AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals.

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is characterized by the heart's beating in an uncoordinated manner. In clinical studies, patients often do not have visible symptoms during AF, and hence it is harder to detect this cardiac ailment. Therefore, automated detection of AF using the electrocardiogram (ECG) signals can reduce the risk of stroke, coronary artery disease, and other cardiovascular complications. In this paper, a novel time-frequency domain deep learning-based approach is proposed to detect AF and classify terminating and non-terminating AF episodes using ECG signals. This approach involves evaluating the time-frequency representation (TFR) of ECG signals using the chirplet transform. The two-dimensional (2D) deep convolutional bidirectional long short-term memory (BLSTM) neural network model is used to detect and classify AF episodes using the time-frequency images of ECG signals. The proposed TFR based 2D deep learning approach is evaluated using the ECG signals from three public databases. Our developed approach has obtained an accuracy, sensitivity, and specificity of 99.18% (Confidence interval (CI) as [98.86, 99.49]), 99.17% (CI as [98.85 99.49]), and 99.18% (CI as [98.86 99.49]), respectively, with 10-fold cross-validation (CV) technique to detect AF automatically. The proposed approach also classified terminating and non-terminating AF episodes with an average accuracy of 75.86%. The average accuracy value obtained using the proposed approach is higher than the short-time Fourier transform (STFT), discrete-time continuous wavelet transform (DT-CWT), and Stockwell transform (ST) based time-frequency analysis methods with deep convolutional BLSTM models to detect AF. The proposed approach has better AF detection performance than the existing deep learning-based techniques using ECG signals from the MIT-BIH database.

Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals.

Emotion is interpreted as a psycho-physiological process, and it is associated with personality, behavior, motivation, and character of a person. The objective of affective computing is to recognize different types of emotions for human-computer interaction (HCI) applications. The spatiotemporal brain electrical activity is measured using multi-channel electroencephalogram (EEG) signals. Automated emotion recognition using multi-channel EEG signals is an exciting research topic in cognitive neuroscience and affective computing. This paper proposes the rhythm-specific multi-channel convolutional neural network (CNN) based approach for automated emotion recognition using multi-channel EEG signals. The delta (δ), theta (θ), alpha (α), beta (ß), and gamma (γ) rhythms of EEG signal for each channel are evaluated using band-pass filters. The EEG rhythms from the selected channels coupled with deep CNN are used for emotion classification tasks such as low-valence (LV) vs. high valence (HV), low-arousal (LA) vs. high-arousal (HA), and low-dominance (LD) vs. high dominance (HD) respectively. The deep CNN architecture considered in the proposed work has eight convolutions, three average pooling, four batch-normalization, three spatial drop-outs, two drop-outs, one global average pooling and, three dense layers. We have validated our developed model using three publicly available databases: DEAP, DREAMER, and DASPS. The results reveal that the proposed multivariate deep CNN approach coupled with ß-rhythm has obtained the accuracy values of 98.91%, 98.45%, and 98.69% for LV vs. HV, LA vs. HA, and LD vs. HD emotion classification strategies, respectively using DEAP database with 10-fold cross-validation (CV) scheme. Similarly, the accuracy values of 98.56%, 98.82%, and 98.99% are obtained for LV vs. HV, LA vs. HA, and LD vs. HD classification schemes, respectively, using deep CNN and θ-rhythm. The proposed multi-channel rhythm-specific deep CNN classification model has obtained the average accuracy value of 57.14% using α-rhythm and trial-specific CV using DASPS database. Moreover, for 8-quadrant based emotion classification strategy, the deep CNN based classifier has obtained an overall accuracy value of 24.37% using γ-rhythms of multi-channel EEG signals. Our developed deep CNN model can be used for real-time automated emotion recognition applications.

Detection of shockable ventricular cardiac arrhythmias from ECG signals using FFREWT filter-bank and deep convolutional neural network.

Among various life-threatening cardiac disorders, ventricular tachycardia (VT) and ventricular fibrillation (VF) are shockable ventricular cardiac arrhythmias (SVCA) which require immediate defibrillation therapy for the survival of patients. Timely and accurate detection of rapid VT or VF episodes using ECG signals is extremely important before initiating external defibrillator (AED) and implantable cardioverter-defibrillator (ICD) therapies. In this paper, a novel approach for the detection of SVCA using ECG signals is proposed. The fixed frequency range empirical wavelet transform (EWT) (FFREWT) filter-bank is introduced for the multiscale analysis of ECG signals. The modes evaluated using FFREWT of ECG signals are used as input to a deep convolutional neural network (CNN) for the detection of SVCA. The architecture of the proposed deep CNN comprises of four convolution, two pooling, and four dense layers. The ECG signals from various public databases are used to evaluate the proposed FFREWT domain deep CNN approach. The results show that the proposed approach has obtained an accuracy of 99.036%, 99.800%, and 81.250% for the classification of shockable vs non-shockable, VF vs Non-VF, and VT vs VF, respectively using 8 s ECG frames with 10-fold cross-validation (CV) strategy. Our proposed approach has obtained an average accuracy value of 97.592% using 8 s ECG frames with subject-specific CV. The hardware implementation of the proposed SVCA detection approach can be done using an Internet of things (IoT) driven patient monitoring system.

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network.

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes.

Automated sleep apnea detection from cardio-pulmonary signal using bivariate fast and adaptive EMD coupled with cross time-frequency analysis.

Sleep apnea is a sleep related pathology in which breathing or respiratory activity of an individual is obstructed, resulting in variations in the cardio-pulmonary (CP) activity. The monitoring of both cardiac (heart rate (HR)) and pulmonary (respiration rate (RR)) activities are important for the automated detection of this ailment. In this paper, we propose a novel automated approach for sleep apnea detection using the bivariate CP signal. The bivariate CP signal is formulated using both HR and RR signals extracted from the electrocardiogram (ECG) signal. The approach consists of three stages. First, the bivariate CP signal is decomposed into intrinsic mode functions (IMFs) and residuals for both HR and RR channels using bivariate fast and adaptive empirical mode decomposition (FAEMD). Second, the features are extracted using time-domain analysis, spectral analysis, and time-frequency domain analysis of IMFs from CP signal. The time-frequency domain features are computed from the cross time-frequency matrices of IMFs of CP signal. The cross time-frequency matrix of each IMF is evaluated using the Stockwell (S)-transform. Third, the support vector machine (SVM) and the random forest (RF) classifiers are used for automated detection of sleep apnea with the features from the bivariate CP signal. Our proposed approach has demonstrated an average sensitivity and specificity of 82.27% and 78.67%, respectively for sleep apnea detection using the 10-fold cross-validation method. The approach has yielded an average sensitivity and specificity of 73.19% and 73.13%, respectively for the subject-specific cross-validation. The performance of the approach was compared with other CPC features used for the detection of sleep apnea.

Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals.

Heart valve diseases (HVDs) are a group of cardiovascular abnormalities, and the causes of HVDs are blood clots, congestive heart failure, stroke, and sudden cardiac death, if not treated timely. Hence, the detection of HVDs at the initial stage is very important in cardiovascular engineering to reduce the mortality rate. In this article, we propose a new approach for the detection of HVDs using phonocardiogram (PCG) signals. The approach uses the Chirplet transform (CT) for the time-frequency (TF) based analysis of the PCG signal. The local energy (LEN) and local entropy (LENT) features are evaluated from the TF matrix of the PCG signal. The multiclass composite classifier formulated based on the sparse representation of the test PCG instance for each class and the distances from the nearest neighbor PCG instances are used for the classification of HVDs such as mitral regurgitation (MR), mitral stenosis (MS), aortic stenosis (AS), and healthy classes (HC). The experimental results show that the proposed approach has sensitivity values of 99.44%, 98.66%, and 96.22% respectively for AS, MS and MR classes. The classification results of the proposed CT based features are compared with existing approaches for the automated classification of HVDs. The proposed approach has obtained the highest overall accuracy as compared to existing methods using the same database. The approach can be considered for the automated detection of HVDs with the Internet of Medical Things (IOMT) applications.

Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings.

The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs.

Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme.

BACKGROUND AND OBJECTIVE: The congestive heart failure (CHF) is a life-threatening cardiac disease which arises when the pumping action of the heart is less than that of the normal case. This paper proposes a novel approach to design a classifier-based system for the automated detection of CHF. METHODS: The approach is founded on the use of the Stockwell (S)-transform and frequency division to analyze the time-frequency sub-band matrices stemming from electrocardiogram (ECG) signals. Then, the entropy features are evaluated from the sub-band matrices of ECG. A hybrid classification scheme is adopted taking the sparse representation classifier and the average of the distances from the nearest neighbors into account for the detection of CHF. The proposition is validated using ECG signals from CHF subjects and normal sinus rhythm from public databases. RESULTS: The results reveal that the proposed system is successful for the detection of CHF with an accuracy, a sensitivity and a specificity values of 98.78%, 98.48%, and 99.09%, respectively. A comparison with the existing approaches for the detection of CHF is accomplished. CONCLUSIONS: The time-frequency entropy features of the ECG signal in the frequency range from 11 Hz to 30 Hz have higher performance for the detection of CHF using a hybrid classifier. The approach can be used for the automated detection of CHF in tele-healthcare monitoring systems.

Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals.

Sleep is a prominent physiological activity in our daily life. Sleep apnea is the category of sleep disorder during which the breathing of the person diminishes causing the alternation in the upper airway resistance. The electrocardiogram derived respiration (EDR) and heart rate (RR-time-series) signals are normally used for the detection of sleep apnea as these two signals capture cardio-pulmonary activity information. Hence, the analysis of these two signals provides vital information about sleep apnea. In this paper, we propose the novel sparse residual entropy (SRE) features for the automated detection of sleep apnea using EDR and heart rate signals. The features required for the automated detection of sleep apnea are extracted in three steps: (i) atomic decomposition based residual estimation from both EDR and heart rate signals using orthogonal matching pursuit (OMP) with different dictionaries, (ii) estimation of probabilities from each sparse residual, and (iii) calculation of the entropy features. The proposed SRE features are fed to the combination of fuzzy K-means clustering and support vector machine (SVM) to pick the best performing classifier. The experimental results demonstrate that the proposed SRE features with radial basis function (RBF) kernel-based SVM classifier yielded higher performance with accuracy, sensitivity and specificity values of 78.07%, 78.01%, and 78.13%, respectively with Fourier dictionary and 10-fold cross-validation. For subject-specific or leave-one-out validation case, the SVM classifier has sensitivity and specificity of 85.43% and 92.60%, respectively using SRE features with Fourier dictionary (FD).

Understanding perception of active noise control system through multichannel EEG analysis.

In this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise and ANC with both background noise and music. The multiscale analysis of EEG signal of each channel is performed using the discrete wavelet transform. The multivariate multiscale matrices are formulated based on the sub-band signals of each EEG channel. The singular value decomposition is applied to the multivariate matrices of multichannel EEG at significant scales. The singular value features at significant scales and the extreme learning machine classifier with three different activation functions are used for classification of multichannel EEG signal. The experimental results demonstrate that, for ANC with noise and ANC with noise and music classes, the proposed method has sensitivity values of 75.831% ( p<0.001 ) and 99.31% ( p<0.001 ), respectively. The method has an accuracy value of 83.22% for the classification of EEG signal with music and ANC with music as stimuli. The important finding of this study is that by the introduction of ANC, music can be better perceived by the human brain.

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.

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.

Serum ferritin level correlates with SLEDAI scores and renal involvement in SLE.

INTRODUCTION: Ferritin is an acute-phase reactant that is elevated in various autoimmune disorders. Serum ferritin levels have been positively correlated with disease activity scores of rheumatoid arthritis and systemic lupus erythematosus (SLE). Further, enhanced levels of ferritin have also been reported in lupus nephritis. However, there are no reports from the Indian subcontinent. METHODS: Seventy-six female SLE patients, diagnosed on the basis of revised ACR criteria, and 50 healthy females, age matched from similar geographical areas, were enrolled in the present study. Serum levels of ferritin, IFN-α and IL-6 were quantified by enzyme-linked immunosorbent assay (ELISA). Clinical, biochemical, serological and other markers of disease activity (C3, C4 and anti-dsDNA) were measured by standard laboratory procedure. RESULTS: Serum ferritin levels were significantly higher in SLE patients compared to healthy controls (p < 0.0001). Ferritin levels positively correlated with SLE Disease Activity Index (SLEDAI) (p = 0.001, r = 0.35), anti-dsDNA (p = 0.001, r = 0.35), IFN-α (p < 0.0001, r = 0.51) and IL-6 (p < 0.0001, r = 0.65) and negatively correlated with C3 (p = 0.0006, r = -0.38) and C4 (p = 0.01, r = -0.28). Interestingly, serum levels of ferritin were positively associated with proteinuria (p = 0.001, r = 0.36), serum urea (p = 0.0004, r = 0.39) and serum creatinine (p = 0.0006, r = 0.38). CONCLUSION: Serum ferritin is an excellent marker of disease activity and renal dysfunction in SLE.

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.

Post-exposure prophylaxis for rabies with ERIG and IDRV in children.

To assess the clinical safety of equine rabies immunoglobulin (ERIG) and purified vero cell rabies vaccine (PVRV) administered intradermally in children for post-exposure prophylaxis against rabies, a study was carried out among 1494 children < 15 years of age having category III exposure to animal bite at the antirabies clinic of community medicine department of MKCG Medical College Hospital, Berhampur, Orissa from 1st May 2007 to 31st March 2008. The patients received 0.1 ml of PVRV intradermally at two sites on days 0, 3, 7 and 28. The PVRV (Abhayrab) supplied by Government of Orissa had an antigen content of > or = 2.5 IU per 0.5 ml vial. ERIG (Equirab) was also given on day 0 as per WHO guideline. As much of the immunoglobulin as possible was infiltrated around the wounds after skin test. Side effects were monitored during the follow up visits on days 3, 7 and 28. One hundred & eight children (7.2%) showed positive reaction to the skin test dose of ERIG. These patients could not afford HRIG and were administered ERIG after premedication with oral antihistamine (Levocetrizine). There were no serious systemic side-effects but local side-effects like induration, erythema, pruritus are due to the intradermal rabies vaccination (IDRV) and pain, induration due to ERIG. Low grade fever and malaise were the only systemic side effects observed. None of the children had anaphylaxis or regional lymphadenopathy. Only 3% of children had mild serum sickness like symptoms by days 5 & 7 which subsided with oral analgesics and antihistamines. Our study showed that administration of ERIG & PVRV by intradermal route in children with WHO category-III rabies exposure is safe.

Pentoxifylline adjunct improves prognosis of human cerebral malaria in adults.

Fifty-two adult patients with cerebral malaria were randomly categorized into two groups to receive either quinine dihydrochloride (Qn) alone or a combination of Qn and pentoxifylline (Px). Thirty-two of them received intravenous (i.v.) Qn (group I), and 20 patients (group II) received i.v. Qn along with parenteral Px support (10 mg/kg/day) for the initial 3 days. There was significant improvement in coma resolution time in group II (21.6 +/- 13.9 h) in comparison with group I (63.5 +/- 19.7 h) (P < 0.001), and mortality was 25% of patients in group I against 10% patients receiving Px adjunct (P > 0.05). Three days post-therapy, serum tumour necrosis factor-alpha (TNF-alpha) levels decreased significantly in patients on Px support (day 0 TNF = 415.62 +/- 477.80 pg/ml; day 3 TNF = 47.92 +/- 27.9 pg/ml; P = 0.0029). There was no significant change in TNF levels in those on quinine alone (day 0 TNF = 477.08 +/- 933.90 pg/ml; day 3 TNF = 589 +/- 602.3 pg/ml; P > 0.05). There were no serious side-effects necessitating withdrawal of patients receiving Px therapy.

Plasma glucose and tumour necrosis factor-alpha in adult patients with severe falciparum malaria.

Plasma glucose was assessed in 81 patients with severe falciparum malaria at the time of presentation along with tumour necrosis factor-alpha (TNF-alpha). The lowest plasma glucose value was 3.38 mmol/l and none of the patients had hypoglycaemia at admission. Plasma glucose values were not significantly lower in those with multiple organ dysfunction (MOD) than in patients with single organ dysfunction (cerebral malaria only) and in those who died compared with patients who survived. Conversely, TNF-alpha showed a good correlation with depth of coma and was significantly higher in patients who had MOD and those who died. There was no correlation between plasma glucose and TNF-alpha values.

Growth in the first year in children following IAP Policy on Infant Feeding.

OBJECTIVE: To study the growth pattern in the first year in children fed according to recommendations of IAP Policy on Infant Feeding. DESIGN: Longitudinal. SETTING: Department of Pediatrics, S.C.B. Medical College Hospital, Cuttack, Orissa. SUBJECTS AND METHODS: 114 infants (68 boys and 46 girls) with birth weight greater than or equal to 2500g from upper and middle S-E status were regularly followed up from birth to 12 mo of age and fed according to recommendations of IAP Policy on Infant Feeding. Mean and standard deviations of weight for age (W/A) and length for age (L/A) and mean Z scores for W/A, L/A and W/L (weight for length) were calculated separately for boys and girls with reference to NCHS-WHO and BFDS data. OBSERVATIONS: Mean Z scores for W/A with reference to NCHS-WHO data showed a positive trend from birth upto the age of 3 to 4 months, subsequently declining upto one year. The Z scores for L/A showed only a minimal downward trend. The W/L Z score remained above the baseline value up to 3 months in boys and 7 months in girls. When BFDS was taken as the reference, W/A Z scores showed consistent positive increments, from birth in girls and 1 mo in boys. L/A Z scores increased from 3 months in boys and 11 months in girls. Using NCHS data as the reference, the percentage of infants below -2SD for weight was 0 to 7% during first 6 months and 14% at 12 months. Ten% were below -2SD for length at 12 months. With BFDS as the reference, the percentage of infants below -2SD for weight was 25% at birth, 5% at 6 months and 12% at 1 yr. For length, it was 12% at birth and 8% at 1 year. The increments in weight and length closely followed BFDS upto 12 mo age. CONCLUSION: The IAP Policy on Infant Feeding results in adequate growth of non low birth weight infants in the first year of life.