25OH维生素D水平检测与自闭症评定量表（CARS）评分的相关性及预测模型研究

摘要 目的：探究25OH维生素D（25(OH) D）水平检测与自闭症评定量表（CARS）评分的相关性及其评估自闭症严重程度的价值。方法：选取2020年4月~2022年3月在我院确诊的自闭症谱系障碍（ASD）患儿67例作为ASD组,并按照病情严重程度将所有患儿分为轻中度组46例,重度组36例。另募集来我科就诊无精神病史及家族史的健康查体儿童93例作为对照组。对比ASD组与对照组、ASD患儿轻中度组与重度组25(OH) D水平差异,分析ASD组患儿25(OH)D水平的影响因素,比较ASD组不同25(OH)D水平患儿CARS评分差异性,分析ASD患儿血清25(OH)D水平与CARS评分的相关性,并采用ROC曲线评估血清25(OH)D水平预估ASD严重程度的效能。结果：ASD组患儿血清25(OH) D水平显著低于于对照组（P<0.05）。相较于轻中度组,重度孤独症组患儿血清25(OH) D水平显著降低（P<0.05）。25(OH) D异常组患儿中母乳喂养、偏食及腹泻发生率显著高于25(OH) D正常组（P<0.05）。25(OH) D异常组患儿中CARS评分中的人际关系、模仿、情感反应、肢体动作、使用物体、对变化的适应、视觉反应、听觉反应及总分显著高于25(OH) D正常组（P<0.05）。CARS总分分与血清25(OH)D水平的成负相关性（r=-0.367,P=0.004）。血清25(OH)D水平预估ASD严重程度的AUC为0.716,敏感度为72.48%,特异度为78.65%。结论：血清25(OH)D在ASD患儿中成低表达,而且不同严重程度患儿血清25(OH)D差异表达,而且血清25(OH)D水平与CARS总分成负相关性,其作为评估ASD严重程度的生物标志物具有一定价值。     

Myocardial Infarction Detection and Localization Using Optimal Features Based Lead Specific Approach

ObjectivesObjective of this paper is to present a reliable and accurate technique for Myocardial Infarction (MI) detection and localization.Material and methodsStationary wavelet transform has been used to decompose the ECG signal. Energy, entropy and slope based features were extracted at specific wavelet bands from selected lead of ECG. k-Nearest Neighbors (kNN) with Mahalanobis distance function has been used for classification. Sensitivity (Se), specificity (Sp), positive predictivity (+P), accuracy (Acc), and area under the receiver operating characteristics curve (AUC) analyzed over 200 subjects (52 health control, 148 with MI) from Physikalisch-Technische Bundesanstalt (PTB) database has been used for performance analysis. To handle the imbalanced data adaptive synthetic (ADASYN) sampling approach has been adopted.ResultsFor detection of MI, the proposed technique has shown an AUC = 0.99, Se = 98.62%, Sp = 99.40%, PPR = 99.41% and Acc = 99.00% using 12 top ranked features, extracted from multiple leads of ECG and AUC = 0.99, Se = 98.34%, Sp = 99.77%, PPR = 99.77% and Acc = 99.05% using 12 features extracted from a single ECG lead (i.e. lead V5). For localization of MI, the proposed technique has an AUC = 0.99, Se = 98.78%, Sp = 99.86%, PPR = 98.80%, and Acc = 99.76% using 5 top ranked features from multiple leads of ECG and AUC = 0.98, Se = 96.47%, Sp = 99.60%, PPR = 96.49% and Acc = 99.28% using 8 features extracted from a single ECG lead (i.e. lead V3).ConclusionThus for MI detection and localization, the proposed technique is independent of time-domain ECG fiducial markers and can work using specific leads of ECG.     

Risk factors in autism spectrum disorder: A Tunisian case-control study

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental condition that causes disability in social interaction, communication, and restrictive and repetitive behaviors. Common environmental factors like prenatal, perinatal, and/or postnatal factors play a key role in ASD etiologies. Moreover, specific metabolic disorders can be associated with ASD.Subjects and methodsWe performed a retrospective case-control study in child psychiatry clinics, involving 51 children with ASD and 40 typical development controls (TDC).ResultsWe found a correlation between children being breastfed for less than 6 months, having fathers more than 40 years old at childbirth in ASD compared to TDC group. Our study also associated low blood cholesterol and low erythrocyte magnesium levels with increased risk for ASD.ConclusionFindings support the implication of total cholesterol (TC) and erythrocyte magnesium level in defining autism outcome.     

An Efficient Automated Algorithm for Distinguishing Normal and Abnormal ECG Signal

ObjectiveThe present study aims to simulate an alarm system for online detecting normal electrocardiogram (ECG) signals from abnormal ECG so that an individual's heart condition can be accurately and quickly monitored at any moment, and any possible serious dangers can be prevented.Materials and methodsFirst, the data from Physionet database were used to analyze the ECG signal. The data were collected equally from both males and females, and the data length varied between several seconds to several minutes. The heart rate variability (HRV) signal, which reflects heart fluctuations in different time intervals, was used due to the low spatial accuracy of ECG signal and its time constraint, as well as the similarity of this signal with the normal signal in some diseases. In this study, the proposed algorithm provided a return map as well as extracted nonlinear features of the HRV signal, in addition to the application of the statistical characteristics of the signal. Then, artificial neural networks were used in the field of ECG signal processing such as multilayer perceptron (MLP) and support vector machine (SVM), as well as optimal features, to categorize normal signals from abnormal ones.ResultsIn this paper, the area under the curve (AUC) of the ROC was used to determine the performance level of introduced classifiers. The results of simulation in MATLAB medium showed that AUC for MLP and SVM neural networks was 89.3% and 94.7%, respectively. Also, the results of the proposed method indicated that the more nonlinear features extracted from the ECG signal could classify normal signals from the patient.ConclusionThe ECG signal representing the electrical activity of the heart at different time intervals involves some important information. The signal is considered as one of the common tools used by physicians to diagnose various cardiovascular diseases, but unfortunately the proper diagnosis of disease in many cases is accompanied by an error due to limited time accuracy and hiding some important information related to this signal from the physicians' vision leading to the risks of irreparable harm for patients. Based on the results, designing the proposed alarm system can help physicians with higher speed and accuracy in the field of diagnosing normal people from patients and can be used as a complementary system in hospitals.     

Heart rate variability as a marker of cardiovascular dysautonomia in post-COVID-19 syndrome using artificial intelligence

IntroductionCardiovascular dysautonomia comprising postural orthostatic tachycardia syndrome (POTS) and orthostatic hypotension (OH) is one of the presentations in COVID-19 recovered subjects. We aim to determine the prevalence of cardiovascular dysautonomia in post COVID-19 patients and to evaluate an Artificial Intelligence (AI) model to identify time domain heart rate variability (HRV) measures most suitable for short term ECG in these subjects.MethodsThis observational study enrolled 92 recently COVID-19 recovered subjects who underwent measurement of heart rate and blood pressure response to standing up from supine position and a 12-lead ECG recording for 60 s period during supine paced breathing. Using feature extraction, ECG features including those of HRV (RMSSD and SDNN) were obtained. An AI model was constructed with ShAP AI interpretability to determine time domain HRV features representing post COVID-19 recovered state. In addition, 120 healthy volunteers were enrolled as controls.ResultsCardiovascular dysautonomia was present in 15.21% (OH:13.04%; POTS:2.17%). Patients with OH had significantly lower HRV and higher inflammatory markers. HRV (RMSSD) was significantly lower in post COVID-19 patients compared to healthy controls (13.9 ± 11.8 ms vs 19.9 ± 19.5 ms; P = 0.01) with inverse correlation between HRV and inflammatory markers. Multiple perceptron was best performing AI model with HRV(RMSSD) being the top time domain HRV feature distinguishing between COVID-19 recovered patients and healthy controls.ConclusionPresent study showed that cardiovascular dysautonomia is common in COVID-19 recovered subjects with a significantly lower HRV compared to healthy controls. The AI model was able to distinguish between COVID-19 recovered patients and healthy controls.     

Resting-state coupling between HbO and Hb measured by fNIRS in autism spectrum disorder

To distinguish between children with autism spectrum disorder (ASD) and typically developing (TD) children, we have uncovered a new discriminative feature, hemoglobin coupling. Functional near-infrared spectroscopy (fNIRS) was used to record resting-state hemodynamic fluctuations in the bilateral temporal lobes in 25 children with ASD and 22 TD children, in which the coupling between low frequency oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) fluctuations was evaluated by Pearson correlation coefficient. The results showed significantly weak coupling in children with ASD in both the left and right, and throughout the whole temporal cortex. To explain this observation, a simulation study was performed using a balloon model, in which we found four related parameters could impact the coupling. This study suggested that hemoglobin coupling might be applied as a new cerebral hemodynamic characteristic for ASD screening or diagnostics.     

Empirical Mode Decomposition and Wavelet Transform Based ECG Data Compression Scheme

ObjectiveIn health-care systems, compression is an essential tool to solve the storage and transmission problems. In this regard, this paper reports a new electrocardiogram (ECG) data compression scheme which employs sifting function based empirical mode decomposition (EMD) and discrete wavelet transform.MethodEMD based on sifting function is utilized to get the first intrinsic mode function (IMF). After EMD, the first IMF and four significant sifting functions are combined together. This combination is free from many irrelevant components of the signal. Discrete wavelet transform (DWT) with mother wavelet ‘bior4.4’ is applied to this combination. The transform coefficients obtained after DWT are passed through dead-zone quantization. It discards small transform coefficients lying around zero. Further, integer conversion of coefficients and run-length encoding are utilized to achieve a compressed form of ECG data.ResultsCompression performance of the proposed scheme is evaluated using 48 ECG records of the MIT-BIH arrhythmia database. In the comparison of compression results, it is observed that the proposed method exhibits better performance than many recent ECG compressors. A mean opinion score test is also conducted to evaluate the true quality of the reconstructed ECG signals.ConclusionThe proposed scheme offers better compression performance with preserving the key features of the signal very well.     

Paroxysmal atrial fibrillation recognition based on multi-scale wavelet <Emphasis Type="Italic">α</Emphasis>-entropy

Background

This study proposed an effective method based on the wavelet multi-scale α-entropy features of heart rate variability (HRV) for the recognition of paroxysmal atrial fibrillation (PAF). This new algorithm combines wavelet decomposition and non-linear analysis methods. The PAF signal, the signal distant from PAF, and the normal sinus signals can be identified and distinguished by extracting the characteristic parameters from HRV signals and analyzing their quantification indexes. The original ECG signals for QRS detection and HRV signal extraction are first processed. The features from the HRV signals are extracted as feature vectors using the wavelet multi-scale entropy. A support vector machine-based classifier is used for PAF prediction.

Results

The performance of the proposed method in predicting PAF episodes is evaluated with 100 signals from the MIT-BIT PAF prediction database. With regard to the dynamics and uncertainty of PAF signals, our proposed method obtains the values of 92.18, 94.88, and 89.48% for the evaluation criteria of correct rate, sensitivity, and specificity, respectively.

Conclusions

Our proposed method presents better results than the existing studies based on time domain, frequency domain, and non-linear methods. Thus, our method shows considerable potential for clinical monitoring and treatment.

     

Hair trace element concentrations in autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD)

BackgroundThe existing data demonstrate that alteration of trace element and mineral status in children with neurodevelopmental disorders including ASD and ADHD. However, comparative analysis of the specific patterns of trace element and mineral metabolism in children with ASD and ADHD was not performed. Therefore, the primary objective of the present study was to assess hair trace element and mineral levels in boys with ADHD, ASD, as well as ADHD with ASD.MethodsBoys with ADHD (n = 52), ASD (n = 53), both ADHD and ASD (n = 52), as well as neurotypical controls (n = 52) were examined. Hair analysis was performed using inductively-coupled plasma mass-spectrometry.ResultsThe obtained data demonstrate that hair Co, Mg, Mn, and V levels were significantly reduced in children with ADHD and ASD, and especially in boys with ADHD + ASD. Hair Zn was found to be reduced by 20% (p = 0.009) only in children with ADHD + ASD as compared to healthy controls. Factor analysis demonstrated that ASD was associated with significant alteration of hair Co, Fe, Mg, Mn, and V levels, whereas impaired hair Mg, Mn, and Zn content was also significantly associated with ADHD. In regression models hair Zn and Mg were negatively associated with severity of neurodevelopmental disorders. The revealed similarity of trace element and mineral disturbances in ASD and ADHD may be indicative of certain similar pathogenetic features.ConclusionThe obtained data support the hypothesis that trace elements and minerals, namely Mg, Mn, and Zn, may play a significant role in development of both ADHD and ASD. Improvement of Mg, Mn, and Zn status in children with ASD and ADHD may be considered as a nutritional strategy for improvement of neurodevelopmental disturbances, although clinical trials and experimental studies are highly required to support this hypothesis.     

Comparing the HRV Time-Series Signals Acquired from Cannabis Consuming and Non-Consuming Indian Paddy-Field Workers by Recurrence Quantification Analysis

ObjectiveIn the last few decades, the consumption of cannabis-based products for recreational purposes has dramatically increased. Unfortunately, cannabis consumption has been associated with the incidences of cardiovascular diseases. Hence, there is a necessity for understanding the plausible mechanics of cardiophysiological changes due to cannabis consumption. Accordingly, the current study was designed to understand the suitability of the recurrence quantification analysis (RQA) method in detecting the changes in the heart rate variability (HRV) time-series signals due to the consumption of cannabis (bhang). Further, a machine learning model has been proposed for the automated detection of the cannabis takers.Materials and MethodsThe RQA of the HRV time-series signals from 200 healthy Indian male paddy-field workers were carried out. The obtained parameters were statistically analyzed using the Mann-Whitney U test. Further, the decision trees, weight-based feature ranking, and dimensionality reduction methods were employed for identifying the relevant features for the development of a suitable machine learning model.ResultsObservable changes in the patterns of the recurrence plots among the bhang consuming and non-consuming groups were noticed. However, there were no significant differences in the RQA parameters. Among the developed machine learning models, the SVM model obtained from the “Information gain ratio” feature selection method exhibited the highest accuracy (%) of 69.09 ± 9.33.ConclusionOur study suggests that the RQA method is not as effective as the time and frequency domain methods for detecting the alterations in the HRV time-series signals due to cannabis consumption. The SVM model was found to be the best model for the automated detection of cannabis takers. The selection of the features by the information gain ratio method played an important role in the development of the optimized SVM model.     

Flexible Analytic Wavelet Transform Based Features for Physical Action Identification Using sEMG Signals

ObjectivesElectromyography (EMG) is recording of the electrical activity produced by skeletal muscles. The classification of the EMG signals for different physical actions can be useful in restoring some or all of the lost motor functionalities in these individuals. Accuracy in classifying the EMG signal indicates efficient control of prosthesis.Material and methodsThe flexible analytic wavelet transform (FAWT) is used for classification of surface electromyography (sEMG) signals for identification of physical actions. FAWT is an efficient method for decomposition of sEMG signal into eight sub-bands, features namely neg-entropy, mean absolute value (MAV), variance (VAR), modified mean absolute value type 1 (MAV1), waveform length (WL), simple square integral (SSI), Tsallis entropy, integrated EMG (IEMG) are extracted from the sub-bands. Extracted features are fed into an extreme learning machine (ELM) classifier with sigmoid activation function.ResultsComprehensive experiments are conducted on the input sEMG signals and the accuracy, sensitivity and specificity scores are used for performance measurement. Experiments showed that among all sub-bands, the seventh sub-band provided the best performance where the recorded accuracy, sensitivity and specificity values were 99.36%, 99.36% and 99.93%, respectively. The comparison results showed best efficiency of proposed method as compared to other methods on the same dataset.ConclusionThis paper investigates the usage of the FAWT and ELM on sEMG signal classification. The results show that the proposed method is quite efficient in classification of the sEMG signals. It is also observed that the seventh sub-band of the FAWT provides the best discrimination property. In the future works, recent wavelet transform methods will be used for improving the classification performance.     

Isolation,characterization and genetic analysis of canine <Emphasis Type="Italic">GATA4</Emphasis> gene in a family of Doberman Pinschers with an atrial septal defect

GATA4 is expressed early in the developing heart where it plays a key role in regulating the expression of genes encoding myocardial contractile proteins. Gene mutations in the human GATA4 have been implicated in various congenital heart defects (CHD), including atrial septal defect (ASD). Although ASD is the third most common CHD in humans, it is generally rare in dogs and cats. There is also no obvious predilection for ASD in dogs and cats, based on sex or breed. However, among dogs, the incidence rate of ASD is relatively high in Samoyeds and Doberman Pinschers, where its inheritance and genetic aetiology are not well understood. In this study, we identified and investigated the genetic aetiology of an ASD affected family in a pure breed dog population. Although the GATA4 gene was screened, we did not find any mutations that would result in the alteration of the coding sequence and hence, the predicted GATA4 structure and function. Although the aetiology of ASD is multifactorial, our findings indicate that GATA4 may not be responsible for the ASD in the dogs used in this study. However, this does not eliminate GATA4 as a candidate for ASD in other dog breeds.     

Prospective,Blinded Exploratory Evaluation of the PlayWisely Program in Children with Autism Spectrum Disorder

The purpose of the study was to explore a low-cost intervention that targets an increasingly common developmental disorder. The study was a blinded, exploratory evaluation of the PlayWisely program on autism symptoms and essential learning foundation skills (attention, recognition, and memory skills) in children with a diagnosis of autism, autism spectrum disorder (ASD), pervasive developmental disorder – not otherwise specified (PDD-NOS), and Asperger syndrome (AS). Eighteen children, 1 to 10 years of age, were evaluated using the Childhood Autism Rating Scale, Second Edition (CARS2); the PlayWisely Interactive Test of Attention, Recognition, and Memory Skills; Autism Treatment Evaluation Checklist (ATEC), and the Modified Checklist for Autism in Toddlers (M-CHAT). There were significant treatment effects for the PlayWisely measure on the Yellow Sets that examine recognition; Purple Sets that examine brain region agility and early memory skills; Blue Sets that examine phonemic awareness and recognition; and for the Total Sets, with a similar trend toward improvement in the Green Sets that examine perception and Red Sets that examine attention. No other measures reached statistical significance. The results suggest that PlayWisely can improve recognition, brain region agility, phonemic awareness, letter recognition, and early memory skills in ASD. It was observed by the parents, coaches, and study investigators that the children who were less than 3 years of age showed improvements in autism symptoms; however, the group was too small to reach statistical significance. Future studies are needed to see if this intervention can mitigate autism symptoms in very young children with ASD.     

Increased Risk of Autism Development in Children Whose Mothers Experienced Birth Complications or Received Labor and Delivery Drugs

Autism spectrum disorder (ASD) is a perplexing and pervasive developmental disorder characterized by social difficulties, communicative deficits, and repetitive behavior. The increased rate of ASD diagnosis has raised questions concerning the genetic and environmental factors contributing to the development of this disorder; meanwhile, the cause of ASD remains unknown. This study surveyed mothers of ASD and non-ASD children to determine possible effects of labor and delivery (L&D) drugs on the development of ASD. The survey was administered to mothers; however, the results were analyzed by child, as the study focused on the development of autism. Furthermore, an independent ASD dataset from the Southwest Autism Research and Resource Center was analyzed and compared. Indeed, L&D drugs are associated with ASD (p = .039). Moreover, the Southwest Autism Research and Resource Center dataset shows that the labor induction drug, Pitocin, is significantly associated with ASD (p = .004). We also observed a synergistic effect between administrations of L&D drugs and experiencing a birth complication, in which both obstetrics factors occurring together increased the likelihood of the fetus developing ASD later in life (p = .0003). The present study shows the possible effects of L&D drugs, such as Pitocin labor-inducing and analgesic drugs, on children and ASD.     

个体化快速心律失常虚拟介入手术体系的建立与临床应用价值研究

目的:建立个体化快速心律失常虚拟介入手术体系定位手术靶点并分析其临床应用价值。方法:收集2011年1月-2013年1月在我院进行射频消融手术治疗的室性早搏和房室折返性心动过速患者共120例,(其中室性早搏40例,房室折返性心动过速80例),平均年龄40.6±9.7岁,获取数字新电机记录18导体表心电图(ECG)、数字食道调搏图、心脏CT成像原始数据,并记录手术靶点。所有采集心电图和CT数据进行多模式序列识别系统的计算机辅助诊断(CAD)处理,然后再对处理后的数据进行分析。两名心内科医生人工对心电图进行分析定位,并不告知患者的临床资料及射频消融手术最终靶点定位结果,按照室性早搏和房室旁路的诊断定位标准进行诊断,随后两名医师对处理后的心电图进行诊断,再次得出诊断结果,以术中成功消融靶点定位诊断为金标准,分析,个体化快速心律失常虚拟介入手术体系定位手术靶点的特异性、敏感性、阳性预测值,阴性预测值等指标。结果:ECG+CAD组诊断准确度高于单独ECG组,ECG组ROC曲线下面积(Az)=0.742,95%可信区间[0.652-0.832];ECG+CAD组:Az=0.934,95%可信区间[0.882-0.985];ECG+CAD组:精确度0.908;敏感性:0.905;特异性:0.923;阳性预测值:0.818;阴性预测值:0.934,较单独ECG组明显提高。结论:与单独体表心电图定位诊断相比,虚拟介入手术体系显著提高快速心律失常诊靶点定位的准确度,临床应用价值更高。     

Alterations of urinary perchlorate levels in euthyroid postpubertal children with autism spectrum disorder

BackgroundPerchlorates ClO₄(⁻) are known environmental and food contaminants that act as inhibitors of iodine uptake by the thyroid gland; however, information concerning their possible association with the development of autism spectrum disorder (ASD) is still missing. The current study is first presenting the alterations in perchlorate urine levels in euthyroid children with ASD.ObjectivesTo examine urinary perchlorates and iodides in euthyroid children diagnosed with ASD, compared to age-, and BMI-matched neurotypical controls, and to verify the association between these two ions in ASD.MethodsIons were determined in 24 h urine samples determined by ion chromatography–conductivity cell detection (IC-CD) and ion chromatography–pulsed amperometric detection (IC-PAD) techniques, respectively, in a total of 130 postpubertal euthyroid children with normal BMI (the mean age 14.46 years, SD = 1.32; the mean BMI 20.6, SD = 1.37), divided into age- and BMI-matched groups of ASD patients and neurotypical, healthy children (control).ResultsThe ASD group presented with significantly higher perchlorate urine levels than the controls (median = 1.05 μg/L, interquartile range(IQR) = 1.5 versus median = 0.09 μg/L, IQR = 0.097, respectively), as well as lower iodide urine levels (median = 100.2 μg/L, IQR = 37 versus median = 156.95 μg/L, IQR = 26.11, respectively). The ASD group presented significantly lower TSH and higher free thyroid hormone (fT4, fT3) levels than the controls. In regression analyses, perchlorate urine levels showed significant positive relationships with normal BMI values and serum TSH, and inverse relationships with serum fT4. Urinary iodide levels showed significant inverse relationships with BMI values. The absence of ASD was associated with decreased odds of perchlorate urine levels (OR = 0.012, 95 % confidence interval [CI] 0.0002−0.76), and increased odds of iodide urine levels (OR = 1.15, 95 %CI 1.05–1.27).ConclusionsASD may have an independent and significant impact on perchlorate as well as iodide levels in urine of euthyroid lean postpubertal children. Perchlorate levels do not appear to be directly associated with iodide levels in euthyroid children.     

Isolation of Fetal ECG Signals from Abdominal ECG Using Wavelet Analysis

ObjectiveMonitoring the heartbeat of the fetus during pregnancy is a vital part in determining their health. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The demand for a reliable method of non-invasive fetal heart monitoring is of high importance.MethodElectrocardiogram (ECG) is a method of monitoring the electrical activity produced by the heart. The extraction of the fetal ECG (FECG) from the abdominal ECG (AECG) is challenging since both ECGs of the mother and the baby share similar frequency components, adding to the fact that the signals are corrupted by white noise. This paper presents a method of FECG extraction by eliminating all other signals using AECG. The algorithm is based on attenuating the maternal ECG (MECG) by filtering and wavelet analysis to find the locations of the FECG, and thus isolating them based on their locations. Two signals of AECG collected at different locations on the abdomens are used. The ECG data used contains MECG of a power of five to ten times that of the FECG.ResultsThe FECG signals were successfully isolated from the AECG using the proposed method through which the QRS complex of the heartbeat was conserved, and heart rate was calculated. The fetal heart rate was 135 bpm and the instantaneous heart rate was 131.58 bpm. The heart rate of the mother was at 90 bpm with an instantaneous heart rate of 81.9 bpm.ConclusionThe proposed method is promising for FECG extraction since it relies on filtering and wavelet analysis of two abdominal signals for the algorithm. The method implemented is easily adjusted based on the power levels of signals, giving it great ease of adaptation to changing signals in different biosignals applications.     

A modified Zeeman model for producing HRV signals and its application to ECG signal generation

Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of the challenges is to generate ECG signals with a wide range of waveforms, power spectra and variations in heart rate variability (HRV)--all of which are important indexes of human heart functions. In this paper we present a comprehensive model for generating such artificial ECG signals. We incorporate into our model the effects of respiratory sinus arrhythmia, Mayer waves and the important very low-frequency component in the power spectrum of HRV. We use a new modified Zeeman model for generating the time series for HRV, and a single cycle of ECG is produced by using a simple neural network. The importance of the work is the model's ability to produce artificial ECG signals that resemble experimental recordings under various physiological conditions. As such the model provides a useful tool to simulate and analyse the main characteristics of ECG, such as its power spectrum and HRV under different conditions. Potential applications of this model include using the generated ECG as a flexible signal source to assess the effectiveness of a diagnostic ECG signal-processing device.     

Automated detection of schizophrenia using optimal wavelet-based [... formula ...] norm features extracted from single-channel EEG

Schizophrenia (SZ) is a mental disorder, which affects the ability of human thinking, memory, and way of living. Manual screening of SZ patients is tedious, laborious and prone to human errors. Hence, we developed a computer-aided diagnosis (CAD) system to diagnose SZ patients accurately using single-channel electroencephalogram (EEG) signals. The EEG signals are nonlinear and non-stationary. Hence, we have used wavelet-based features to capture the hidden non-stationary nature present in the signal. First, the EEG signals are subjected to the the wavelet decomposition through six iterations, which yields seven sub-bands. The l1 norm is computed for each sub-band. The extracted norm features are disseminated to various classification algorithms. We have obtained the highest accuracy of 99.21% and 97.2% using K-nearest neighbor classifiers with ten-fold and leave-one-subject-out cross-validations. The developed single-channel EEG wavelet-based CAD model can help the clinicians to confirm the outcome of their manual screening and obtain an accurate diagnosis.