H. K. Chatterjee

Bio: H. K. Chatterjee is an academic researcher from Camellia Institute of Technology. The author has contributed to research in topics: QRS complex & Personal computer. The author has an hindex of 4, co-authored 8 publications receiving 95 citations.

An FPGA implementation of real-time QRS detection

Abstract: This paper illustrates a simple algorithm for real time QRS detection from ECG data. The algorithm is implemented on Xilinx field programmable gate array using very small number of memory cells. Single lead Synthetic ECG using ptb-db database (from Physionet) is generated from a personal computer using the parallel port (LPT1) at 1 ms sampling interval and delivered to the FPGA (Field Programmable Gate Array) board. At first, from the first 1500 samples, the QRS detection algorithm calculates some characteristic amplitude and slope based signatures which are used to form a rule base. These rules are used for detecting the next incoming QRS regions accurately. The index points of R-peaks are determined and shown in the LEDs using switch-based commands.

Real-time detection of electrocardiogram wave features using template matching and implementation in FPGA

Abstract: Electrocardiogram (ECG) can provide valuable clinical information on cardiac functions. This paper illustrates an algorithm for real–time detection of wave peaks and their features from single lead ECG data. At first, the ECG data was filtered for power line interference and high frequency noise. Then, a set of slope and polarity–based rule bases were generated from the first 6000 samples, which define templates of R–peak, P–and T–wave detection from the following beats. The algorithm was implemented on Xilinx Spartan III Field Programmable Gate Array (FPGA). For testing of the algorithm, ECG data was quantised at 8–bit resolution and delivered to the FPGA using synchronous transfer mechanism using parallel port of computer. Xilinx implementation results provided 97.58%, 98.4% and 97.78% detection sensitivity for P–, R– and T–waves, respectively. Different wave features (height, polarity and duration) were detected with an average error rate of 9.3%. The detected wave signatures were clinically validated by medical expert.

A microcontroller based system for real-time heart rate estimation from ECG signal

Abstract: This paper illustrates an algorithm for real time detection QRS complex from ECG signal for computation of heart rate. The algorithm is implemented on a standalone embedded system based on Atmel 89C51 microcontroller. Synthetic ECG is generated using Physionet data through the parallel port (LPT1) of a personal computer and delivered to the embedded system. During an initial training period of first 1500 samples, some amplitude and slope based signatures are learned to form a rule base, which are used for detecting the subsequent QRS regions accurately. An average sensitivity of 97.82% and predictivity of 98.35% respectively are obtained from MIT BIH arrhythmia data. From the detected successive R peak locations heart rate has been computed.

Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification

Abstract: In this paper, we use cross wavelet transform (XWT) for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time-domain signals gives a measure of similarity between two waveforms. The application of the continuous wavelet transform to two time series and the cross examination of the two decompositions reveal localized similarities in time and frequency. Application of the XWT to a pair of data yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences. A pathologically varying pattern from the normal pattern in the QT zone of the inferior leads shows the presence of inferior myocardial infarction. A normal beat ensemble is selected as the absolute normal ECG pattern template, and the coherence between various other normal and abnormal subjects is computed. The WCS and WCOH of various ECG patterns show distinguishing characteristics over two specific regions R1 and R2, where R1 is the QRS complex area and R2 is the T-wave region. The Physikalisch-Technische Bundesanstalt diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH. Empirical tests establish that the parameter(s) are relevant for classification of normal and abnormal cardiac patterns. The overall accuracy, sensitivity, and specificity after combining the three leads are obtained as 97.6%, 97.3%, and 98.8%, respectively.

A comprehensive survey of wearable and wireless ECG monitoring systems for older adults

Abstract: Wearable health monitoring is an emerging technology for continuous monitoring of vital signs including the electrocardiogram (ECG). This signal is widely adopted to diagnose and assess major health risks and chronic cardiac diseases. This paper focuses on reviewing wearable ECG monitoring systems in the form of wireless, mobile and remote technologies related to older adults. Furthermore, the efficiency, user acceptability, strategies and recommendations on improving current ECG monitoring systems with an overview of the design and modelling are presented. In this paper, over 120 ECG monitoring systems were reviewed and classified into smart wearable, wireless, mobile ECG monitoring systems with related signal processing algorithms. The results of the review suggest that most research in wearable ECG monitoring systems focus on the older adults and this technology has been adopted in aged care facilitates. Moreover, it is shown that how mobile telemedicine systems have evolved and how advances in wearable wireless textile-based systems could ensure better quality of healthcare delivery. The main drawbacks of deployed ECG monitoring systems including imposed limitations on patients, short battery life, lack of user acceptability and medical professional's feedback, and lack of security and privacy of essential data have been also discussed.