Wavelet based R-peak detection for heart rate variability studies.

Detection of QRS complex in electrocardiogram (ECG) signals is of immense importance in cardiac health prognosis. In this paper a new symmetric wavelet for detection of R-peak is presented, which has been designed based on spectral characteristics and morphology of QRS complex. The detection of R-peak was carried out using this designed wavelet, and with existing symmetric wavelets such as db3, db6, haar and bior2.2. The detection accuracy with this wavelet is 99.99%, which is higher than those with existing symmetric wavelets. The algorithm has been tested on standard databases such as Fantasia database of normal and healthy subjects, MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital) arrhythmia database, and on self-recorded electrocardiograms of normal subjects and patients under diseased stress. The study of heart rate variability (HRV) through computation of RR-tachogram using the new wavelet has proved to be effective in reliably evaluating HRV parameters.

Modified SPIHT wavelet compression for ECG signal.

This paper presents a modified version of Set Partitioning In Hierarchical Trees (SPIHT) wavelet compression method, which has been developed for ECG signal compression. Two more steps in the existing technique have been added to achieve higher compression ratio (CR) and lower percentage rms difference (PRD). The method has been tested on selected records from the MIT-BIH arrhythmia database. Even with two more steps, the method retains its simplicity, computational efficiency and self-adaptiveness, without compromising on any other performance parameter.

Spectral evaluation of aging effects on blood pressure and heart rate variations in healthy subjects.

The background to heart rate variability (HRV) and blood pressure variability (BPV), and their determinants and physiological correlates, remain obscure. The impact of age must be taken into account if HRV and BPV are used for predictive purposes in clinical settings. Healthy subjects show wide inter-individual variation in their heart rate behaviour and the factors affecting heart rate dynamics are not well known. This paper has undertaken to evaluate heart rate variability (HRV) and baroreflex sensitivity (BRS) in a random sample of subjects without evidence of heart disease, and to estimate the relation of HRV and BPV behaviour to age. The aim of this study was to analyse the effects of ageing on HRV and BPV for simultaneous recordings of electrocardiograph (ECG) and blood pressure (BP) signals at rest in healthy subjects. We studied eight young (21-34 years old) and eight elderly (68-85 years old) rigorously screened subjects from the Fantasia Database to make the reproducibility and comparability of the results more extensive. Time- and frequency-domain analysis of HRV and BPV was performed on 5-minute ectopic-free recordings. BRS on the heart was estimated by frequency-domain analysis of spontaneous variability of systolic blood pressure (SBP) and RR interval. It has been observed that compared to young the elderly subjects have (i) diminished HRV; (ii) a shift in the power spectral density and median frequency to low frequency side for HRV and to higher frequency side for BPV; and (iii) increased low-frequency alpha index and decreased high-frequency alpha index of BRS with overall alpha index augmented. The results convey that normal ageing in the absence of disease is associated with lesser parasympathetic regulation of heart rate. Thus it is concluded that the age is an important factor to be considered for prognosis and diagnosis by HRV and BPV. For reliable clinical applications, more research needs to be done on a broad spectrum of subjects. In addition, these observations will prove to be useful for dynamic modelling of cardiovascular regulation for testing the authentication of new techniques for analysis purposes.

Wavelet based compression of medical ultrasound images using vector quantization.

In this paper, an efficient technique for compression of medical ultrasound (US) images is proposed. The technique is based on wavelet transform of the original image combined with vector quantization (VQ) of high-energy subbands using the LBG algorithm. First, we analyse the statistical behaviour of wavelet coefficients in US images across various subbands and scales. The analysis show that most of the image energy is concentrated in one of the detail subband, either in the vertical detail subband (most of the time) or in the horizontal subband. The other two subbands at each decomposition level contribute negligibly to the total image energy. Then, by exploiting this statistical analysis, a low-complexity image coder is designed, which applies VQ only to the highest energy subband while discarding the other detail subbands at each level of decomposition. The coder is tested on a series of abdominal and uterus greyscale US images. The experimental results indicate that the proposed method clearly outperforms the JPEG2000 (Joint Photographers Expert Group) encoder both qualitatively and quantitatively. For example, without using any entropy coder, the proposed method yields a peak signal to noise ratio gain of 0.2 dB to 1.2 dB over JPEG2000 on medical US images.

Joint thresholding and quantizer selection for compression of medical ultrasound images in the wavelet domain.

This paper introduces a simple and efficient technique for compression of medical ultrasound (US) images in the wavelet domain. The statistics of subband wavelet coefficients are modelled using the generalized Gaussian distribution (GGD). By exploiting these statistics, a uniform scalar quantizer is designed which adapts very well to the changing statistics of the signal across various subbands and scales. To increase the quantization performance, a threshold is chosen adaptively to zero-out the insignificant wavelet coefficients in the detail subbands before quantization. A distinctive feature of the proposed technique is that it unifies the two approaches to image adaptive coding: rate-distortion (R-D) optimized quantizer selection and R-D optimal thresholding, in order to increase the compression performance of the coder. The operational R-D criterion used for joint optimization is derived in the minimum description length (MDL) framework. The experimental results show that the joint R-D optimization leads to significant improvement in the compression performance of the proposed coder, named JTQ-WV, over the best state-of-the-art image coder, SPIHT. For example, the coding of US images at 0.25 bpp by JTQ-WV yields a PSNR gain of 1.0 dB over the benchmark SPIHT.

Performance improvement of the SPIHT coder based on statistics of medical ultrasound images in the wavelet domain.

This paper proposes some modifications to the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) image coder based on statistical analysis of the wavelet coefficients across various subbands and scales, in a medical ultrasound (US) image. The original SPIHT algorithm codes all the subbands with same precision irrespective of their significance, whereas the modified algorithm processes significant subbands with more precision and ignores the least significant subbands. The statistical analysis shows that most of the image energy in ultrasound images lies in the coefficients of vertical detail subbands while diagonal subbands contribute negligibly towards total image energy. Based on these statistical observations, this work presents a new modified SPIHT algorithm, which codes the vertical subbands with more precision while neglecting the diagonal subbands. This modification speeds up the coding/decoding process as well as improving the quality of the reconstructed medical image at low bit rates. The experimental results show that the proposed method outperforms the original SPIHT on average by 1.4 dB at the matching bit rates when tested on a series of medical ultrasound images. Further, the proposed algorithm needs 33% less memory as compared to the original SPIHT algorithm.

Homomorphic wavelet thresholding technique for denoising medical ultrasound images.

A novel homomorphic wavelet thresholding technique for reducing speckle noise in medical ultrasound images is presented. First, we show that the speckle wavelet coefficients in the logarithmically transformed ultrasound images are best described by the Nakagami family of distributions. By exploiting this speckle model and the Laplacian signal prior, a closed form, data-driven, and spatially adaptive threshold is derived in the Bayesian framework. The spatial adaptivity allows the additional information of the image (such as identification of homogeneous or heterogeneous regions) to be incorporated into the algorithm. Further, the threshold has been extended to the redundant wavelet representation, which yields better results than the decimated wavelet transform. Experimental results demonstrate the improved performance of the proposed method over other well-known speckle reduction filters. The application of the proposed method to a realistic US test image shows that the new technique, named HomoGenThresh, outperforms the best wavelet-based denoising method reported in [1] by more than 1.6 dB, Lee filter by 3.6 dB, Kaun filter by 3.1 dB and band-adaptive soft thresholding [2] by 2.1 dB at an input signal-to-noise ratio (SNR) of 13.6 dB.

Robust non-homomorphic approach for speckle reduction in medical ultrasound images.

Most existing wavelet-based image denoising techniques are developed for additive white Gaussian noise. In applications to speckle reduction in medical ultrasound (US) images, the traditional approach is first to perform the logarithmic transform (homomorphic processing) to convert the multiplicative speckle noise model to an additive one, and then the wavelet filtering is performed on the log-transformed image, followed by an exponential operation. However, this non-linear operation leads to biased estimation of the signal and increases the computational complexity of the filtering method. To overcome these drawbacks, an efficient, non-homomorphic technique for speckle reduction in medical US images is proposed. The method relies on the true characterisation of the marginal statistics of the signal and speckle wavelet coefficients. The speckle component was modelled using the generalised Nakagami distribution, which is versatile enough to model the speckle statistics under various scattering conditions of interest in medical US images. By combining this speckle model with the generalised Gaussian signal first, the Bayesian shrinkage functions were derived using the maximum a posteriori (MAP) criterion. The resulting Bayesian processor used the local image statistics to achieve soft-adaptation from homogeneous to highly heterogeneous areas. Finally, the results showed that the proposed method, named GNDShrink, yielded a signal-to-noise ratio (SNR) gain of 0.42dB over the best state-of-the-art despeckling method reported in the literature, 1.73dB over the Lee filter and 1.31dB over the Kaun filter at an input SNR of 12.0dB, when tested on a US image. Further, the visual comparison of despeckled US images indicated that the new method suppressed the speckle noise well, while preserving the texture and organ surfaces.

An improved windowing technique for heart rate variability power spectrum estimation.

Spectral analysis of heart rate variability (HRV) is an accepted method for assessment of cardiac autonomic function and its relationship to numerous disorders and diseases. Discrete Fourier transform (DFT) based methods are widely used for their easy applicability, computational speed and the possibility for direct interpretation of results. This study assesses the limitation of windowing of the RR interval series of power spectrum estimation using DFT for heart rate variability studies. The mean value of the RR interval series should be subtracted before windowing. This may leave a small residual DC component after windowing, but the RR interval series is properly tapered to zero at the beginning and end of the window. However, if the windowed RR interval series has a non-zero mean then subtracting this mean will create an abrupt transition between the first and last data points, and the padded zeros. This is equivalent to superimposing upon the RR interval series a rectangular pulse of the same length as the window, with a height equal to the subtracted mean value. In the present paper an approach to overcome the above effects of the window in reducing the signal energy and introducing the low frequency component into spectrum has been suggested and incorporated. Result have been compared for DC biasing of windowed data spectrum, bias of windowed data removed by substraction of mean data, and data processed to remove windowed mean level and to maintain mean power. Thus the preprocessing of RR interval series with this method improves the accuracy of HRV analysis methods. The study was carried out by smoothing the complete RR interval series by single Hann window and by 50% overlapping the data segments of 256 data points followed by the DFT. Overlapping the data segments provides equal weight to all values in the RR interval series and smoothed spectral estimate with clearly dominant peaks in low- and high-frequency regions.

Space-frequency quantiser design for ultrasound image compression based on minimum description length criterion.

The paper addresses the problem of how the spatial quantisation mode and subband adaptive uniform scalar quantiser can be jointly optimised in the minimum description length (MDL) framework for compression of ultrasound images. It has been shown that the statistics of wavelet coefficients in the medical ultrasound (US) image can be better approximated by the generalised Student t-distribution. By combining these statistics with the operational rate-distortion (RD) criterion, a space-frequency quantiser (SFQ) called the MDL-SFQ was designed, which used an efficient zero-tree quantisation technique for zeroing out the tree-structured sets of wavelet coefficients and an adaptive scalar quantiser to quantise the non-zero coefficients. The algorithm used the statistical 'variance of quantisation error' to achieve the different bit-rates ranging from near-lossless to lossy compression. Experimental results showed that the proposed coder outperformed the set partitioning in hierarchical trees (SPIHT) image coder both quantitatively and qualitatively. It yielded an improved compression performance of 1.01 dB over the best zero-tree based coder SPIHIT at 0.25 bits per pixel when averaged over five ultrasound images.

Sampling frequency of the RR interval time series for spectral analysis of heart rate variability.

Spectral analysis of heart rate variability (HRV) is an accepted method for assessment of cardiac autonomic function and its relationship to numerous disorders and diseases. Various non-parametric methods for HRV estimation have been developed and extensive literature on their respective properties is available. The RR interval time series can be seen as a series of non-uniformly spaced samples. To analyse the power spectra of this series using the discrete Fourier transform (DFT), we need to interpolate the series for obtaining uniformly spaced intervals. The selection of sampling period plays a critical role in obtaining the power spectra in terms of computational efficiency and accuracy. In this paper, we shall analyse the RR interval time series from selected subjects for different sampling frequencies to compare the error introduced in selected frequency-domain measures of HRV at a constant frequency resolution for a specific duration of electrocardiogram (ECG) data. It should be pointed out that, although many other error causes are possible in the frequency-domain measures, our attention will be confined only to the performance comparison due to the different sampling frequencies. While the choice of RR interval sampling frequency (f(s)) is arbitrary, the sampling rate of RR interval series must be selected with due consideration to mean and minimum RR interval; f(s = )4 Hz was proposed for a majority of cases. This is an appropriate sampling rate for the study of autonomic regulation, since it enables us to compute reliable spectral estimates between dc and 1 Hz, which represents the frequency band within which the autonomic nervous system has significant response. Furthermore, resampled RR intervals are evenly spaced in time and are synchronized with the samples of the other physiologic signals, enabling cross-spectral estimates with these signals.

Telecardiology for effective healthcare services.

Continuous monitoring of the electrocardiogram (ECG) and other signals related to current heart activity are necessary for patients who are suffering from cardiac diseases. This paper deals with the work that has been carried out to transmit ECGs from remote sites. Software has been developed which enables the uploading of ECG data from a patient so that the physician can monitor the state of the patient from a distance and at the same time may consult other experts for a second opinion. Records can only be examined by the authorized physician after proper registration and diagnosis or prescription may be sent back to the referring site. Further consultation with the patient through a 'chat' facility is also possible. The suitability of the system over transport control protocol (TCP), internet protocol (IP), local area network (LAN), wide area network (WAN) and World Wide Web (WWW) has been assessed. The bandwidth, latency, availability, security and ubiquity have also been discussed. A study has also been undertaken in order to make the system compatible with available bandwidths and to find out which one out of a number of available techniques is most efficient for ECG data compression. The results indicate that the scheme is suitable for telecardiology and can form part of an overall telemedicine system in a health care network.

QRS detection using new wavelets.

This paper deals with a new wavelet (WVT) which has been developed and very effectively and efficiently used for the detection of QRS segments from the ECG signal. After carrying out the detection using five existing wavelets (two symmetric--WT1 and WT2--and three asymmetric--WT3, WT4 and WT5), two new wavelets (WT6 and WT7) were constructed and used for QRS detection. WT6 is a symmetric wavelet and has been constructed by a trial-and-error method. WT7 is an adaptive symmetric wavelet and adjusts its threshold as per the amplitude of the ECG signal. The accuracy of QRS detection obtained from WT6 is 99.8 % and from WT7 100%. The CSE DS-3 database has been used for tests. Both WT6 and WT7 have been proved to be superior in performance to the existing wavelets. Out of WT6 and WT7, WT7 holds high promise for error-free reliable QRS detection in computer-aided feature extraction and disease diagnostics.

Calf circumference as a predictor of low birth weight babies.

PIP: To identify an inexpensive, reliable predictor of low birth weight capable of being utilized by untrained personnel, anthropometric measures were performed on 1600 newborns in Kanpur, India. This analysis indicated that, for a birth weight of 2500 g, the critical limit of calf circumference was 10.8 cm or less. Critical limits for crown-heel length, head circumference, chest circumference, midarm circumference, and thigh circumference were equal to or less than 45.2 cm, 32.2 cm, 30.1 cm, 9.0 cm, and 15.4 cm. All these measurements were significantly correlated with birth weight. This correlation was highest for calf circumference (r = 0.98), thigh circumference (r = 0.93), and chest circumference (r = 0.86). The highest sensitivity of detecting low-birth-weight infants was found for calf circumference (98.4%), followed by thigh circumference (91.6%) and head circumference (76.7%); specificity was 98.2% for thigh circumference and 90.0% for calf circumference. Traditional birth attendants can be trained easily to screen out high-risk newborns by measuring calf circumference.^ieng

The structure of certain newly synthesized non-bridged and bridged cyclopentadienyl titanium complexes and their insecticidal activity.

Three bridged and non-bridged cyclopentadienyl titanium complexes, synthesized in our laboratory, were tested for their insecticidal activity against Trogoderma granarium (Everts), the Khapra beetle, and show moderate toxicity towards this product pest. The non-bridged derivatives have been observed to be slightly more active than the bridged ones.