Rajarshi Gupta

ECG Acquisition and Automated Remote Processing

ECG Acquisition and Automated Remote Processing - Libros de Medicina - Electrocardiografia - 99,99

An FPGA implementation of real-time QRS detection

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.

A Derivative-Based Approach for QT-Segment Feature Extraction in Digitized ECG Record

Automatic electrocardiogram analysis using different signal processing technique is one of the prominent areas of research in biomedical engineering. This paper illustrates a simple approach for time plane feature extraction in the QT segment. At first, the R peaks are accurately determined using a simple derivative-based approach and hence heart rate is calculated. Next, the baseline points of all cardiac cycles in the dataset are determined in the TP segment and the baseline modulation in the signal is eliminated by an empirical formula. Finally, the characteristic points Q, Q-offset, S, S-offset, T-onset, T and T-offset are calculated for all cycles using a magnitude and slope threshold based method. Hence, QRS width, ST segment, QT segment and (QT)c are determined. ECG data for 30 second interval from MIT-PTB diagnostic database is used for testing the algorithm.

A bi-phase enabled serial acquisition system for remote processing of digitized ECG

This paper presents an approach for acquiring digitized ECG samples in a personal computer using an offline communication technique. For this, two standalone embedded modules are placed at the two ends of the communication link. The transmit end module collects ECG samples from the source and stores them in a RAM. Finally it converts each data byte into a bi-phase encoded bit stream for transmission using a standard telephone set through post office telephone line. The receive-end module, coupled with the telephone receiver decodes the ECG data, and then delivers them to a desktop computer through the serial port. An application software in the computer is used to store these samples for visual inspection. The detailed architecture and test results are discussed with synthetic ECG data available from PTB diagnostic ECG database (ptb-db) under Physionet.

Short range centralized cardiac health monitoring system based on ZigBee communication

Remote health monitoring is a prominent area in modern biomedical research. This involves collection in different biomedical signals from patient using information and communication technology with the objective of remote end assessment of these vital conditions. This paper describes a short range centralized health monitoring system to acquire electrocardiogram (ECG) data using wireless ZigBee communication for computerized analysis. A prototype compact patient data collection system based on ATmega16L microcontroller was developed to collect and compress single lead ECG data for wireless transfer to a centralized station for remote end processing. A state of the art developed software in the central station controlled the patient modules and post acquisition data analysis. Test results with Physionet data and ECG collected from volunteers shown satisfactory result. Average compression achieved using 70 ECG files was 6.93 with average PRD and PRDN of 1.1343 and 8.4645 respectively. Feature extraction results using receiving end ECG data showed an average variance of 0.12%.

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

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.

Development of a State-of-the-Art ECG DAS for Storing, Processing and Analysis Using MATLAB-Based GUI and Microprocessor

This paper illustrates a low cost novel method for ECG signal acquisition, display and storage using a Graphical User Interface (GUI), which provides a user-friendly front end by using MATLAB-based toolsets. At first, analog ECG has been converted to its digital equivalent with the help of an ADC. Microcontrollers converted this data to a serial (RS232) format and transmit to the PC for serial acquisition. The developed GUI performs the acquisition for its future processing.

Dissimilarity factor based classification of inferior myocardial infarction ECG

Electrocardiography (ECG) is popular non-invasive technique for preliminary level investigation on cardiovascular assessment. Computerized analysis of ECG can significantly contribute towards assisted diagnosis and in early detection of many cardiac diseases. Conventional automated ECG classifiers employing soft computing tools may suffer from the inaccuracies that may result in different clinical feature extraction stages. In this paper, we propose the use of a statistical index, namely, dissimilarity factor (D) for classification of normal and Inferior Myocardial Infarction (IMI) data, without the need of any direct clinical feature extraction. Time aligned ECG beats were obtained through filtering, wavelet decomposition processes, followed by PCA based beat enhancement to generate multivariate time series data. The T wave and QRS segments of IMI datasets from Lead II, III and aVF were extracted and compared with corresponding segments of healthy patients using Physionet ptbdb data. With 35 IMI datasets, the average composite dissimilarity factor Dc between normal data was found to be 0.39, and the same between normal and abnormal data were found to be 0.65. This paper shows the promise of descriptive statistical tools as an alternative for medical signal analysis.

Classification of photoplethysmogram signal using self organizing map

Photoplethysmography (PPG) is a popular noninvasive technique to estimate cardiovascular functions by the use of infrared light at peripheral body parts. This paper describes an approach for clinical feature extraction from finger PPG under resting condition and its binary classification. The peak and foot fiducial points were detected by an algorithm that utilized the concept of threshold comparison between the consecutive samples. The other fiducial points, dicrotic notch and diastolic peak were detected using acceleration PPG (APG). The extracted clinical features were: pulse width, systolic amplitude, Peak to peak time, and ratio of areas before and after dicrotic notch in a complete cycle. These were fed to a Self Organizing Map (SOM) to form a binary classifier. In the study, short duration PPG data from 30 healthy volunteers and 20 patients with pre-assessed cardiovascular diseases were used. The average detection sensitivity for systolic peaks and foots were 100% and for diastolic peaks and dicrotic notches were 95% and 96% respectively. The correct classification of normal and abnormal data was visually estimated from the weight distance plot and Hit plot. The software can be upgraded to form a PC based online PPG assessment tool.

ECG Acquisition in a Computer

Most of the modern electrocardiogram recording systems in a clinical setup use a desktop computer as the final data acquisition element.