A Comparison of ECG Signal Pre-processing Using FrFT,FrWT and IPCA for Improved Analysis

ObjectiveElectrocardiogram (ECG) is a diagnostic tool for recording electrical activities of the human heart non-invasively. It is detected by electrodes placed on the surface of the skin in a conductive medium. In medical applications, ECG is used by cardiologists to observe heart anomalies (cardiovascular diseases) such as abnormal heart rhythms, heart attacks, effects of drug dosage on subject's heart and knowledge of previous heart attacks. Recorded ECG signal is generally corrupted by various types of noise/distortion such as cardiac (isoelectric interval, prolonged depolarization and atrial flutter) or extra cardiac (respiration, changes in electrode position, muscle contraction and power line noise). These factors hide the useful information and alter the signal characteristic due to low Signal-to-Noise Ratio (SNR). In such situations, any failure to judge the ECG signal correctly may result in a delay in the treatment and harm a subject (patient) health. Therefore, appropriate pre-processing technique is necessary to improve SNR to facilitate better treatment to the subject. Effects of different pre-processing techniques on ECG signal analysis (based on R-peaks detection) are compared using various Figures of Merit (FoM) such as sensitivity (Se), accuracy (Acc) and detection error rate (DER) along with SNR.MethodsIn this research article, a new fractional wavelet transform (FrWT) has been proposed as a pre-processing technique in order to overcome the disadvantages of other existing commonly used techniques viz. wavelet transform (WT) and the fractional Fourier transform (FrFT). The proposed FrWT technique possesses the properties of multiresolution analysis and represents signal in the fractional domain which consists of representation in terms of rotation of signals in the time–frequency plane. In the literature, ECG signal analysis has been improvised using statistical pre-processing techniques such as principal component analysis (PCA), and independent component analysis (ICA). However, both PCA and ICA are prone to suffer from slight alterations in either signal or noise, unless the basis functions are prepared with a worldwide set of ECG. Independent Principal Component Analysis (IPCA) has been used to overcome this shortcoming of PCA and ICA. Therefore, in this paper three techniques viz. FrFT, FrWT and IPCA are selected for comparison in pre-processing of ECG signals.ResultsThe selected methods have been evaluated on the basis of SNR, Se, Acc and DER of the detected ECG beats. FrWT yields the best results among all the methods considered in this paper; 34.37dB output SNR, 99.98% Se, 99.96% Acc, and 0.036% DER. These results indicate the quality of biology-related information retained from the pre-processed ECG signals for identifying different heart abnormalities.ConclusionCorrect analysis of the acquired ECG signal is the main challenge for cardiologist due to involvement of various types of noises (high and low frequency). Twenty two real time ECG records have been evaluated based on various FoM such as SNR, Se, Acc and DER for the proposed FrWT and existing FrFT and IPCA preprocessing techniques. Acquired real-time ECG database in normal and disease situations is used for the purpose. The values of FoMs indicate high SNR and better detection of R-peaks in a ECG signal which is important for the diagnosis of cardiovascular disease. The proposed FrWT outperforms all other techniques and holds both analytical attributes of the actual ECG signal and alterations in the amplitudes of various ECG waveforms adequately. It also provides signal portrayals in the time-fractional-frequency plane with low computational complexity enabling their use practically for versatile applications.     

Inter-observer agreement,diagnostic sensitivity and specificity of animal-based indicators of young lamb welfare

A scientific literature review and consensus of expert opinion used the welfare definitions provided by the Farm Animal Welfare Council (FAWC) Five Freedoms as the framework for selecting a set of animal-based indicators that were sensitive to the current on-farm welfare issues of young lambs (aged ⩽6 weeks). Ten animal-based indicators assessed by observation – demeanour, response to stimulation, shivering, standing ability, posture, abdominal fill, body condition, lameness, eye condition and salivation were tested as part of the objective of developing valid, reliable and feasible animal-based measures of lamb welfare The indicators were independently tested on 966 young lambs from 17 sheep flocks across Northwest England and Wales during December 2008 to April 2009 by four trained observers. Inter-observer reliability was assessed using Fleiss's kappa (κ), and the pair-wise agreement with an experienced, observer designated as the ‘test standard observer’ (TSO) was examined using Cohen's κ. Latent class analysis (LCA) estimated the sensitivity (Se) and specificity (Sp) of each observer without assuming a gold standard and predicted the Se and Sp of randomly selected observers who may apply the indicators in the future. Overall, good levels of inter-observer reliability, and high levels of Sp were identified for demeanour (κ = 0.54, Se ⩾ 0.70, Sp ⩾ 0.98), stimulation (κ = 0.57, Se = 0.30 to 0.77, Sp ⩾ 0.98), shivering (κ = 0.55, Se = 0.37 to 0.85, Sp ⩾ 0.99), standing ability (0.54, Se ⩾ 0.80, Sp ⩾ 0.99), posture (κ = 0.45, Se ⩾ 0.56, Sp = 0.99), abdominal fill (κ = 0.44, Se = 0.39 to 0.98, Sp = 0.99), body condition (κ = 0.72, Se ⩾ 0.38 to 0.90, Sp = 0.99), lameness (κ = 0.68, Se > 0.73, Sp = 1.00), and eye condition (κ = 0.72, Se ⩾ 0.86, Sp = 0.99). LCA predicted that randomly selected observers had Se > 0.77 (acceptable), and Sp ⩾ 0.98 (high) for assessments of demeanour, lameness, abdominal fill posture, body condition and eye condition. The diagnostic performance of some indicators was influenced by the composition of the study population, and it would be useful to test the indicators on lambs with a greater level of outcomes associated with poor welfare. The findings presented in this paper could be applied in the selection of valid, reliable and feasible indicators used for the purposes of on-farm assessments of lamb welfare.     

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.     

Immuno-enzymatic evaluation of the recombinant TSSA-II protein of Trypanosoma cruzi in dogs and human sera: a tool for epidemiological studies

The rTSSA-II (recombinant Trypomastigote Small Surface II) antigen was evaluated by ELISA to detect anti-Trypanosoma cruzi antibodies in sera from naturally infected dogs and humans. For this evaluation ELISA-rTSSA-II was standardized and groups were classified according to the results obtained through xenodiagnosis, ELISA and PCR. Sensitivity (Se), Specificity (Sp), Kappa index (KI) and area under curve (AUC) were determined. The Se was determined by using 14 sera from dogs infected with T. cruzi VI (TcVI) whereas Sp was determined by using 95 non-chagasic sera by xenodiagnosis, ELISA-Homogenate and PCR. The performance of ELISA-rTSSA-II in dog sera was high (AUC=0·93 and KI=0·91). The Se was 92·85% (1 false negative) and Sp was 100%. Two sera from dogs infected with TcI and 1 with TcIII were negative. For patients infected with T. cruzi, reactivity was 87·8% (36/41), there was only 1 indeterminate, and Sp was 100%. Fifty-four sera from non-chagasic and 68 sera from patients with cutaneous leishmaniasis did not react with rTSS-II. ELISA-rTSSA-II showed a high performance when studying sera from naturally infected dogs and it also presented 100% Sp. This assay could be an important tool to carry out sero-epidemiological surveys on the prevalence of T. cruzi circulating lineages in the region.     

Diagnostic value of the electrocardiogram in the assessment of prior myocardial infarction

BackgroundThe best available imaging technique for the detection of prior myocardial infarction (MI) is cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE). Although the electrocardiogram (ECG) still plays a major role in the diagnosis of prior MI, the diagnostic value of the ECG remains uncertain. This study evaluates the diagnostic value of the ECG in the assessment of prior MI.MethodsIn this retrospective study, data from electronic patient files were collected of 1033 patients who had undergone CMR with LGE between January 2014 and December 2017. After the exclusion of 59 patients, the data of 974 patients were analysed. Twelve-lead ECGs were blinded and evaluated for signs of prior MI by two cardiologists separately. Disagreement in interpretation was resolved by the judgement of a third cardiologist. Outcomes of CMR with LGE were used as the gold standard.ResultsThe sensitivity of the ECG in the detection of MI was 38.0% with a 95% confidence interval (CI) of 31.6–44.8%. The specificity was 86.9% (95% CI 84.4–89.1%). The positive and negative predictive value were 43.6% (95% CI 36.4–50.9%) and 84.0% (95% CI 81.4–86.5%) respectively. In 170 ECGs (17.5%), the two cardiologists disagreed on the presence or absence of MI. Inter-rater variability was moderate (κ 0.51, 95% CI 0.45–0.58, p < 0.001).ConclusionThe ECG has a low diagnostic value in the detection of prior MI. However, if the ECG shows no signs of prior MI, the absence of MI is likely. This study confirms that a history of MI should not be based solely on an ECG.     

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。     

Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancer

BackgroundThe prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients.ObjectivesTo develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images.Materials and MethodsThis retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment. Two integrated models were developed using radiomic and deep-learning-based features. First, we partitioned pre-chemotherapy CT images into spheres and shells with different radii around the tumor (0–3, 3–6, 6–9, 9–12, 12–15 mm) containing intratumoral and peritumoral regions. Second, we extracted radiomic and deep-learning-based features from each partition. Third, using radiomic features, five sphere–shell models, one feature fusion model, and one image fusion model were developed. Finally, the model with the best performance was validated in two cohorts.ResultsAmong the five partitions, the model of 9–12 mm achieved the highest area under the curve (AUC) of 0.87 (95% confidence interval: 0.77–0.94). The AUC was 0.94 (0.85–0.98) for the feature fusion model and 0.91 (0.82–0.97) for the image fusion model. For the model integrating radiomic and deep-learning-based features, the AUC was 0.96 (0.88–0.99) for the feature fusion method and 0.94 (0.85–0.98) for the image fusion method. The best-performing model had an AUC of 0.91 (0.81–0.97) and 0.89 (0.79–0.93) in two validation sets, respectively.ConclusionsThis integrated model can predict the response to chemotherapy in NSCLC patients and assist physicians in clinical decision-making.     

Time Domain Analysis of Heart Rate Variability Signals in Valence Recognition for Children with Autism Spectrum Disorder (ASD)

BackgroundAutism Spectrum Disorder (ASD) is a neurodevelopmental condition that is characterized by various social impairments. Children with ASD have major difficulties in expressing themselves, resulting in stress and meltdowns. Understanding their hidden feelings and needs may help in tackling and avoiding such strenuous behaviors.ObjectiveThis research aims to aid the parents and caretakers of children with ASD to understand the hidden and unexpressed emotional state by using physiological signals obtained from wearable devices.MethodsHere, electrocardiogram (ECG) signals pertaining to two valence states (‘like’ and ‘dislike’) were recorded from twenty children (10 Control and 10 children with ASD). The heart rate variability (HRV) signals were then obtained from the ECG signals using the Pan-Tompkins's algorithm. The statistical, higher order statistics (HOS) and geometrical features which were statistically significant were trained using the K Nearest Neighbor (KNN) and Ensemble Classifier algorithms.ResultsThe findings of our analysis indicate that the integration of major statistical features resulted in an overall average accuracy of 84.8% and 75.3% using HRV data for the control and test population, respectively. Similarly, geometrical features resulted in a maximum average accuracy of 84.8% and 74.2% for control and test population respectively. The decreased HRV in the test population indicates the presence of autonomic dysregulation in children with ASD when compared to their control peers.     

Sensitivity and specificity of culture and PCR of smegma samples of bulls experimentally infected with Tritrichomonas foetus

The sensitivity (Se) and specificity (Sp) of different testing schemes were estimated for detecting Tritrichomonas foetus (T. foetus) in smegma samples from experimentally infected bulls. Culture and polymerase chain reaction (PCR) on smegma samples were evaluated alone and in parallel testing. Mature dairy bulls (n=79) were intrapreputially inoculated with T. foetus (n=19); Campylobacter (C.) fetus venerealis (n=13); both T. foetus and C. fetus venerealis (n=11); Tetratrichomonas spp. (n=9); C. fetus fetus (n=8); or were not inoculated (n=19). For each bull, smegma samples were collected for 6 week post-inoculation and tested for T. foetus by In Pouch TF culture and PCR. Most T. foetus-inoculated bulls became infected, according to culture (86.7%), PCR (90.0%), and both tests together (93.3%). In T. foetus-inoculated bulls, both tests combined in parallel on a single sample had a Se (78.3%) and Sp (98.5%) similar to two cultures (Se 76.0%, Sp 98.5%) or two PCR (Se 78.0%, Sp 96.7%) sampled on consecutive weeks. The PCR on three consecutive weekly samples (Se 85.0%, Sp 95.4%) and both tests applied in parallel on three consecutive weekly samples (Se 87.5%, Sp 95.6%) were similar to the current gold-standard of six weekly cultures (Se 86.7% and Sp 97.5%). Both tests used in parallel six times had the highest Se (93.3%), with similar Sp (92.5%). Tetratrichomonas spp. were only sporadically detected by culture or PCR. In conclusion, we have proposed alternative strategies for T. foetus diagnostics (for the AI industry), including a combination of tests and repeat testing strategies that may reduce time and cost for bull surveillance.     

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

ObjectivesWhen the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.Material and methodsThe sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.ResultsIn this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.ConclusionThe findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.     

Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients–The SaMi-Trop cohort

BackgroundLeft ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm showed excellent accuracy to detect LVSD in a general population, but its accuracy in ChD has not been tested.ObjectiveTo analyze the ability of AI to recognize LVSD in patients with ChD, defined as a left ventricular ejection fraction determined by the Echocardiogram ≤ 40%.Methodology/principal findingsThis is a cross-sectional study of ECG obtained from a large cohort of patients with ChD named São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) Study. The digital ECGs of the participants were submitted to the analysis of the trained machine to detect LVSD. The diagnostic performance of the AI-enabled ECG to detect LVSD was tested using an echocardiogram as the gold standard to detect LVSD, defined as an ejection fraction <40%. The model was enriched with NT-proBNP plasma levels, male sex, and QRS ≥ 120ms.Among the 1,304 participants of this study, 67% were women, median age of 60; there were 93 (7.1%) individuals with LVSD. Most patients had major ECG abnormalities (59.5%). The AI algorithm identified LVSD among ChD patients with an odds ratio of 63.3 (95% CI 32.3–128.9), a sensitivity of 73%, a specificity of 83%, an overall accuracy of 83%, and a negative predictive value of 97%; the AUC was 0.839. The model adjusted for the male sex and QRS ≥ 120ms improved the AUC to 0.859. The model adjusted for the male sex and elevated NT-proBNP had a higher accuracy of 0.89 and an AUC of 0.874.ConclusionThe AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD.     

A Comparative Approach for MI-Based EEG Signals Classification Using Energy,Power and Entropy

ObjectiveThe initial principal task of a Brain-Computer Interfacing (BCI) research is to extract the best feature set from a raw EEG (Electroencephalogram) signal so that it can be used for the classification of two or multiple different events. The main goal of the paper is to develop a comparative analysis among different feature extraction techniques and classification algorithms.Materials and methodsIn this present investigation, four different methodologies have been adopted to classify the recorded MI (motor imagery) EEG signal, and their comparative study has been reported. Haar Wavelet Energy (HWE), Band Power, Cross-correlation, and Spectral Entropy (SE) based Cross-correlation feature extraction techniques have been considered to obtain the necessary features set from the raw EEG signals. Four different machine learning algorithms, viz. LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis), Naïve Bayes, and Decision Tree, have been used to classify the features.ResultsThe best average classification accuracies are 92.50%, 93.12%, 72.26%, and 98.71% using the four methods. Further, these results have been compared with some recent existing methods.ConclusionThe comparative results indicate a significant accuracy level performance improvement of the proposed methods with respect to the existing one. Hence, this presented work can guide to select the best feature extraction method and the classifier algorithm for MI-based EEG signals.     

Automated Classification of Fatty and Normal Liver Ultrasound Images Based on Mutual Information Feature Selection

Background

Fatty Liver Disease (FLD) is one of the most critical diseases that should be detected and cured at the earlier stage in order to decrease the mortality rate. To identify the FLD, ultrasound images have been widely used by the radiologists. However, due to poor quality of ultrasound images, they found difficulties in recognizing FLD. To resolve this problem, many researchers have developed various Computer Aided Diagnosis (CAD) systems for the classification of fatty and normal liver ultrasound images. However, the performance of existing CAD systems is not good in terms of sensitivity while classifying the FLD.

Cardiovascular implantable electronic device lead removal in a resource-constrained setting: A single-center experience from India

BackgroundData from large-volume centers in developed countries, using dedicated tools, show a high success rate with a good safety record for the percutaneous lead removal procedure. However, there are constraints to replicate the results in a resource-poor setting and there is limited data from India.MethodsWe retrospectively analyzed lead removal procedures performed in our institution from 2008 to 2019.ResultsSeventy-five patients underwent percutaneous removal of 138 leads. Of these, 44 procedures and 80 leads qualified as extraction with a median dwell time of 52.1 (IQR 28.2–117.2) months. Overall, 33/44 (75.0%) procedures were successful and 65/80 (81.2%) leads were successfully extracted. Manual traction was successful in the extraction of 44/57 (77.2%) leads. All leads implanted less than 2.7 years could be removed with manual traction alone. Specialized tools were used in 23 leads and 21 (91.3%) of those could be successfully extracted. Inability to use dedicated tools was an independent predictor of procedural failure (adjusted OR 14.0; 95% CI 1.8–110.2; p-value 0.012). Right-sided implant (adjusted OR 12.6; 95% CI 1.3–119.5; p-value 0.027) was also independently associated with failure. There was 1 death (1.3%) and minor complications occurred in 6 (8.0%) patients.ConclusionsIn a resource-limited setting, percutaneous lead extraction of predominantly pacemaker leads by manual traction methods achieved success in extracting about three-fourths of the leads. Inability to use specialized tools was the main factor limiting success. The complication rate was low.     

Fundus Retinal Vessels Image Segmentation Method Based on Improved U-Net

ObjectivesAlthough the segmentation of retinal vessels in the fundus is of great significance for screening and diagnosing retinal vascular diseases, it remains difficult to detect the low contrast and the information around the lesions provided by retinal vessels in the fundus and to locate and segment micro-vessels in the fine-grained area. To overcome this problem, we propose herein an improved U-Net segmentation method NoL-UNet.Material and methodsThis work introduces NoL-UNet. First of all, the ordinary convolution block of the U-Net network is changed to random dropout convolution blocks, which can better extract the relevant features of the image and effectively alleviate the network overfitting. Next, a NoL-Block attention mechanism added to the bottom of the encoding-decoding structure expands the receptive field and enhances the correlation of pixel information without increasing the number of parameters.ResultsThe proposed method is verified by applying it to the fundus image datasets DRIVE, CHASE_DB1, and HRF. The AUC for DRIVE, CHASE_DB1 and HRF is 0.9861, 0.9891 and 0.9893, Se for DRIVE, CHASE_DB1 and HRF is 0.8489, 0.8809 and 0.8476, and the Acc for DRIVE, CHASE_DB1 and HRF is 0.9697, 0.9826 and 0.9732, respectively. The total number of parameters is 1.70M, and for DRIVE, it takes 0.050s to segment an image.ConclusionOur method is statistically significantly different from the U-Net method, and the improved method shows superior performance with better accuracy and robustness of the model, which has good practical application in auxiliary diagnosis.     

Arm exercise stress perfusion imaging predicts clinical outcome

Treadmill exercise capacity in resting metabolic equivalents (METs) and stress hemodynamic, electrocardiographic (ECG), and myocardial perfusion imaging (MPI) responses are independently predictive of adverse clinical events. However, limited data exist for arm ergometer stress testing (AXT) in patients who cannot perform leg exercise because of lower extremity disabilities. We sought to determine the extent to which AXT METs, hemodynamic, ECG, and MPI responses to arm exercise add independent incremental value to demographic and clinical variables for prediction of all-cause mortality, myocardial infarction (MI), or late coronary revascularization, individually or as a composite. A prospective cohort of 186 patients aged 64 ± 10 (SD) yr, unable to perform lower extremity exercise, underwent AXT MPI for clinical reasons between 1997 and 2002, and were followed for 62 ± 23 mo, to an endpoint of death or 12/31/2006. Average annual rates were 5.4% for mortality, 2.2% for MI, 2.5% for late coronary revascularization, and 8.0% for combined events. After adjustment for age and clinical variables, AXT METs [P < 0.05; hazard ratio (HR) = 0.59; confidence interval (CI) = 0.35-0.84] and abnormal MPI (P < 0.01; HR = 2.48; CI = 2.15-2.81) were independently predictive of mortality. A positive AXT ECG (P < 0.05; HR = 2.61; CI = 2.13-3.10) was predictive of MI. Death and MI combined were prognosticated by METs (P < 0.05; HR = 0.63; CI = 0.41-0.85), MPI (P < 0.05; HR = 1.77; CI = 1.49-2.05), and a positive AXT ECG (P < 0.05; HR = 1.86; CI = 1.55-2.17). In conclusion, for high risk older patients who cannot perform leg exercise because of lower extremity disabilities, AXT METs are as important as MPI for prediction of mortality alone and death and MI combined, and a positive AXT ECG prognosticates MI alone and death and MI combined.     

Evaluation of two Toxoplasma gondii serologic tests used in a serosurvey of domestic cats in California

We evaluated the sensitivity (Se) and specificity (Sp) of an IgG enzyme-linked immunosorbent assay (ELISA) and IgG indirect fluorescent antibody test (IFAT) for detection of Toxoplasma gondii-specific antibodies in sera from 2 cat populations using a Bayesian approach. Accounting for test covariance, the Se and Sp of the IgG ELISA were estimated to be 92.6% and 96.5%, and those of the IgG IFAT were 81.0% and 93.8%, respectively. Both tests performed poorly in cats experimentally coinfected with feline immunodeficiency virus and T. gondii. Excluding this group, Se and Sp of the ELISA were virtually unchanged (92.3% and 96.4%, respectively), whereas the IFAT Se improved to 94.2% and Sp remained stable at 93.7%. These tests and an IgM ELISA were applied to 123 cat sera from the Morro Bay area, California, where high morbidity and mortality attributable to toxoplasmosis have been detected in southern sea otters. Age-adjusted IgG seroprevalence in this population was estimated to be 29.6%, and it did not differ between owned and unowned cats. Accounting for Se, Sp, and test covariances, age-adjusted seroprevalence was 45.0%. The odds for T. gondii seropositivity were 12.3-fold higher for cats aged >12 mo compared with cats aged <6 mo.     

Visualization of cardiac health using vector cardiogram

An electrocardiogram (ECG) is an electrical recording of the heart and is used in the investigation of heart disease. It displays an apparent periodicity (of about 60–100 bpm in a healthy adult), but is not exactly periodic. The symptoms of disease may show up only during certain periods of the day, and that too may occur at random in the time scale. Visual media is a most effective tool for communication, especially when the data has subtle variations. A novel visualization technique is presented to display each ECG beat. The features like PR interval, QRS width, ST interval, are extracted from the magnitude and phase plot of different lead combinations. These features are displayed on a Cartesian quadrant as different curves, with a menu driven display strategy to visualize the ECG for a chosen interval. The scheme employed can be used to identify different types of abnormalities.