A Low Complexity Architecture for Online On-chip Detection and Identification of f-QRS Feature for Remote Personalized Health Care Applications

This paper introduces a novel low complexity highly accurate on-chip architecture for the detection of fragmented QRS (f-QRS) feature including notches and local extrema in the QRS complexes and subsequently identifies its various morphologies (Notched S, rsR', RsR' without elevation etc.) under the real-time environment targeting remote personalized health care. The proposed architecture uses the outcome of recently proposed Hybrid feature extraction algorithm (HFEA) [1] Level 3 detailed coefficients and detects and identifies the fragmentation feature from the QRS complex based on the criteria of the positions, and the magnitudes of the extrema (maxima and minima) and notches from the wavelet coefficients with no extra cost in terms of arithmetic complexity. To verify the proposed architecture 100 patients were randomly selected from the MIT-BIH Physio Net PTB database and their ECG was examined by two experienced cardiologists individually and the results were compared with those obtained from the architecture output wherein we have achieved 95 % diagnostic matching

Classification methodology of CVD with localized feature analysis using Phase Space Reconstruction targeting personalized remote health monitoring

2016 Computing in Cardiology Conference (CinC), 11-14 September 2016, Vancouver, BC, CanadaThis is the final version of the article. Available from the publisher via the DOI in this recordThis paper introduces the classification methodology of Cardiovascular Disease (CVD) with localized feature analysis using Phase Space Reconstruction (PSR) technique targeting personalized health care. The proposed classification methodology uses a few localized features (QRS interval and PR interval) of individual Electrocardiogram (ECG) beats from the Feature Extraction (FE) block and detects the desynchronization in the given intervals after applying the PSR technique. Considering the QRS interval, if any notch is present in the QRS complex, then the corresponding contour will appear and the variation in the box count indicating a notch in the QRS complex. Likewise, the contour and the disparity of box count due to the variation in the PR interval localized wave have been noticed using the proposed PSR technique. ECG database from the Physionet (MIT-BIH and PTBDB) has been used to verify the proposed analysis on localized features using proposed PSR and has enabled us to classify the various abnormalities like fragmented QRS complexes, myocardial infarction, ventricular arrhythmia and atrial fibrillation. The design have been successfully tested for diagnosing various disorders with 98% accuracy on all the specified abnormal databases.This work is partly supported by the Department of Electronics and Information and Technology (DeitY), India under the “Internet of Things (IoT) for Smarter Healthcare” under Grant No: 13(7)/2012-CC&BT, dated 25 Feb 2013. Naresh V is funded by Ministry of Human Resource Development (MHRD) PhD studentship through IIT Hyderabad

Phase Space Reconstruction Based CVD Classifier Using Localized Features

This is the final version. Available on open access from Nature Research via the DOI in this recordData Availability: The datasets analysed during the current study are available in the ‘PhysioNet’; the web address is [https://physionet.org/cgi-bin/atm/ATM].This paper proposes a generalized Phase Space Reconstruction (PSR) based Cardiovascular Diseases (CVD) classification methodology by exploiting the localized features of the ECG. The proposed methodology first extracts the ECG localized features including PR interval, QRS complex, and QT interval from the continuous ECG waveform using features extraction logic, then the PSR technique is applied to get the phase portraits of all the localized features. Based on the cleanliness and contour of the phase portraits CVD classification will be done. This is first of its kind approach where the localized features of ECG are being taken into considerations unlike the state-of-art approaches, where the entire ECG beats have been considered. The proposed methodology is generic and can be extended to most of the CVD cases. It is verified on the PTBDB and IAFDB databases by taking the CVD including Atrial Fibrillation, Myocardial Infarction, Bundle Branch Block, Cardiomyopathy, Dysrhythmia, and Hypertrophy. The methodology has been tested on 65 patients’ data for the classification of abnormalities in PR interval, QRS complex, and QT interval. Based on the obtained statistical results, to detect the abnormality in PR interval, QRS complex and QT interval the Coefficient Variation (CV) should be greater than or equal to 0.1012, 0.083, 0.082 respectively with individual accuracy levels of 95.3%, 96.9%, and 98.5% respectively. To justify the clinical significance of the proposed methodology, the Confidence Interval (CI), the p-value using ANOVA have been computed. The p-value obtained is less than 0.05, and greater F-statistic values reveal the robust classification of CVD using localized features.Department of Science & Technology (DST

Low Power Personalized ECG Based System Design Methodology for Remote Cardiac Health Monitoring

This paper describes a mixed-signal ECG system for personalized and remote cardiac health monitoring. The novelty of this work is four-fold. Firstly, a low power analog front end with an efficient automatic gain control mechanism, maintaining the input of the ADC to a level rendering optimum SNR and the enhanced recyclic folded cascode opamp used as an integrator for ADC. Secondly, a novel on-the-fly PQRST Boundary Detection (BD) methodology is formulated for finding the boundaries in continuous ECG signal. Thirdly, a novel low-complexity ECG feature extraction architecture is designed by reusing the same module present in the proposed BD methodology. Fourthly, the system is having the capability to reconfigure the proposed Low power ADC for low (8 bits) and high (12 bits) resolution with the use of the feedback signal obtained from the digital block when it is in processing. The proposed system has been tested and validated on patient’s data from PTBDB, CSEDB and in-house IIT Hyderabad DB (IITHDB) and we have achieved an accuracy of 99% upon testing on various normal and abnormal ECG signals. The whole system is implemented in 180 nm technology resulting in 9.47W (@ 1 MHz) power consumption and occupying 1.74mm2 silicon area

Classification Methodology of CVD with Localized Feature Analysis Using Phase Space Reconstruction Targeting Personalized Remote Health Monitoring

This paper introduces the classification methodology of C ardiovascular Disease ( CVD ) with lo calized feature analysis using Phase Space R econstruction (PSR) technique targeting personalized health care . The proposed classification methodology uses a few localized features (QRS inter val and PR interval ) of individual Electrocardiogram ( ECG ) beats from the Feature Extraction (FE) block and detect s the desynchronization in the given intervals after applying the PSR technique. Considering the QRS interval, if any notch is present in the QRS complex, then th e corresponding contour will appear and the variation in the box count indicating a notch in the QRS complex. Likewise, the contour and the disparity of box count due to the variation in the PR interval localized wave have been noticed using the proposed PSR technique. ECG database from the Phy sionet (MIT - BIH and PTBDB) has been used t o verify the proposed analysis on localized features using proposed PSR and has enabled us to classify the various abnorm alities like fragmented QRS complexes, myocardial infarction , ventricular arrhythmia and atrial fibrillation. The design have been successfully tested for diagnosing various disorders with 98% accuracy on all the specified abnormal databases

CORDIC based Universal Modulator

In the literature, different architectures have been proposed for FPGA implementation of Universal Modulator. The Look up table (LUT) technique is one way of realizing universal modulator. In this paper, a pipelined CORDIC using 2 stages of Multiplexer is proposed for efficient realization of universal modulator. For the purpose of comparison, this universal modulator is implemented on Spartan 3E FPGA and its performance is compared with that of unrolled CORDIC which uses only shifters and adders and unpipelined Multiplexer based CORDIC. The Universal Modulator is used for realizing Amplitude, Frequency and Phase Modulation. From the implementation, it is found that the pipelined multiplexer based Modulator is 5% more area efficient and 4 % more speed efficient than unrolled CORDIC

Phase Space Reconstruction Based CVD Classifier Using Localized Features

This paper proposes a generalized Phase Space Reconstruction (PSR) based Cardiovascular Diseases (CVD) classification methodology by exploiting the localized features of the ECG. The proposed methodology first extracts the ECG localized features including PR interval, QRS complex, and QT interval from the continuous ECG waveform using features extraction logic, then the PSR technique is applied to get the phase portraits of all the localized features. Based on the cleanliness and contour of the phase portraits CVD classification will be done. This is first of its kind approach where the localized features of ECG are being taken into considerations unlike the state-of-art approaches, where the entire ECG beats have been considered. The proposed methodology is generic and can be extended to most of the CVD cases. It is verified on the PTBDB and IAFDB databases by taking the CVD including Atrial Fibrillation, Myocardial Infarction, Bundle Branch Block, Cardiomyopathy, Dysrhythmia, and Hypertrophy. The methodology has been tested on 65 patients' data for the classification of abnormalities in PR interval, QRS complex, and QT interval. Based on the obtained statistical results, to detect the abnormality in PR interval, QRS complex and QT interval the Coefficient Variation (CV) should be greater than or equal to 0.1012, 0.083, 0.082 respectively with individual accuracy levels of 95.3%, 96.9%, and 98.5% respectively. To justify the clinical significance of the proposed methodology, the Confidence Interval (CI), the p-value using ANOVA have been computed. The p-value obtained is less than 0.05, and greater F-statistic values reveal the robust classification of CVD using localized features

A Robust Reliable and Low Complexity on Chip f-QRS Detection and Identification Architecture for Remote Personalized Health Care Applications

With increasing number of cardiovascular cases throughout the world, personalized remote healthcare has emerged as a solution to the constraints encountered by pervasive and affordable healthcare from both comfort and economic perspectives. Personalized healthcare applications, generally involve battery operated devices which form a part of Internet-of-Things (IoT) or Cyber-physical systems (CPS), where power becomes a major bottleneck. With this motivation, we present a novel low-complexity on-chip architectural implementation of our recently proposed algorithm for automated detection and identification of fragmentation in QRS complex of standard 12-lead ECG signals. The proposed architecture also identifies the morphology of frag mented QRS viz. Notched S, RsR' without elevation, rsR' and many other. QRS complexes were extracted using our recently proposed Hybrid Feature Extraction Algorithm (HEFA). The proposed algorithm applies discrete wavelet transform using haar wavelet on the QRS complex to identify the position of occurrence of discontinuities/extrema and lays out classification rules for various types of fragmentation documented in the literature. Haar wavelet was chosen because of its enhanced time-resolution, accurate discontinuity detection properties. Its moving average nature allows simple hardware implementation. The verification of results of the architecture has been performed using 100 patients from MIT-BIH database and PhysioNet PTB database and their ECG were examined by two experienced cardiologists individually and the results were compared with those obtained from the architecture output, wherein we have achieved 95% diagnostic matching. The design has been implemented in 130 nm technology and operated at 1 MHz with V dd 1.3 V. The power consumption and area were found to be 22.4 μW and 0.22 mm2 respectively

Affordable low complexity heart/brain monitoring methodology for remote health care

This paper introduces a dual-mode low complex on-chip methodology for processing of ECG (Electrocardiogram) and EEG (Electroencephalography) signals, wherein based on the input switch the architecture can be dynamically configured to operate either as an ECG bio-marker or EEG signal de-noising system. In both the modes the signal processing technique depends on the output of the DWT (Discrete Wavelet Transform), hence a low complex methodology has been developed in which both ECG and EEG processing blocks sharing the same DWT block resulting in low area and low power consumption. The integrated ECG and EEG methodology has been implemented in Matlab, for verifying the ECG processing block the ECG database is taken from MIT-BIH PTBDB and IITH DB, similarly for EEG processing block the EEG signals are taken from PhysioNet database. The outcome of methodology in Matlab is equal to the results obtained from individual ECG and EEG blocks

P911Universal S-ICD eligibility: eliminating the need for pre-implant screening using mathematical vector rotation and a gradient filter

S-ICD eligibility is determined by ECG morphology across three sensing vectors; primary (P), secondary (S) and alternate (A). Small R:T ratios and low amplitude signals confer an unacceptable risk of oversensing and are unsuitable. The vector score is a composite measure of signal amplitude and R:T ratio calculated using an S-ICD simulator. Eligibility requires a single vector to score > 100. Around 5% of ICD patients have no suitable vector, this rises to 13-16% in some patient groups (ACHD, hypertrophic cardiomyopathy). Mathematical vector rotation is a novel technique which can generate vectors, at any given angle of observation, using signal recorded in the current S-ICD position. Vector rotation alters the relative amplitudes of both R and T such that for any individual, the largest R wave vector (Rmax) and the smallest T wave vector (Tmin) can be generated. We hypothesise that combining these signals, using a gradient filter to identify periods of rapidly changing signal amplitude, will significantly increase both R:T ratio and vector scores. Application of this programming to a cohort of patients, who are currently S-ICD ineligible, has the potential to produce universal device eligibility