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%.

Automatic Detection of Aortic Valve Opening Using Seismocardiography in Healthy Individuals.

Accurate detection of fiducial points in a seismocardiogram (SCG) is a challenging research problem for its clinical application. In this paper, an automated method for detecting aortic valve opening (AO) instants using the dorso-ventral component of the SCG signal is proposed. This method does not require electrocardiogram (ECG) as a reference signal. After preprocessing the SCG, multiscale wavelet decomposition is carried out to get signal components in different wavelet subbands. The subbands having possible AO peaks are selected by a newly proposed dominant-multiscale-kurtosis- and dominant-multiscale-central-frequency-based criterion. The signal is reconstructed using selected subbands, and it is emphasized using the weights derived from the proposed relative squared dominant multiscale kurtosis. The Shannon energy followed by autocorrelation coefficients is computed for systole envelope construction. Finally, AO peaks are detected by a Gaussian-derivative-filtering-based scheme. The robustness of the proposed method is tested using clean and noisy SCG signals from the combined measurement of ECG, breathing, and SCG database. Evaluation results show that the method can achieve an average sensitivity of 94%, a prediction rate of 90%, and a detection accuracy of 86% approximately over 4585 analyzed beats.

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.

Information theoretic multiscale truncated SVD for multilead electrocardiogram.

BACKGROUND AND OBJECTIVE: In this paper an information theory based multiscale singular value decomposition (SVD) is proposed for multilead electrocardiogram (ECG) signal processing. The shrinkage of singular values for different multivariate multiscale matrices at wavelet scales is based on information content. It aims to capture and preserve the information of clinically important local waves like P-waves, Q-waves, T-waves and QRS-complexes. METHODS: The information is derived through clinically relevant multivariate multiscale entropy in SVD domain modifying Shannon's entropy. This optimizes the approximate ranks for matrices to capture the clinical components of ECG signals appearing at different scales. A newly introduced multivariate clinical distortion (MCD) metric is computed and compared with existing subjective and objective signal distortion measures. The proposed method is tested with records from CSE multilead measurement library and PTB diagnostic ECG database for various pathological cases. RESULTS: It gives average percentage root mean square difference (PRD), average normalized root mean square error (NRMSE), average wavelet energy based diagnostic distortion measure (WEDD) values 5.8879%, 0.0059 and 1.0760% respectively for myocarditis pathology. The corresponding MCD value is 1.9429%. The highest average PRD and average WEDD values are 11.4053% and 5.5194% for cardiomyopathy with the corresponding MCD value 1.4003%. CONCLUSIONS: Based on WEDD values and mean opinion scores (MOS), the quality group of all processed signals fall under excellent category.

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.

Neuralgic amyotrophy associated with dengue fever: case series of three patients.

Dengue is an acute mosquito borne viral infection caused by one of the four distinct serotype of dengue viruses (type 1-4), belonging to flavivirus family. Dengue fever, an arboviral infection is known to cause various neurological complications. Commonly reported neurological manifestations associated with dengue infection are encephalopathy, myelopathy, stroke, Guillain-Barre syndrome and hypokalemic paralysis. Brachial amyotrophy associated with dengue infection were not described previously. Here, we describe three patients presenting with brachial neuritis associated with dengue infection. Dengue infection should be considered in the etiological list of brachial neuritis in dengue endemic areas, especially if preceded by history of febrile illness compatible with dengue illness.

Analysis, assessment and mapping of groundwater quality of Chandigarh (India).

Chandigarh (India) has been depending on groundwater resources to meet its water requirements in addition to the surface water source (Bhakra Main Canal). With a view to assess the groundwater quality, samples were collected from geo-referenced tube wells in different localities of the city. Samples were analysed for conventional parameters indicative of the physico-chemical quality of groundwater. The groundwater quality mapping was attempted using the ARCGIS 9.0. Thematic maps were generated for each parameter of groundwater quality. This paper presents the spatial distribution of groundwater quality of Chandigarh city. The quality of groundwater was found to be varying with geology of the area as well as the land use and land cover.

Catatonia incidence in acute psychiatric admissions.

Eighty six consecutively admitted unmedicated patients, with a current duration of illness of less than two years, who fulfilled ICD-10 criteria for mood disorder or schizophrenia were assessed for catatonic signs over a three week study period Thirty two of them could be rated as catatonic, most of them starting to exhibit the signs at the time of admission or a few days thereafter. While the percentage of manic patients showing catatonic signs was comparable to earlier studies, a significant proportion of patients belonging to the Schizophrenic and Acute and Transient Psychotic Disorder group also exhibited these signs. The reasons for obtaining such a high percentage of catatonias are discussed. It is contended that short lasting catatonic signs are a common feature of acute psychiatric admissions and are ignored when viewed within the framework of an affective or psychotic illness.

Tardive dyskinesia and type II schizophrenia.

Cognitive impairment, negative and positive symptoms, primitive release reflexes, and age/temporal disorientation were assessed in 20 male patients meeting the DSM-III-R criteria for chronic schizophrenia and Schooler & Kane's criteria for TD. The control group comprised 20 age-matched male chronic schizophrenic patients without TD. Significant associations were found between TD, cognitive impairment, some negative symptoms, and formal thought disorder. These associations were independent of other illness and treatment variables. The severity of TD correlated significantly with that of cognitive impairment.

The stability of symptoms and syndromes in chronic schizophrenic patients.

36 chronic schizophrenic patients meeting D.S.M. III - R criteria were assessed by a single rater using the Positive and Negative Syndrome Scale (PANSS). Ratings were repeated 9 months later by the same rater. Negative symptoms and syndromes were much more stable over time than positive symptoms and syndromes. Only hallucinations had stability comparable to the negative symptoms. Positive and negative subtypes of schizophrenia based on the composite score were very stable. Relatively few symptoms from the general psychopathology subscale were stable over time. The implications of these findings are discussed.

Prediction of outcome in schizophrenia using the subjective response to a test dose of a neuroleptic.

Twenty four patients meeting R.D.C. criteria for schizophrenia were assessed using the B.P.R.S. before starting neuroleptics. They were then given a standardized test dose of haloperidol. Their subjective response to the test dose was assessed 4 hours later by a blind rater. The B.P.R.S. ratings were repeated after 3 weeks of neuroleptic treatment. A dysphoric response to the test dose was associated with a poor therapeutic outcome. The implications of these findings are discussed.

Catatonia and hyponatremia : a case report.

We report a case of recurrent mania who developed catatonia and was found to be markedly hyponatraemia. The catatonic symptoms showed rapid resolution following correction of hyponatraemia suggesting a causal link between the two. It is suggested that the estimation of serum electrolyte levels should be an important part of the evaluation of cases of catatonia.

Experiences of psychiatric consultation service in two coalfield hospitals of bihar.

The Central Institute of Psychiatry provides extension consultation services at two coalfield hospitals of Bihar, representing the intermediate and central level of health care delivery system. Analysis of data covering a period of one year, from September 1988 to August 1989, revealed significant differences in the utilization pattern of the two centres. The differences relate to the proportion of population observed, their demographic characteristics, diagnostic break-up, and follow-up pattern. The provision of psychiatric extension services at the intermediate level is recommended.

Buprenorphine abuse : a case report.

Search for a safe and effective analgesic is long and elusive. Buprenorphine was marketed as a potent and non-addictive narcotic drug. In recent years there have been various case reports highlighting its abuse potential. As the drug will be used more widely and particularly with its use in the detoxification of heroin dependence there is an eminent possibility of its abuse at larger scale in future. Physicians should be careful in its prescription and cases of its abuse should be actively sought tor. A case report along with literature review is presented here.

Low dose levodopa in tardive dyskinesia.

Efficacy of low dose levodopa in treatment of TD has been studied on 30 male inpatients with predefined selection criteria. The preliminary results are encouraging but long term double blind studies in larger samples are needed.