Current Diagnostic Techniques for Pneumonia: A Scoping Review.

Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic techniques for pneumonia are lacking. In this scoping review, three major electronic databases were searched and the resulting research was screened. We categorized these diagnostic techniques into four classes (i.e., lab-based methods, imaging-based techniques, acoustic-based techniques, and physiological-measurement-based techniques) and summarized their recent applications. Major research has been skewed towards imaging-based techniques, especially after COVID-19. Currently, chest X-rays and blood tests are the most common tools in the clinical setting to establish a diagnosis; however, there is a need to look for safe, non-invasive, and more rapid techniques for diagnosis. Recently, some non-invasive techniques based on wearable sensors achieved reasonable diagnostic accuracy that could open a new chapter for future applications. Consequently, further research and technology development are still needed for pneumonia diagnosis using non-invasive physiological parameters to attain a better point of care for pneumonia patients.

Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection.

Anesthesia drug overdose hazards and lack of gold standards in anesthesia monitoring lead to an urgent need for accurate anesthesia drug detection. To investigate the PPG waveform features affected by anesthesia drugs and develop a machine-learning classifier with high anesthesia drug sensitivity. This study used 64 anesthesia and non-anesthesia patient data (32 cases each), extracted from Queensland and MIMIC-II databases, respectively. The key waveform features (total area, rising time, width 75%, 50%, and 25%) were extracted from 16,310 signal recordings (5-s duration). Discriminant analysis, support vector machine (SVM), and K-nearest neighbor (KNN) were evaluated by splitting the dataset into halve training (11 patients, 8570 segments) and halve testing dataset (11 patients, 7740 segments). Significant differences exist between PPG waveform features of anesthesia and non-anesthesia groups (p < 0.05) except total area feature (p > 0.05). The KNN classifier achieved 91.7% (AUC = 0.95) anesthesia detection accuracy with the highest sensitivity (0.88) and specificity (0.90) as compared to other classifiers. Kohen's kappa also shows almost perfect agreement (0.79) with the KNN classifier. The KNN classifier trained with significant PPG features has the potential to be used as a reliable, non-invasive, and low-cost method for the detection of anesthesia drugs for depth analysis during surgical operations and postoperative monitoring.

To study the effect of acute infrared radiation-induced alterations in human skin at cellular and molecular level using in vivo confocal Raman spectroscopy.

BACKGROUND: Solar radiations are classified in terms of wavelengths, including visible light, infrared, and ultraviolet. Infrared radiation (IR) accounts the largest proportion of solar radiations that cause oxidative stress-induced aging of human skin. This study investigates the biochemical changes in proteins, lipids, and DNA associated with acute exposure to IR radiations. METHOD: In vivo confocal Raman spectroscopy was used to examine the forearms region of 20 healthy participants with phototype II skin, aged between 18 and 30 years, without IR incidence (T0), with IR incidence 30 minutes (T30) at day 1 and 30 minutes at day 2 (T60). One-way ANOVA and two-tailed t test along with post hoc Bonferroni correction were used to detect the existence of significant differences in the timestamps of stratum corneum, stratum basale, and dermis at all IR wavenumbers under test. RESULTS: An increase in the Raman peaks of stratum corneum lipids, decrease in stratum basal DNA peaks, and a shift in the amide I peak of collagen in the skin dermis were observed. One-way ANOVA results showed significant differences among timestamps of stratum corneum, stratum basale, and dermis at all wavenumbers under test (P < .001). Furthermore, paired timestamps also showed significant differences (P < .016) except at two wavenumbers 1293 cm-1 and 852 cm-1 in stratum corneum and basale layer clusters on timestamps (T0 & T30 and T30 & T60, P > .016). This study proved that confocal Raman spectroscopy is an useful technique for early evaluation of IR-induced skin changes.

Consistency in Geometry Among Coronary Atherosclerotic Plaques Extracted From Computed Tomography Angiography.

Background: The three-dimensional (3D) geometry of coronary atherosclerotic plaques is associated with plaque growth and the occurrence of coronary artery disease. However, there is a lack of studies on the 3D geometric properties of coronary plaques. We aim to investigate if coronary plaques of different sizes are consistent in geometric properties. Methods: Nineteen cases with symptomatic stenosis caused by atherosclerotic plaques in the left coronary artery were included. Based on attenuation values on computed tomography angiography images, coronary atherosclerotic plaques and calcifications were identified, 3D reconstructed, and manually revised. Multidimensional geometric parameters were measured on the 3D models of plaques and calcifications. Linear and non-linear (i.e., power function) fittings were used to investigate the relationship between multidimensional geometric parameters (length, surface area, volume, etc.). Pearson correlation coefficient (r), R-squared, and p-values were used to evaluate the significance of the relationship. The analysis was performed based on cases and plaques, respectively. Significant linear relationship was defined as R-squared > 0.25 and p < 0.05. Results: In total, 49 atherosclerotic plaques and 56 calcifications were extracted. In the case-based analysis, significant linear relationships were found between number of plaques and number of calcifications (r = 0.650, p = 0.003) as well as total volume of plaques (r = 0.538, p = 0.018), between number of calcifications and total volume of plaques (r = 0.703, p = 0.001) as well as total volume of calcification (r = 0.646, p = 0.003), and between the total volumes of plaques and calcifications (r = 0.872, p < 0.001). In plaque-based analysis, the power function showed higher R-squared values than the linear function in fitting the relationships of multidimensional geometric parameters. Two presumptions of plaque geometry in different growth stages were proposed with simplified geometric models developed. In the proposed models, the exponents in the power functions of geometric parameters were in accordance with the fitted values. Conclusion: In patients with coronary artery disease, coronary plaques and calcifications are positively related in number and volume. Different coronary plaques are consistent in the relationship between geometry parameters in different dimensions.

Filtering-induced time shifts in photoplethysmography pulse features measured at different body sites: the importance of filter definition and standardization.

Objective.The waveform of a photoplethysmography (PPG) signal depends on the measurement site and individual physiological conditions. Filtering can distort the morphology of the original PPG signal waveform and change the timing of pulse feature points on PPG signals. We aim to quantitatively investigate the effect of PPG signal morphology (related to measurement site) and type of pulse feature on the filtering-induced time shift (TS).Approach.60 s PPG signals were measured from six body sites (finger, wrist under (volar), wrist upper (dorsal), earlobe, and forehead) of 36 healthy adults. Using infinite impulse response digital filters which are common in PPG signal processing, PPG signals were prefiltered (band-pass, pass and stop bands: >0.5 Hz and <0.2 Hz for high-pass filter, <20 Hz and >30 Hz for low-pass filter) and then filtered (low-pass, pass and stop bands: <3 Hz and >5 Hz). Four pulse feature points were defined and extracted (peak, valley, maximal first derivative, and maximal second derivative). For each subject, overall TS and intra-subject TS variability in feature points were calculated as the mean and standard deviation of TS between prefiltered and filtered PPG signals in 50 cardiac cycles. Statistical testing was performed to investigate the effect of measurement site and type of pulse feature on overall TS and intra-subject TS variability.Main results.Measurement site, type of pulse feature, and their interaction had significant impacts on the overall TS and intra-subject TS variability (p < 0.001 for all). Valley and maximal second derivative showed higher overall TS than peak and maximal first derivative. Finger had higher overall TS and lower intra-subject TS variability than other measurement sites.Significance. Measurement site and type of pulse feature can significantly influence the timing of feature points on filtered PPG signals. Filtering parameters should be quoted to support the reproducibility of PPG-related studies.

Extraction of Coronary Atherosclerotic Plaques From Computed Tomography Imaging: A Review of Recent Methods.

Background: Atherosclerotic plaques are the major cause of coronary artery disease (CAD). Currently, computed tomography (CT) is the most commonly applied imaging technique in the diagnosis of CAD. However, the accurate extraction of coronary plaque geometry from CT images is still challenging. Summary of Review: In this review, we focused on the methods in recent studies on the CT-based coronary plaque extraction. According to the dimension of plaque extraction method, the studies were categorized into two-dimensional (2D) and three-dimensional (3D) ones. In each category, the studies were analyzed in terms of data, methods, and evaluation. We summarized the merits and limitations of current methods, as well as the future directions for efficient and accurate extraction of coronary plaques using CT imaging. Conclusion: The methodological innovations are important for more accurate CT-based assessment of coronary plaques in clinical applications. The large-scale studies, de-blooming algorithms, more standardized datasets, and more detailed classification of non-calcified plaques could improve the accuracy of coronary plaque extraction from CT images. More multidimensional geometric parameters can be derived from the 3D geometry of coronary plaques. Additionally, machine learning and automatic 3D reconstruction could improve the efficiency of coronary plaque extraction in future studies.

Comparison of different modulations of photoplethysmography in extracting respiratory rate: from a physiological perspective.

OBJECTIVE: Based on different physiological mechanisms, the respiratory modulations of photoplethysmography (PPG) signals differ in strength and resultant accuracy of respiratory frequency (RF) estimations. We aimed to investigate the strength of different respiratory modulations and the accuracy of resultant RF estimations in different body sites and two breathing patterns. APPROACH: PPG and reference respiratory signals were simultaneously measured over 60 s from 36 healthy subjects in six sites (arm, earlobe, finger, forehead, wrist-under (volar side), wrist-upper (dorsal side)). Respiratory signals were extracted from PPG recordings using four demodulation approaches: amplitude modulation (AM), baseline wandering (BW), frequency modulation (FM) and filtering. RFs were calculated from the PPG-derived and reference respiratory signals. To investigate the strength of respiratory modulations, the energy proportion in the range that covers 75% of the total energy in the reference respiratory signal, with RF in the middle, was calculated and compared between different modulations. Analysis of variance and the Scheirer-Ray-Hare test were performed with post hoc analysis. MAIN RESULTS: In normal breathing, FM was the only modulation whose RF was not significantly different from the reference RF (p > 0.05). Compared with other modulations, FM was significantly higher in energy proportion (p < 0.05) and lower in RF estimation error (p < 0.05). As to energy proportion, measurements from the finger and the forehead were not significantly different (p > 0.05), but both were significantly different from the other four sites (p < 0.05). In deep breathing, the RFs derived by BW, filtering and FM were not significantly different from the reference RF (p > 0.05). The RF estimation error of FM was significantly less than that of AM or BW (p < 0.05). The energy proportion of FM was significantly higher than that of other modulations (p < 0.05). SIGNIFICANCE: Of all the respiratory modulations, FM has the highest strength and is appropriate for accurate RF estimation from PPG signals recorded at different sites and for different breathing patterns.

Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches.

Introduction: Blood pressure (BP) has been a potential risk factor for cardiovascular diseases. BP measurement is one of the most useful parameters for early diagnosis, prevention, and treatment of cardiovascular diseases. At present, BP measurement mainly relies on cuff-based techniques that cause inconvenience and discomfort to users. Although some of the present prototype cuffless BP measurement techniques are able to reach overall acceptable accuracies, they require an electrocardiogram (ECG) and a photoplethysmograph (PPG) that make them unsuitable for true wearable applications. Therefore, developing a single PPG-based cuffless BP estimation algorithm with enough accuracy would be clinically and practically useful. Methods: The University of Queensland vital sign dataset (online database) was accessed to extract raw PPG signals and its corresponding reference BPs (systolic BP and diastolic BP). The online database consisted of PPG waveforms of 32 cases from whom 8133 (good quality) signal segments (5 s for each) were extracted, preprocessed, and normalised in both width and amplitude. Three most significant pulse features (pulse area, pulse rising time, and width 25%) with their corresponding reference BPs were used to train and test three machine learning algorithms (regression tree, multiple linear regression (MLR), and support vector machine (SVM)). A 10-fold cross-validation was applied to obtain overall BP estimation accuracy, separately for the three machine learning algorithms. Their estimation accuracies were further analysed separately for three clinical BP categories (normotensive, hypertensive, and hypotensive). Finally, they were compared with the ISO standard for noninvasive BP device validation (average difference no greater than 5 mmHg and SD no greater than 8 mmHg). Results: In terms of overall estimation accuracy, the regression tree achieved the best overall accuracy for SBP (mean and SD of difference: -0.1 ± 6.5 mmHg) and DBP (mean and SD of difference: -0.6 ± 5.2 mmHg). MLR and SVM achieved the overall mean difference less than 5 mmHg for both SBP and DBP, but their SD of difference was >8 mmHg. Regarding the estimation accuracy in each BP categories, only the regression tree achieved acceptable ISO standard for SBP (-1.1 ± 5.7 mmHg) and DBP (-0.03 ± 5.6 mmHg) in the normotensive category. MLR and SVM did not achieve acceptable accuracies in any BP categories. Conclusion: This study developed and compared three machine learning algorithms to estimate BPs using PPG only and revealed that the regression tree algorithm was the best approach with overall acceptable accuracy to ISO standard for BP device validation. Furthermore, this study demonstrated that the regression tree algorithm achieved acceptable measurement accuracy only in the normotensive category, suggesting that future algorithm development for BP estimation should be more specific for different BP categories.