Saurabh Pal

QRS Complex detection using Empirical Mode Decomposition based windowing technique

In this work an Empirical Mode Decomposition based QRS complex detection algorithm is proposed. Other decomposition techniques use some predetermined basis function for transformation and hence may not be applicable for all kind of signals. Being a fully data driven adaptive technique, the present method depends on selection of proper and optimum set of IMFs to generate an intermediate signal. Some simple mathematical operations are performed on that signal to highlight the R peak. Then the Q and S points are detected by windowing technique. Only second and third IMFs are required for QRS complex detection. The proposed method is tested with PTB diagnostic database and MIT-BIH Arrhythmia database. The R peak detection success rate is 98.67%. Sensitivity and specificity of QRS complex detection is 98.88% and 99.04% respectively.

Significance of Exhaled Breath Test in Clinical Diagnosis: A Special Focus on the Detection of Diabetes Mellitus

Analysis of volatile organic compounds (VOCs) emanating from human exhaled breath can provide deep insight into the status of various biochemical processes in the human body. VOCs can serve as potential biomarkers of physiological and pathophysiological conditions related to several diseases. Breath VOC analysis, a noninvasive and quick biomonitoring approach, also has potential for the early detection and progress monitoring of several diseases. This paper gives an overview of the major VOCs present in human exhaled breath, possible biochemical pathways of breath VOC generation, diagnostic importance of their analysis, and analytical techniques used in the breath test. Breath analysis relating to diabetes mellitus and its characteristic breath biomarkers is focused on. Finally, some challenges and limitations of the breath test are discussed.

ECG Feature Extraction by Multi Resolution Wavelet Analysis based Selective Coefficient Method

One of the major problems in feature extraction methodologies from ECG is to identify the wave and complex boundaries. Here we present a multiresolution wavelet based approach to identify the boundaries. The wavelet reconstruction coefficients are assessed in terms of shape and size to eliminate the interfering components for better detection of wave boundaries. The relevant coefficients are retained that resembles with the original structure of the wave under investigation. The algorithm suggests different set of reconstruction coefficients for different part of the ECG wave which eliminates the scope of probable interaction between adjacent regions and thus correct identification of wave boundaries are ensured. The QRS complex and T wave duration are measured with the algorithm and validated against some arbitrarily chosen ECG records for lead 1 from Physionet PTB diagnostic database. The measured values are compared with the manually determined values and the accuracy for each evaluation is calculated. The test result shows over 95% accuracy for QRS complex and over 92% accuracy for QT interval and T duration.

Estimation of arrhythmia episode using variational mode decomposition technique

Detection of life threatening arrhythmia like ventricular flutter (VFL) or ventricular tachycardia (VT) and atrial flutter (AFL) at the earlier stage may save life by defibrillation therapy. Different type of mechanism has been proposed earlier in time domain or by spectral analysis of ECG signal. In this work a frequency domain approach is proposed by spectral decomposition of ECG signal. Spectral decomposition of signal is done with the help of variational mode decomposition (VMD) technique. VMD model is used to obtain the required number of spectral mode of the test signal and their central mode of oscillation. This central frequency of decomposed signal and maximum phase change within a specified window are used to characterize ventricular tachycardia and atrial flutter and compared to normal rhythms by K-near neighbour (KNN) classification method. Accuracy of 98.6% is obtained for VT classification. The proposed method eliminates the requirement of detecting fiducial points of ECG signal as necessary in conventional classification methods.

Photoplethysmogram signal based biometric recognition using linear discriminant classifier

A preliminary study on photoplethysmogram (PPG) based biometry system is presented here. PPG is a physiological signal related to cardiac output and blood flow saturation in body. Recently it is reported that being an automatic physiological phenomenon PPG and other biosignals can be used as biometric parameters for human authentication. In this work, 12 number of features are extracted from filtered PPG and its derivatives and Linear Discriminant Analysis (LDA) is used for classification over the statistical parameters extracted from the feature set. 100% accuracy is achieved for 15 number of data captured using Biopac MP 45.

Empirical mode decomposition vs. variational mode decomposition on ECG signal processing: A comparative study

Most of the non-stationary signals need adaptive processing technique for denoising, signal processing for feature extraction and analysis. In this regard, signal decomposition methods plays a vital role as selective reconstruction extracts the enhanced version of the signal buried in the noise. Decomposition mode based analysis also becomes popular especially in case of biosignals due to their highly non-stationary nature. Biosignals are better decomposed by a technique where basis function is derived from the signal itself. This data adaptive decomposition of biosignals into different frequency modes is very effective irrespective of multiple periodicities present in the signal or unknown sampling rate. This paper aims to study the performance of Empirical Mode Decomposition (EMD) and the Variational Mode Decomposition (VMD) technique over the popular ECG signal in terms of different periodicities during various cardiac abnormalities. The results highlight the main differences between the methods in range of signal decomposition levels as well as ability of extracting both low and high frequency from the signal.

Detection of Atrial Flutter using PRSA

Automatic detection of different cardiac abnormalities is an emerging field of study in assistive diagnosis technology for cardiac diseases. A study on the feasibility of automatic detection of Atrial Flutter (AFL) based on time and frequency domain features has been presented in this paper to prevent the serious heart failure by detecting it at early stage. The proposed algorithm is developed based on feature subsets of a set of statistical time-frequency-domain parameters by using phase rectified signal average (PRSA) method. Classification of the abnormality using the derived features has been performed with the help of two class clustering method by Support Vector Machine (SVM). This classifier is tested on 382 and 587 numbers of AFL and normal cardiac cycles respectively taken from MIT-BIH Arrhythmia database. Satisfactory result is obtained as the 96% sensitivity and 98% specificity is observed.

Arduino-based noise robust online heart-rate detection

Abstract This paper introduces a noise robust real time heart rate detection system from electrocardiogram (ECG) data. An online data acquisition system is developed to collect ECG signals from human subjects. Heart rate is detected using window-based autocorrelation peak localisation technique. A low-cost Arduino UNO board is used to implement the complete automated process. The performance of the system is compared with PC-based heart rate detection technique. Accuracy of the system is validated through simulated noisy ECG data with various levels of signal to noise ratio (SNR). The mean percentage error of detected heart rate is found to be 0.72% for the noisy database with five different noise levels.

Imposed target based modification of Taguchi method for feature optimisation with application in arrhythmia beat detection

This paper shows a system for feature optimization using modified Taguchi method.This method can reduce the number of features, and classification hazards.This study enhances the rules of Taguchi method for the system has no exact output.This study classifies multiple clusters with less number of parameters.This method possesses minimal pre-processing with anchor point feature selection. Development of an expert system for clinical application includes automation in diagnosis of abnormality and patient monitoring based on features derived from continuous data set. This paper presents a novel method for feature optimization and classification of electrocardiogram (ECG) for arrhythmia analysis. A feature set optimization technique can reduce the classification hazard by selecting few comprehensive features to cater all kind of abnormalities under consideration. Proposed work deals with ranking and selection of an optimized pair of features using Taguchi method from eleven possible features normally used for characterizing arrhythmic beats like left bundle branch (LBBB), right bundle branch (RBBB) and premature ventricular contraction (PVC) are compared to normal beats. An imposed target based modification of Taguchi method is also suggested for the systems where the output is not pre-defined as in the case of biomedical applications. The proposed method is advantageous for the expert systems in which individual identity of the features are to be stored while reducing the dimensionality of the feature set. Multiclass Navis Bayes classifier is used to classify the beats in a single run and good performance parameters are obtained as reported in the result section.

Real time heart rate detection from PPG signal in noisy environment

In this paper a Photoplethysmography (PPG) based noise robust real time heart rate measurement technique is proposed. It has been developed using Arduino Uno board based on 8-bit AVR core microcontroller and having 16 MHz clock frequency. The basic idea of the proposed work is to extract the periodic PPG signal contaminated by non-periodic noise and atrifact. The algorithm is based on short term autocorrelation technique over the time shifted PPG signal. The algorithm and the developed system is validated against the heart rates derived from the signals acquired in BIOPAC MP150 data acquisition system. The designed system is highly noise robust and it can detect heart rate with almost 0% error considering BIOPAC MP150 as a standard.