Rajarshi Gupta

Bio: Rajarshi Gupta is an academic researcher from University of Calcutta. The author has contributed to research in topics: Discrete wavelet transform & Wavelet. The author has an hindex of 14, co-authored 62 publications receiving 712 citations.

Zonal complexity based measure for lossy compression of photoplethysmogram using delta encoding

Abstract: Photoplethysmography (PPG) is one of the prime non-invasive vascular assessment methodology adopted in modern clinical practice. This paper proposes a modification of delta modulation for effective compression of PPG signal for real time measurements and monitoring applications. Based on the energy content and fluctuations, the PPG signal was windowed and categorized into ‘complex’ or ‘plain’ zones. The algorithm applies an adaptive hard thresholding, run length encoding and selective biasing of the first difference array based on zonal complexity. The final stage employs a selective nibble combination or offsetting rule for further compression. For performance assessment of the compression-decompression algorithms, PPG data was collected from fingers of healthy volunteers using Biopac Systems®. An average compression ratio of 3.84, percentage root mean square difference of 5.82 and percentage root mean square difference normalized of 7.57 were obtained with 20 sets of volunteers' data at 10 bit resolution and 125 Hz sampling. The decompressed data were clinically validated by Cardiologists.

Preserving Abnormal Beat Morphology in Long-Term ECG Recording: An Efficient Hybrid Compression Approach

Abstract: In long-term electrocardiogram (ECG) recording for arrhythmia monitoring, using a uniform compression strategy throughout the entire data to achieve high compression efficiency may result in unacceptable distortion of abnormal beats. The presented work addressed a solution to this problem, rarely discussed in published research. A support vector machine (SVM)-based binary classifier was implemented to identify the abnormal beats, achieving a classifier sensitivity (SE) and negative predictive value (NPV) of 99.89% and 0.003%, respectively with 34 records from MIT-BIH Arrhythmia database (mitdb). A hybrid lossy compression technique was implemented to ensure on-demand quality, either in terms of distortion or compression ratio (CR) of ECG data. A wavelet-based compression for the abnormal beats was implemented, while the consecutive normal beats were compressed in groups using a hybrid encoder, employing a combination of wavelet and principal component analysis. Finally, a neural network-based intelligent model was used, which was offline tuned by a particle swarm optimization (PSO) technique, to allocate optimal quantization level of transform domain coefficients generated from the hybrid encoder. The proposed technique was evaluated with four types of morphology tags, “A,” “F,” “L,” and “V,” from mitdb database, achieving less than 2% PRDN and less than 1% in two diagnostic distortion measures for abnormal beats. Overall, an average CR of 19.78 and PRDN of 3.34% was obtained. A useful outcome of the proposed technique is the low reconstruction time in rapid screening of long arrhythmia records, while only abnormal beats are presented for evaluation.

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.

Electrocardiogram compression technique for global system of mobile-based offline telecardiology application for rural clinics in india

Abstract: Compression of Electrocardiographic (ECG) data is an important requirement to develop an efficient telecardiology application. This study describes an offline compression technique, which is implemented for ECG transmission in a global system of mobile (GSM) network for preliminary level evaluation of patient's cardiac condition in a non-critical condition. A short-duration (5 - 6 beats) ECG data from Massachusetts Institute of Technology- Beth Israel Hospital (MIT - BIH) arrhythmia database is used for the trial. The compression algorithm is based on direct processing of ECG samples in four major steps: viz., down-sampling of dataset, normalising inter-sample differences, grouping for sign and magnitude encoding, zero element compression and finally, conversion of bytes into corresponding 8 bit American standard code for information interchange (ASCII) characters. The developed software at the patient side computer also converts the compressed data file into formatted sequence of short text messages (SMSs). Using a dedicated GSM module these message are delivered to the mobile phone of the remote cardiologist. The received SMSs are to be downloaded at the authors computer for concatenation and decompression to obtain back the original ECG for visual or automated investigation. An average percentage root-mean- squared difference and compression ratio values of 43.54 and 1.73 are obtained, respectively, with MIT - BIH arrhythmia data. The proposed technique is useful for rural clinics in India for preliminary level cardiac investigation.

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.

Privacy and Security in Mobile Health Apps: A Review and Recommendations

Abstract: In a world where the industry of mobile applications is continuously expanding and new health care apps and devices are created every day, it is important to take special care of the collection and treatment of users' personal health information. However, the appropriate methods to do this are not usually taken into account by apps designers and insecure applications are released. This paper presents a study of security and privacy in mHealth, focusing on three parts: a study of the existing laws regulating these aspects in the European Union and the United States, a review of the academic literature related to this topic, and a proposal of some recommendations for designers in order to create mobile health applications that satisfy the current security and privacy legislation. This paper will complement other standards and certifications about security and privacy and will suppose a quick guide for apps designers, developers and researchers.

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.