Biomedical subjects

Abdulnasir Hossen

Publications and source records attributed to Abdulnasir Hossen.

3 recordsLinked to original sources

PubMed2006

Statistical signal characterization for congestive heart failure patient's classification.

This paper aims at investigating a new technique of time-domain analysis of heart variability (R-R interval (RRI)) for the screening of patients with Congestive Heart Failure (CHF). This method is based on the Statistical Signal Characterization (SSC) of the analytical signal that is generated using Hilbert transformation of the RRI data. The four SSC parameters are: amplitude mean, period mean, amplitude deviation and period deviation. These parameters and their maximum and minimum values are determined over sliding segments of 300-samples, 32-samples and 16-samples for both the instantaneous amplitudes and the instantaneous frequencies derived from the analytical signal of the RRI data. Data used in this work are drawn from MIT database. Threshold values used in the identification of CHF patients from normal records are selected using the Receiver Operating Characteristics (ROC) curves on trial data. This new technique correctly classifies 31/33 of trial data and 65/70 of test data.

Adult↗

PubMed2005

A soft decision algorithm for obstructive sleep apnea patient classification based on fast estimation of wavelet entropy of RRI data.

A soft decision algorithm for Obstructive Sleep Apnea (OSA) patient classification using R-R interval (RRI) data is investigated. This algorithm is based on fast and approximate estimation of the entropy of the wavelet-decomposed bands of the RRI data. The classification is done on the whole record as OSA patient or non-patient (normal). The ratio of the estimated entropy of the low-frequency (LF) band to that of the very-low frequency (VLF) band is used as a classification factor. RRI data used in this work are drawn from MIT database. The MIT trial records are used to set the threshold value of the classification factor using the Receiver Operating Characteristics (ROC). This threshold value is used then to classify the MIT challenge (test) records to obtain the efficiency of classification. The new algorithm classifies correctly 30/30 of MIT-test data using different wavelet filters. Comparison of the results of different wavelet filters is done in terms of complexity and distance parameters. The method is also compared with other two techniques using wavelets in their analysis. The consistency of the results is examined using the leave-one-out technique.

Adult↗

PubMed2004

Screening of obstructive sleep apnea based on statistical signal characterization of Hilbert transform of RRI data.

A new technique of time-domain analysis for screening of Obstructive Sleep Apnea (OSA) using R-R interval (RRI) data is investigated. This method is based on the Statistical Signal Characterization (SSC) of the analytical signal that is generated using Hilbert transformation of the RRI data. The four SSC parameters: amplitude mean, period mean, amplitude deviation and period deviation, and their maximum and minimum values are found over a 5-minutes sliding window for both the instantaneous amplitudes and the instantaneous frequencies derived from the analytical signal of the RRI data. Data used in this work are drawn from both MIT database as well as from the Sleep Laboratory at Sultan Qaboos University (SQU) hospital. Threshold values used in the identification of OSA from normal subjects are selected using the Receiver Operating Characteristics (ROC) curves. The new technique classifies correctly 29/30 of MIT Trial data, 27/30 of MIT challenge data, and 30/30 of SQU data.

Algorithms↗