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Biomedical subjects

S M Krishnan

Publications and source records attributed to S M Krishnan.

13 recordsLinked to original sources
PubMed2006

Cardiac health diagnosis using data fusion of cardiovascular and haemodynamic signals.

The electrocardiogram (ECG) is a representative signal containing useful information about the condition of the heart. The shape and size of the P-QRS-T wave, the r-r interval, etc., may help to identify the nature of disease afflicting the heart. However, human observer cannot directly monitor these subtle details and it is difficult to evaluate the cardiac health using ECG alone. Hence, the fusion of ECG, blood pressure, saturated oxygen content and respiratory data for achieving improved clinical diagnosis of patients in cardiac care units. In this study, a computer based analysis and display of the heterogeneous signals for the detection of life threatening states is demonstrated using fuzzy logic based data fusion. And to evaluate the severity of the disease a new parameter, deterioration index is proposed and results are tabulated for various cases.

Electrocardiography↗
PubMed2005

High-sensitivity and specificity of laser-induced autofluorescence spectra for detection of colorectal cancer with an artificial neural network.

An artificial neural network (ANN) has been used in various clinical research for the prediction and classification of data in cancer disease. Previous research in this direction focused on the correlation between various input parameters such as age, antigen, and size of tumor growth. Recently, laser-induced autofluorescence (LIAF) techniques have been shown to be a useful noninvasive early diagnostic tool for various cancer diseases. We report on a successful application of ANN to in vitro LIAF spectra. We show that classification of tumor samples with ANN can be done with high sensitivity, specificity, and accuracy. Thus a combination of LIAF techniques and ANN can provide a robust method for clinical diagnosis.

Algorithms↗
PubMed2005

A fusion-based clinical decision support for disease diagnosis from endoscopic images.

This paper presents an intelligent decision support system designed on a decision fusion framework coupled with a priori knowledge base for abnormality detection from endoscopic images. Sub-decisions are made based on associated component feature sets derived from the endoscopic images and predefined algorithms, and subsequently fused to classify the patient state. Bayesian probability computations are employed to evaluate the accuracies of sub-decisions, which are utilized in estimating the probability of the fused decision. The overall detectability of abnormalities by using the proposed fusion approach is improved in terms of detection of true positive and true negative conditions when compared with corresponding results from individual methods.

Algorithms↗
PubMed2004

Analysis of cardiac signals using spatial filling index and time-frequency domain.

BACKGROUND: Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. METHODS: This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain. RESULTS: This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution. CONCLUSION: Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%.

Electrocardiography↗
PubMed2004

Classification of cardiac abnormalities using heart rate signals.

The heart rate is a non-stationary signal, and its variation can contain indicators of current disease or warnings about impending cardiac diseases. The indicators can be present at all times or can occur at random, during certain intervals of the day. However, to study and pinpoint abnormalities in large quantities of data collected over several hours is strenuous and time consuming. Hence, heart rate variation measurement (instantaneous heart rate against time) has become a popular, non-invasive tool for assessing the autonomic nervous system. Computer-based analytical tools for the in-depth study and classification of data over day-long intervals can be very useful in diagnostics. The paper deals with the classification of cardiac rhythms using an artificial neural network and fuzzy relationships. The results indicate a high level of efficacy of the tools used, with an accuracy level of 80-85%.

Arrhythmias, Cardiac↗
PubMed2004

Comprehensive analysis of cardiac health using heart rate signals.

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc may contain useful information about the nature of disease affecting the heart. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the heart rate variability signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. Analysis of heart rate variability (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. The HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially nonstationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. This paper deals with the analysis of eight types of cardiac abnormalities and presents the ranges of linear and nonlinear parameters calculated for them with a confidence level of more than 90%.

Autonomic Nervous System↗
PubMed2003

A dynamic nonlinear time domain model for reconstruction and compression of cardiovascular signals with application to telemedicine.

A new nonlinear time domain model is proposed in this paper for signals of cardiovascular origin. An equation of the dynamic nonlinear model has been obtained by considering a masking function, which is modulated by a harmonic series with the baseline drift incorporated into the model. Signal reconstruction using model parameters has established the effectiveness of the model for signal compression. Improvement has been effected by using neural networks for reducing the time for optimizing the initial parameters. An improved adaptive optimization step size algorithm has also been implemented. Results show that the technique is able to provide reasonable compression with low error between the original and reconstructed signals. One of the main advantages of the model is its potential of being used for compression of many different types of biosignals transmitted in parallel. Incorporation of the compression model into a telemedicine system has led to considerable saving in transmission time for patient data.

Algorithms↗
PubMed2000

Extraction of microcalcifications in digital mammograms using regional watershed.

In this report, a novel technique is proposed for computer-aided automatic extraction of microcalcifications in a digital mammogram. First, the microcalcifications are detected by morphological filtering, followed by entropy-based thresholding. Next, the microcalcifications are segmented by computing regional watershed. The proposed automatic technique is designed to serve as a visual aid to radiologists. Its efficacy is demonstrated through experimental results.

Breast Neoplasms↗
PubMed2000

Color image segmentation based on fuzzy rule-based reasoning applied to colonoscopic images.

A fuzzy color segmentation approach is developed for the analysis of colonoscopic images. The segmentation is made up of two phases: segmentation through histogram space filtering and region merging using fuzzy rule-base reasoning. The first phase involves using a scale-space filter to analyze the hue, saturation, and intensity (HSI) histograms to determine the number of classes and construct a 3-D class grid. The color image is then segmented based on the class grid. In the second phase, region merging based on applying the fuzzy rule-base is employed to guide the combining process of the segmented regions. For fuzzy reasoning, three criteria are evaluated, namely, the edge strength along the boundary, color similarity, and spatial connectivity of adjoining regions. Experimental testing of the proposed method applied on colonoscopic images was conducted, and the results are encouraging.

Colonoscopy↗
PubMed1999

Micromachines in endoscopy.

Conventional endoscopy has reached a plateau in technical development, necessitating the exploration of bold new ideas in order to make further advances. One such idea is a self-navigating, independent, intelligent colonoscopic micro-robot. The design of a vehicle that can negotiate the difficult and hostile terrain of the colon is a complex task. Options include wheeled or tracked vehicles and pneumatically driven devices. The development of navigation and lesion recognition software to drive such a vehicle is also challenging. The various mathematical concepts involved in the development of such software are explored in this article.

Animals↗
PubMed1999

Control of hypertension in postoperative patients.

In this paper, adaptive control systems have been developed for the closed-loop control of mean arterial pressure using vasoactive drugs. An adaptive algorithm based on generalized predictive control law has been presented. A adaptive PI controller has been designed. The recursive least-square identification algorithm is used for on-line parameter estimation. The supervisor is added to provide safety and efficacy of control under the consideration of the physical and physiological constraints. Extensive computer simulation in the presence of unpredictable disturbances shows the system is capable of inducing hypertension.

Algorithms↗
PubMed1993

Future developments in high-technology abdominal surgery: ultrasound, stereo imaging, robotics.

The surgical world is experiencing a revolution brought about by the proliferation of minimally invasive techniques. These developments have had most impact on abdominal surgery and chest surgery, but there are ramifications affecting other fields as well. One feature of this change is the increasing dependence of surgeons on technology. Developments in video imaging, ultrasound and robotics are required to make complex endoscopic procedures surgeon-friendly, just as the minimally invasive approach has made surgery more patient-friendly. In the future, integration of stereo imaging systems, computers, microrobots and robotic manipulators will result in technically sophisticated but ergonomic operating systems that will allow surgeons to perform endoscopically almost any type of surgery that can be done today.

Abdomen↗