Quantifying Intra-Arterial Verapamil Response as a Diagnostic Tool for Reversible Cerebral Vasoconstriction Syndrome.

BACKGROUND AND PURPOSE: There is mounting evidence supporting the benefit of intra-arterial administration of vasodilators in diagnosing reversible cerebral vasoconstriction syndrome. We prospectively quantified the degree of luminal diameter dilation after intra-arterial administration of verapamil and its accuracy in diagnosing reversible cerebral vasoconstriction syndrome. MATERIALS AND METHODS: Patients suspected of having intracranial arteriopathy on noninvasive imaging and referred for digital subtraction angiography were enrolled in a prospective registry. Intra-arterial verapamil was administered in vascular territories with segmental irregularities. The caliber difference (Caliberpost - Caliberpre) and the proportion of caliber change ([(Caliberpost - Caliberpre)/Caliberpre] × 100%) were used to determine the response to verapamil. The diagnosis of reversible cerebral vasoconstriction syndrome was made on the basis of clinical and imaging features at a follow-up appointment, independent of the reversibility of verapamil. Receiver operating characteristic curve analysis was performed to determine the best threshold. RESULTS: Twenty-six patients were included, and 9 (34.6%) were diagnosed with reversible cerebral vasoconstriction syndrome. A total of 213 vascular segments were assessed on diagnostic angiography. Every patient with a final diagnosis of reversible cerebral vasoconstriction syndrome responded to intra-arterial verapamil. The maximal proportion of change (P < .001), mean proportion of change (P = .002), maximal caliber difference (P = .004), and mean caliber difference (P = .001) were statistically different between patients with reversible cerebral vasoconstriction syndrome and other vasculopathies. A maximal proportion of change ≥32% showed a sensitivity of 100% and a specificity of 88.2% to detect reversible cerebral vasoconstriction syndrome (area under the curve = 0.951). The Reversible Cerebral Vasoconstriction Syndrome-2 score of ≥5 points achieved a lower area under the curve (0.908), with a sensitivity of 77.8% and a specificity of 94.1%. CONCLUSIONS: Objective measurement of the change in the arterial calibers after intra-arterial verapamil is accurate in distinguishing reversible cerebral vasoconstriction syndrome from other vasculopathies. A proportion of change ≥32% has the best diagnostic performance.

Enhancement of the heart sound envelope using the logistic function amplitude moderation method.

This paper presents a new method to extract the envelope of the fundamental heart sound (S1 and S2) using the logistic function. The sigmoid characteristic of the logistic function is incorporated to segregate S1, and S2 signal intensities from silent or noise interfered systolic and diastolic intervals in a heart sound cycle. This signal intensity transformation brings uniformity to the envelope peak of S1 and S2 sound by inclining the transform intensity distribution towards the upper asymptote of the sigmoid curve. The proposed logistic function based amplitude moderation (LFAM) envelogram method involves finding the critical upper amplitude (xuc) above which the signals will be categorized as loud sound and the critical lower amplitude (xlc) below which the signal will be considered as noise. These critical values are regressively obtained from the signal itself by histogram analysis of intensity distribution. The performance is evaluated on noisy PCG dataset taken from PhysioNet/Computing in Cardiology Challenge 2016. The LFAM envelope yields better hill-valley discrimination of heart sounds from its silent/noisy signal intervals. The enhance heart sound envelope peaks are better than conventional methods. The proposed envelope feature is evaluated for heart sound segmentation using HSMM. There is a significant improvement in segmentation accuracy, especially at a low signal-to-noise ratio. The best average F1 score is 97.73%.

Objective assessment of cleft lip and palate speech intelligibility using articulation and hypernasality measures.

Assessment of intelligibility is required to characterize the overall speech production capability and to measure the speech outcome of different interventions for individuals with cleft lip and palate (CLP). Researchers have found that articulation error and hypernasality have a significant effect on the degradation of CLP speech intelligibility. Motivated by this finding, the present work proposes an objective measure of sentence-level intelligibility by combining the information of articulation deficits and hypernasality. These two speech disorders represent different aspects of CLP speech. Hence, it is expected that the composite measure based on them may utilize complementary clinical information. The objective scores of articulation and hypernasality are used as features to train a regression model, and the output of the model is considered as the predicted intelligibility score. The Spearman's correlation coefficient based analysis shows a significant correlation between the predicted and perceptual intelligibility scores (ρ = 0.77, p < 0.001).

Detection and assessment of hypernasality in repaired cleft palate speech using vocal tract and residual features.

The presence of hypernasality in repaired cleft palate (CP) speech is a consequence of velopharyngeal insufficiency. The coupling of the nasal tract with the oral tract adds nasal formant and antiformant pairs in the hypernasal speech spectrum. This addition deviates the spectral and linear prediction (LP) residual characteristics of hypernasal speech compared to normal speech. In this work, the vocal tract constriction feature, peak to side-lobe ratio feature, and spectral moment features augmented by low-order cepstral coefficients are used to capture the spectral and residual deviations for hypernasality detection. The first feature captures the lower-frequencies prominence in speech due to the presence of nasal formants, the second feature captures the undesirable signal components in the residual signal due to the nasal antiformants, and the third feature captures the information about formants and antiformants in the spectrum along with the spectral envelope. The combination of three features gives normal versus hypernasal speech detection accuracies of 87.76%, 91.13%, and 93.70% for /a/, /i/, and /u/ vowels, respectively, and hypernasality severity detection accuracies of 80.13% and 81.25% for /i/ and /u/ vowels, respectively. The speech data are collected from 30 control normal and 30 repaired CP children between the ages of 7 and 12.

Importance of glottis landmarks for the assessment of cleft lip and palate speech intelligibility.

The present work explores the acoustic characteristics of articulatory deviations near g(lottis) landmarks to derive the correlates of cleft lip and palate speech intelligibility. The speech region around the g landmark is used to compute two different acoustic features, namely, two-dimensional discrete cosine transform based joint spectro-temporal features, and Mel-frequency cepstral coefficients. Sentence-specific acoustic models are built using these features extracted from the normal speakers' group. The mean log-likelihood score for each test utterance is computed and tested as the acoustic correlates of intelligibility. Derived intelligibility measure shows significant correlation (ρ = 0.78, p < 0.001) with the perceptual ratings.

Intelligibility assessment of cleft lip and palate speech using Gaussian posteriograms based on joint spectro-temporal features.

Intelligibility is considered as one of the primary measures for speech rehabilitation of individuals with a cleft lip and palate (CLP). Currently, speech processing and machine-learning-based objective methods are gaining more research interest as a way to quantify speech intelligibility. In this work, joint spectro-temporal features computed from a time-frequency representation of speech are explored to derive speech representations based on Gaussian posteriograms. A comparative framework using dynamic time warping (DTW) is used to quantify the intelligibility of child CLP speech. The DTW distance is used to score sentence-level intelligibility and tested for correlation with perceptual intelligibility ratings obtained from expert speech-language pathologists. A baseline DTW system using the conventional Mel-frequency cepstral coefficients (MFCCs) is also developed to compare the performance of the proposed system. Spearman's rank correlation coefficient between the objective intelligibility scores and the perceptual intelligibility rating is studied. A Williams significance test is conducted to assess the statistical significance of the correlation difference between the methods. The results show that the system based on joint spectro-temporal features significantly outperforms the MFCC-based system.

Detection of hypernasality based on vowel space area.

This study proposes a method for differentiating hypernasal-speech from normal speech using the vowel space area (VSA). Hypernasality introduces extra formant and anti-formant pairs in vowel spectrum, which results in shifting of formants. This shifting affects the size of the VSA. The results show that VSA is reduced in hypernasal-speech compared to normal speech. The VSA feature plus Mel-frequency cepstral coefficient feature for support vector machine based hypernasality detection leads to an accuracy of 86.89% for sustained vowels and 89.47%, 90.57%, and 91.70% for vowels in contexts of high pressure consonants /k/, /p/, and /t/, respectively.

Spatial enhancement of ECG using diagnostic similarity score based lead selective multi-scale linear model.

In this work, a new patient-specific approach to enhance the spatial resolution of ECG is proposed and evaluated. The proposed model transforms a three-lead ECG into a standard twelve-lead ECG thereby enhancing its spatial resolution. The three leads used for prediction are obtained from the standard twelve-lead ECG. The proposed model takes advantage of the improved inter-lead correlation in wavelet domain. Since the model is patient-specific, it also selects the optimal predictor leads for a given patient using a lead selection algorithm. The lead selection algorithm is based on a new diagnostic similarity score which computes the diagnostic closeness between the original and the spatially enhanced leads. Standard closeness measures are used to assess the performance of the model. The similarity in diagnostic information between the original and the spatially enhanced leads are evaluated using various diagnostic measures. Repeatability and diagnosability are performed to quantify the applicability of the model. A comparison of the proposed model is performed with existing models that transform a subset of standard twelve-lead ECG into the standard twelve-lead ECG. From the analysis of the results, it is evident that the proposed model preserves diagnostic information better compared to other models.

Multiplex Amplification Refractory Mutation System PCR (ARMS-PCR) provides sequencing independent typing of canine parvovirus.

Canine parvovirus-2 antigenic variants (CPV-2a, CPV-2b and CPV-2c) ubiquitously distributed worldwide in canine population causes severe fatal gastroenteritis. Antigenic typing of CPV-2 remains a prime focus of research groups worldwide in understanding the disease epidemiology and virus evolution. The present study was thus envisioned to provide a simple sequencing independent, rapid, robust, specific, user-friendly technique for detecting and typing of presently circulating CPV-2 antigenic variants. ARMS-PCR strategy was employed using specific primers for CPV-2a, CPV-2b and CPV-2c to differentiate these antigenic types. ARMS-PCR was initially optimized with reference positive controls in two steps; where first reaction was used to differentiate CPV-2a from CPV-2b/CPV-2c. The second reaction was carried out with CPV-2c specific primers to confirm the presence of CPV-2c. Initial validation of the ARMS-PCR was carried out with 24 sequenced samples and the results were matched with the sequencing results. ARMS-PCR technique was further used to screen and type 90 suspected clinical samples. Randomly selected 15 suspected clinical samples that were typed with this technique were sequenced. The results of ARMS-PCR and the sequencing matched exactly with each other. The developed technique has a potential to become a sequencing independent method for simultaneous detection and typing of CPV-2 antigenic variants in veterinary disease diagnostic laboratories globally.

Diagnostic measure to quantify loss of clinical components in multi-lead electrocardiogram.

In this Letter, a novel principal component (PC)-based diagnostic measure (PCDM) is proposed to quantify loss of clinical components in the multi-lead electrocardiogram (MECG) signals. The analysis of MECG shows that, the clinical components are captured in few PCs. The proposed diagnostic measure is defined as the sum of weighted percentage root mean square difference (PRD) between the PCs of original and processed MECG signals. The values of the weight depend on the clinical importance of PCs. The PCDM is tested over MECG enhancement and a novel MECG data reduction scheme. The proposed measure is compared with weighted diagnostic distortion, wavelet energy diagnostic distortion and PRD. The qualitative evaluation is performed using Spearman rank-order correlation coefficient (SROCC) and Pearson linear correlation coefficient. The simulation result demonstrates that the PCDM performs better to quantify loss of clinical components in MECG and shows a SROCC value of 0.9686 with subjective measure.

Multi-channel ECG data compression using compressed sensing in eigenspace.

In recent years, compressed sensing (CS) has emerged as a potential alternative to traditional data compression techniques for resource-constrained telemonitoring applications. In the present work, a CS framework of data reduction is proposed for multi-channel electrocardiogram (MECG) signals in eigenspace. The sparsity of dimension-reduced eigenspace MECG signals is exploited to apply CS. First, principal component analysis (PCA) is applied over the MECG data to retain diagnostically important ECG features in a few principal eigenspace signals based on maximum variance. Then, the significant eigenspace signals are randomly projected over a sparse binary sensing matrix to obtain the reduced dimension compressive measurement vectors. The compressed measurements are quantized using a uniform quantizer and encoded by a lossless Huffman encoder. The signal recovery is carried out by an orthogonal matching pursuit (OMP) algorithm. The proposed method is evaluated on the MECG signals from PTB and CSE multilead measurement library databases. The average value of percentage root mean square difference (PRD) across the PTB database is found to be 5.24% at a compression ratio (CR)=17.76 in Lead V3 of PTB database. The visual signal quality of the reconstructed MECG signals is validated through mean opinion score (MOS), found to be 6.66%, which implies very good quality signal reconstruction.

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

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

Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition.

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are shockable ventricular cardiac ailments. Detection of VT/VF is one of the important step in both automated external defibrillator (AED) and implantable cardioverter defibrillator (ICD) therapy. In this paper, we propose a new method for detection and classification of shockable ventricular arrhythmia (VT/VF) and non-shockable ventricular arrhythmia (normal sinus rhythm, ventricular bigeminy, ventricular ectopic beats, and ventricular escape rhythm) episodes from Electrocardiogram (ECG) signal. The variational mode decomposition (VMD) is used to decompose the ECG signal into number of modes or sub-signals. The energy, the renyi entropy and the permutation entropy of first three modes are evaluated and these values are used as diagnostic features. The mutual information based feature scoring is employed to select optimal set of diagnostic features. The performance of the diagnostic features is evaluated using random forest (RF) classifier. Experimental results reveal that, the feature subset derived from mutual information based scoring and the RF classifier produces accuracy, sensitivity and specificity values of 97.23 %, 96.54 %, and 97.97 %, respectively. The proposed method is compared with some of the existing techniques for detection of shockable ventricular arrhythmia episodes from ECG.

Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction.

In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel.

A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification.

A new measure for quantifying diagnostic information from a multilead electrocardiogram (MECG) is proposed. This diagnostic measure is based on principal component (PC) multivariate multiscale sample entropy (PMMSE). The PC analysis is used to reduce the dimension of the MECG data matrix. The multivariate multiscale sample entropy is evaluated over the PC matrix. The PMMSE values along each scale are used as a diagnostic feature vector. The performance of the proposed measure is evaluated using a least square support vector machine classifier for detection and classification of normal (healthy control) and different cardiovascular diseases such as cardiomyopathy, cardiac dysrhythmia, hypertrophy and myocardial infarction. The results show that the cardiac diseases are successfully detected and classified with an average accuracy of 90.34%. Comparison with some of the recently published methods shows improved performance of the proposed measure of cardiac disease classification.

Multichannel ECG data compression based on multiscale principal component analysis.

In this paper, multiscale principal component analysis (MSPCA) is proposed for multichannel electrocardiogram (MECG) data compression. In wavelet domain, principal components analysis (PCA) of multiscale multivariate matrices of multichannel signals helps reduce dimension and remove redundant information present in signals. The selection of principal components (PCs) is based on average fractional energy contribution of eigenvalue in a data matrix. Multichannel compression is implemented using uniform quantizer and entropy coding of PCA coefficients. The compressed signal quality is evaluated quantitatively using percentage root mean square difference (PRD), and wavelet energy-based diagnostic distortion (WEDD) measures. Using dataset from CSE multilead measurement library, multichannel compression ratio of 5.98:1 is found with PRD value 2.09% and the lowest WEDD value of 4.19%. Based on, gold standard subjective quality measure, the lowest mean opinion score error value of 5.56% is found.

Perturbations in immune responses induced by concurrent subchronic exposure to arsenic and endosulfan.

The metalloid arsenic and the chlorinated insecticide endosulfan are common environmental contaminants. Humans, animals, and birds are exposed to these chemicals through water and food. Although health effects due to either arsenic or endosulfan exposure are documented, the toxicological impact of co-exposure to these environmental pollutants is unpredictable and unknown. The present study was undertaken to assess whether concurrent exposure to arsenic and endosulfan induces significant alterations in immunological functions. Day-old chicks were exposed to 3.7 ppm of arsenic via drinking water and to 30 ppm of endosulfan-mixed feed either individually or concurrently for up to 60 days. All the chicks were vaccinated with Ranikhet disease virus (F-strain; RD-F) on days 1 and 30. During the course of study and at term, parameters of cellular and humoral immunity were determined. None of the treatments altered the absolute body weight or body weight gain, except arsenic significantly reduced weight gain on day 60. Absolute, but not the relative, weights of spleen, thymus and bursa of Fabricius were significantly reduced in all the treatment groups. The metalloid and insecticide combination significantly depressed the ability of peripheral blood and splenic lymphocytes to proliferate in response to antigen RD-F and mitogen Con A. The delayed type hypersensitivity response to 2,4-dinitro-1-chlorobenzene or to PHA-P was also significantly decreased. Nitric oxide production by RD-F or lipopolysaccharide-stimulated peripheral blood and splenic mononuclear cells was significantly suppressed following concurrent exposure to arsenic and endosulfan. Furthermore, the combined exposure also decreased the antibody response to RD-F. The suppression of cellular and humoral immune responses was also evident following administration of individual compounds, and it was not exacerbated following concurrent exposure. To our knowledge, this is the first report describing the suppression of immune responses following exposure to arsenic alone or in combination with endosulfan at environmentally realistic concentrations in avian species. Therefore, immunotoxicological effects induced by concurrent exposure to arsenic and chlorinated pesticides should be considered when assessing the risk to human and animal health.

Wavelet transform domain data embedding in a medical image.

In this work, we propose a wavelet-based technique for embedding medical data in a medical image. In the spectral domain, the patient data are embedded into the wavelet coefficients of the host image. A diagnostic distortion measure (DDM) has been defined to measure the visible distortions between the original image and the embedded image. The performance of the DDM has been compared with the standard PSNR characteristics. The results show that DDM not only captures the distortions for different quantities of embedded data but also can quantify the differences when the same data are embedded at different subbands. The embedment of data into the mid and high frequency subbands of the host image show lower values of DDM and higher values of PSNR.

Spike detection in biomedical signals using midprediction filter.

Spikes such as QRS complex in ECG, epileptic seizures in EEG, fine crackles in vesicular sound and glottal closure instants in voiced sound are of diagnostic importance. Various methods of spike detection use the amplitude and frequency characteristics of the spikes. Because of the high frequency content, the spikes appear in the error signal when a linear prediction filtering scheme is used. The authors use the method of midprediction filtering for the detection of the spikes. In this method, the present sample is predicted as a weighted average of p recent past and p immediate future samples. The symmetrical nature of midprediction causes the spikes to appear in the error signal with their original basewidths. This can help in improving the reliability of spike detection, as both the amplitude and the duration of the spike can be considered as decision making parameters. It is observed that the high frequency gain of the midprediction filter is higher compared to the high frequency gain of the LPC or endprediction filter. As a result, this method works better than linear prediction for the detection of spikes.

Anti-idiotype antibodies to Marek's disease-associated tumour surface antigen in protection against Marek's disease.

Marek's disease-associated tumour surface antigen (MATSA) removed by enzymatic (papain) digestion of Marek's disease tumour cells was fractionated by gel filtration chromatography. The first peak (F1) was used to raise antibody in rabbits. Monoclonal antibody (RPH-6) directed against MATSA and the anti-F1 IgG were used as idiotypic antibodies to raise polyclonal anti-idiotype serum in heterologous hosts; rabbit and goat, respectively. The anti-idiotypes (anti-Id) were purified by affinity chromatography and characterized by competitive binding assay using immunofluorescent (IF) tests. Day-old white Leghorn chicks were immunized with anti-Id to MATSA (Group 1) or anti-Id to F1 (Group 3) and challenged with virulent Marek's disease virus (MDV) on the tenth day post immunization. In positive control groups, the day-old chicks were inoculated with anti-BALB/c mouse globulin (Group 2) and anti-rabbit globulin (Group 4) and challenged with virulent MDV on the tenth day post inoculation. As compared with positive control groups, the vaccinated groups (1 and 3) had considerably lower level of MATSA positive cells during the post challenge observation period. The protection level against MD in the immunized groups was 66.6% (Group 1) and 86.6% (Group 3).