Pulse signal is prone to corruption with motion artifacts (MA) due to attachment of the sensor to extreme body parts like finger, toes and forehead. This paper compares the performance between two popular statistical signal processing tools, viz., principal component analysis (PCA) with fast independent component analysis (/ICA) in reduction of MA from finger pulse signal collected from 30 human volunteers. A multivariate dataset was generated with systolic peak-aligned Photoplethysmogram (PPG) beats extracted from time series data. After eigenvalues decomposition of the covariance matrix, the original data was reconstructed using the first principal component. The mean correlation coefficient of average beat template of ICA preprocessed data and clean data, averaged over 30 volunteers is 0.9876 while that of PCA preprocessed data with clean data is 0.9778. With white Gaussian noise of known SNR, maximum absolute error for PCA preprocessed data is very small, 3.14% from SNR 25dB onwards. It was also found that beat to beat correlation is higher in the PCA preprocessed data. © 2017 IEEE.

RAJARSHI GUPTA

Applied Physics

P GHOSAL

P KUNDU

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.

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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.

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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

Finger photoplethysmogram signal enhancement: Comparing performance between PCA and ICA methods

Proceedings of 2017 International Conference on Innovations in Electronics, Signal Processing and Communication, IESC 2017

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. © 2015 IEEE.

Proceedings of 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2015

Classification of photoplethysmogram signal using self organizing map

On-site measurement of physiological information can provide a quick assessment of patients' health condition. The paper illustrates a simple PPG acquisition and preliminary level analysis instrument using a laptop. A portable data acquisition system based on ATmega16 microcontroller was developed to collect 30 sec PPG data at 250 Hz sampling using USB interface. Based on the data, all the fiducial points were detected using first and second difference PPG. The software computes the heart rate, peak to peak time, stiffness index and crest time averaged over all cardiac cycles and displays them on the front panel of the laptop. © 2016 IEEE.

2016 2nd International Conference on Control, Instrumentation, Energy and Communication, CIEC 2016

Portable photoplethysmogram acquisition and analysis instrument

2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD)

Photoplethysmogram (PPG) signal analysis and wavelet de-noising

2011 IEEE Recent Advances in Intelligent Computational Systems

Use of Multi-Scale Principal Component Analysis for motion artifact reduction of PPG signals

2011 International Conference on Communications and Signal Processing

Extraction of respiratory activity from PPG and BP signals using Principal Component Analysis

Journal	Data powered by TypesetProceedings of 2017 International Conference on Innovations in Electronics, Signal Processing and Communication, IESC 2017
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open Access	No