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%. © 2014 IEEE.

RAJARSHI GUPTA

Applied Physics

S ROY

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

&nbsp;

The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

&nbsp;

It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Short range centralized cardiac health monitoring system based on ZigBee communication

2014 IEEE Global Humanitarian Technology Conference - South Asia Satellite, GHTC-SAS 2014

In this work, a multi-lead Electrocardiogram (ECG) data compression using principal component analysis (PCA) combined with a machine learning technique is proposed to achieve a high compression ratio (CR) with low reconstruction error (within 2% percentage root mean squared difference, or, PRD). The beat detection procedure was inspired by the Pan-Tompkins algorithm with some necessary modifications. A lead-wise PCA decomposition was performed for dimensionality reduction with a single beat from each lead at a time using a fixed energy reconstruction criteria. The optimal quantization levels of the principal components were allocated using multi-layer perceptron neural network (MLP-NN) using lead clinical features as the input. This MLP-NN was tuned offline by a particle swarm optimization (PSO) generated data for quantization level of coefficients of PC as the reference. The proposed technique was evaluated using 8 types of cardiac abnormalities record from multi-lead ECG data from the PTB Diagnostic ECG database, with an average CR, PRD and PRDN of 16.2, 1.47% and 1.84% respectively. The reconstructed records were clinically acceptable. The proposed technique provides superior performance than few recent published works on multilead ECG compression. © 2018 IEEE.

Proceedings of 2018 IEEE Applied Signal Processing Conference, ASPCON 2018

Compression of multilead electrocardiogram using principal component analysis and machine learning approach

Biomedical sensors provide an interface between the electrophysiological activity of different body organs and the instruments those facilitate an in-depth analysis of the pathological conditions. They constitute the most important part of a biomedical health monitoring system. Electrocardiogram (ECG), Photoplethysmogram (PPG), Blood pressure (BP) and respiration are the most fundamental cardiac and respiratory function indicators in a human body. This chapter is aimed to provide a brief overview of these sensors and interfacing techniques for digitized acquisition. © Springer International Publishing AG 2017.

Smart Sensors, Measurement and Instrumentation

Biomedical sensors and their interfacing

Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression. © 2016, Springer Science+Business Media New York.

Journal	Data powered by Typeset2014 IEEE Global Humanitarian Technology Conference - South Asia Satellite, GHTC-SAS 2014
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open Access	No