arterial blood pressure in 3-dimensional space reflected HR fluctuations (i.e. transient decelerations and accelerations).     

Fiber bragg grating sensor based device for simultaneous measurement of respiratory and cardiac activities

This paper reports a novel optical ballistocardiography technique, which is non‐invasive, for the simultaneous measurement of cardiac and respiratory activities using a Fiber Bragg Grating Heart Beat Device (FBGHBD). The unique design of FBGHBD offers additional capabilities such as monitoring nascent morphology of cardiac and breathing activity, heart rate variability, heart beat rhythm, etc., which can assist in early clinical diagnosis of many conditions associated with heart and lung malfunctioning. The results obtained from the FBGHBD positioned around the pulmonic area on the chest have been evaluated against an electronic stethoscope which detects and records sound pulses originated from the cardiac activity. In order to evaluate the performance of the FBGHBD, quantitative and qualitative studies have been carried out and the results are found to be reliable and accurate, validating its potential as a standalone medical diagnostic device. The developed FBGHBD is simple in design, robust, portable, EMI proof, shock proof and non‐electric in its operation which are desired features for any clinical diagnostic tool used in hospital environment.

     

The sensitive giant: the role of titin-based stretch sensing complexes in the heart

Every heart beat is not equal. As physiological demands of the cardiovascular system change, cardiac myocytes modulate contractile parameters including the rate and force of contraction. Adaptive responses require the sensing of biomechanical signals involving the interface between the contractile cytoskeleton (myofibrils) and the sarcolemma at specialized cell-cell junctions (intercalated discs) and cell-substrate adhesion complexes (costameres). Recent studies have shed insight into how protein complexes within cardiac myocytes sense biomechanical signals, processes required for normal adaptive or pathological responses. This new evidence suggests that complexes associated with the giant, myofibrillar protein titin sense myocyte stretch. Here, we discuss evidence supporting titin being an ideal biomechanical sensor.     

Biomimetic scaffold combined with electrical stimulation and growth factor promotes tissue engineered cardiac development

Toward developing biologically sound models for the study of heart regeneration and disease, we cultured heart cells on a biodegradable, microfabricated poly(glycerol sebacate) (PGS) scaffold designed with micro-structural features and anisotropic mechanical properties to promote cardiac-like tissue architecture. Using this biomimetic system, we studied individual and combined effects of supplemental insulin-like growth factor-1 (IGF-1) and electrical stimulation (ES). On culture day 8, all tissue constructs could be paced and expressed the cardiac protein troponin-T. IGF-1 reduced apoptosis, promoted cell-to-cell connectivity, and lowered excitation threshold, an index of electrophysiological activity. ES promoted formation of tissue-like bundles oriented in parallel to the electrical field and a more than ten-fold increase in matrix metalloprotease-2 (MMP-2) gene expression. The combination of IGF-1 and ES increased 2D projection length, an index of overall contraction strength, and enhanced expression of the gap junction protein connexin-43 and sarcomere development. This culture environment, designed to combine cardiac-like scaffold architecture and biomechanics with molecular and biophysical signals, enabled functional assembly of engineered heart muscle from dissociated cells and could serve as a template for future studies on the hierarchy of various signaling domains relative to cardiac tissue development.