ORCID Profile
0000-0001-8345-0952
Current Organisations
Deakin University
,
Kongju National University
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Publisher: Korean Institute of Information Technology
Date: 31-01-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 10-2017
Publisher: Korean Institute of Information Technology
Date: 21-12-2017
Publisher: IEEE
Date: 10-2016
Publisher: MDPI AG
Date: 22-04-2023
DOI: 10.3390/S23094178
Abstract: Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).
Publisher: World Scientific Pub Co Pte Ltd
Date: 21-03-2023
Publisher: The Royal Society
Date: 08-2023
DOI: 10.1098/RSOS.221382
Abstract: The onset of stress triggers sympathetic arousal (SA), which causes detectable changes to physiological parameters such as heart rate, blood pressure, dilation of the pupils and sweat release. The objective quantification of SA has tremendous potential to prevent and manage psychological disorders. Photoplethysmography (PPG), a non-invasive method to measure skin blood flow changes, has been used to estimate SA indirectly. However, the impact of various wavelengths of the PPG signal has not been investigated for estimating SA. In this study, we explore the feasibility of using various statistical and nonlinear features derived from peak-to-peak (AC) values of PPG signals of different wavelengths (green, blue, infrared and red) to estimate stress-induced changes in SA and compare their performances. The impact of two physical stressors: and Hand Grip are studied on 32 healthy in iduals. Linear (Mean, s.d.) and nonlinear (Katz, Petrosian, Higuchi, S En, TotalS En) features are extracted from the PPG signal’s AC litudes to identify the onset, continuation and recovery phases of those stressors. The results show that the nonlinear features are the most promising in detecting stress-induced sympathetic activity. TotalS En feature was capable of detecting stress-induced changes in SA for all wavelengths, whereas other features (Petrosian, AvgS En) are significant (AUC ≥ 0.8) only for IR and Red wavelengths. The outcomes of this study can be used to make device design decisions as well as develop stress detection algorithms.
Publisher: The Royal Society
Date: 04-2022
Abstract: Electrocardiogram (ECG) signal quality indices (SQIs) are essential for improving diagnostic accuracy and reliability of ECG analysis systems. In various practical applications, the ECG signals are corrupted by different types of noise. These corrupted ECG signals often provide insufficient and incorrect information regarding a patient’s health. To solve this problem, signal quality measurements should be made before an ECG signal is used for decision-making. This paper investigates the robustness of existing popular statistical signal quality indices (SSQIs): relative power of QRS complex (SQI p ), skewness (SQI skew ), signal-to-noise ratio (SQI snr ), higher order statistics SQI (SQI hos ) and peakedness of kurtosis (SQI kur ). We analysed the robustness of these SSQIs against different window sizes across erse datasets. Results showed that the performance of SSQIs considerably fluctuates against varying datasets, whereas the impact of varying window sizes was minimal. This fluctuation occurred due to the use of a static threshold value for classifying noise-free ECG signals from the raw ECG signals. Another drawback of these SSQIs is the bias towards noise-free ECG signals, that limits their usefulness in clinical settings. In summary, the fixed threshold-based SSQIs cannot be used as a robust noise detection system. In order to solve this fixed threshold problem, other techniques can be developed using adaptive thresholds and machine-learning mechanisms.
No related grants have been discovered for MD Saifur Rahman.