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OBJECTIVES: The objectives of the study were to (i) determine the incidence of type 1 diabetes mellitus (T1DM) in children aged <15 yr in Victoria, Australia, from 1999 to 2002 and (ii) to analyze trends in incidence over this period. METHODS: Prospective population-based incidence study. The primary source of case ascertainment was from the Australasian Paediatric Endocrine Group (APEG) Victorian diabetes register. The secondary source was the National Diabetes Register (NDR), which ascertains cases from the National Diabetes Service Scheme (NDSS), a Commonwealth government initiative, where patients register to receive diabetes supplies at a subsidized price. MAIN OUTCOME MEASURES: Age-standardized incidence, trends in incidence by age, sex and year, and variation in incidence by region, season, and socioeconomic status. RESULTS: Case ascertainment was 99.1% complete using the capture-recapture method. The mean annual age-standardized incidence was 19.3 per 100 000 person years from 1999 to 2002. On average, incidence increased by 9.3% per year, with a greater relative increase in the 0-4 yr age-group (p = 0.037). No gender bias in incidence was found, but the increase in females was statistically significant (13.6% per year, 95% confidence interval 3.7-24.3). Variation in geographical distribution and seasonal onset of incidence was not statistically significant. CONCLUSIONS: The marked increase in the incidence of T1DM in Victoria is greater than that recently described in other Australia states and developed nations. The etiology of this rise is unclear, while the increased caseload has major implications for diabetes health care providers for current and future resource allocation. [ABSTRACT FROM AUTHOR]

Background: Meteorin-like protein (METRNL)/Interleukin-41 (IL-41) is a novel immune-secreted cytokine/myokine involved in several inflammatory diseases. However, how METRNL exerts its regulatory properties on skin inflammation remains elusive. This study aims to elucidate the functionality and regulatory mechanism of METRNL in atopic dermatitis (AD)., Methods: METRNL levels were determined in skin and serum samples from patients with AD and subsequently verified in the vitamin D3 analogue MC903-induced AD-like mice model. The cellular target of METRNL activity was identified by multiplex immunostaining, single-cell RNA-seq and RNA-seq., Results: METRNL was significantly upregulated in lesions and serum of patients with dermatitis compared to healthy controls (p <.05). Following repeated MC903 exposure, AD model mice displayed elevated levels of METRNL in both ears and serum. Administration of recombinant murine METRNL protein (rmMETRNL) ameliorated allergic skin inflammation and hallmarks of AD in mice, whereas blocking of METRNL signaling led to the opposite. METRNL enhanced β-Catenin activation, limited the expression of Th2-related molecules that attract the accumulation of Arginase-1 (Arg1) hi macrophages, dendritic cells, and activated mast cells., Conclusions: METRNL can bind to KIT receptor and subsequently alleviate the allergic inflammation of AD by inhibiting the expansion of immune cells, and downregulating inflammatory gene expression by regulating the level of active WNT pathway molecule β-Catenin., (© 2024 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.)

Background: Little is known about the prevalence of dermatoses in "skin-well" geriatric Singaporeans. We aim to identify the prevalence of dermatoses and their associations within the geriatric population in Singapore, and to understand the distribution of dermatological encounters presenting to primary care physicians, and the resultant referral behaviour., Methods: A joint quantitative-qualitative study was performed across 8 months. Patients aged 65 years and above who visited a local polyclinic for management of non-dermatological chronic diseases were recruited. They were administered questionnaires, and underwent full skin examinations. Online surveys were disseminated to polyclinic physicians under the same healthcare cluster., Results: 201 patients and 53 physicians were recruited. The most common dermatoses identified in patients were benign tumours and cysts (97.5%), and asteatosis (81.6%). For every 1-year increase in age, the odds of having asteatosis increased by 13.5% (95% CI 3.4-24.7%, p = 0.008), and urticarial disorders by 14.6% (95% CI 0.3-30.9%, p = 0.045). Patients who used any form of topical preparations on a daily basis had higher odds of having eczema and inflammatory dermatoses (OR 2.51, 95% CI 1.38 to 4.56, p = 0.003). Physicians reported dermatological conditions involving 20% of all clinical encounters. Eczema represented the most commonly reported dermatosis within the first visit. 50% of dermatology referrals were done solely at the patient's own request., Conclusion: The prevalence of dermatoses in the elderly in Singapore is high, especially asteatosis. Prompt recognition by the primary healthcare provider potentially prevents future morbidity. Outreach education for both primary care physicians and the general public will be key., Ethics Approval: National Healthcare group (NHG) Domain Specific Review Board (DSRB), Singapore, under Trial Registration Number 2020/00239, dated 11 August 2020., (© 2024. The Author(s).)

A new pneumonia detection method is proposed to provide both pneumonia detection in respiratory sound signals and wheeze and crackle discrimination when pneumonia episodes are detected. In the proposed method, two-step hierarchy, classifying pneumonia in the first step and discriminating wheezing and crackling in the second step, is considered; the conventional pneumonia detection method is modified to improve pneumonia detection performance, while wheezing and crackling discrimination functionality is added to facilitate the application of appropriate remedies for each case. We used resampling techniques to address the imbalance in the ICBHI pneumonia dataset. The random forest algorithm is used to classify pneumonia from healthy respiratory data, as well as to distinguish between wheeze and crackle from pneumonia data. Against the ICBHI respiratory dataset, the proposed random forest-based hierarchy pneumonia detection method provides 85.40% accuracy in detecting pneumonia and 82.70% accuracy in discriminating wheeze from crackling, respectively.

Biochar is a high-carbon-content organic compound that has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant-based, animal-based, and municipal waste at different pyrolysis conditions. However, it is difficult to produce biochar on a large scale if the relationship between the type of biomass, operating conditions, and biochar properties is not understood well. Hence, the use of machine learning-based data analysis is necessary to find the relationship between biochar production parameters and feedstock properties with biochar energy properties. In this work, a rough set-based machine learning (RSML) approach has been applied to generate decision rules and classify biochar properties. The conditional attributes were biomass properties (volatile matter, fixed carbon, ash content, carbon, hydrogen, nitrogen, and oxygen) and pyrolysis conditions (operating temperature, heating rate residence time), while the decision attributes considered were yield, carbon content, and higher heating values. The rules generated were tested against a set of validation data and evaluated for their scientific coherency. Based on the decision rules generated, biomass with ash content of 11-14 wt%, volatile matter of 60-62 wt% and carbon content of 42-45.3 wt% can generate biochar with promising yield, carbon content and higher heating value via a pyrolysis process at an operating temperature of 425°C-475°C. This work provided the optimal biomass feedstock properties and pyrolysis conditions for biochar production with high mass and energy yield.

Aims: This study was conducted to develop a population pharmacokinetic (PK) model of methotrexate in Korean patients with haematologic malignancy, identify factors affecting methotrexate PK, and propose an optimal dosage regimen for the Korean population., Methods: Data were retrospectively collected from 188 patients with acute leukaemia or non-Hodgkin's lymphoma who were admitted to Severance Hospital during the period from November 2005 to January 2016. Using demographic factors and laboratory results as potential covariates for PK parameters, model development was performed using NONMEM and optimal dosing regimens were developed using the final PK model., Results: A two-compartment model incorporating body weight via allometry best described the data, yielding typical parameter values of 25.09 L for central volume of distribution ( V 1 ), 17.65 L for peripheral volume of distribution ( V 2 ), 12.89 L/h for clearance (CL) and 0.655 L/h for inter-compartmental clearance in a 50 kg patient. Covariate analyses showed that, at the weight of 50 kg, CL decreased by 0.11 L/h for each 1-year increase in age above 14 years old and decreased 0.8-fold when serum creatinine level doubled, indicating the importance of age-specific dose individualization in methotrexate treatment. Volume of distribution at steady state derived from PK parameters (=  V 1  +  V 2 ) was 0.85 L/kg, which was similar to those in the Western or Chinese populations. Optimal doses simulated from the final model successfully produced the PK measures close to the target chosen., Conclusions: The population PK model and optimal dosage regimens developed in this study can be used as a basis to achieve precision dosing in Korean patients with haematologic malignancy., (© 2023 British Pharmacological Society.)

Glaucoma, characterized by its damage to the retinal nerve, is one of the most statistically dominant eye diseases in the U.S. It can cause vision loss and blindness by affecting the optic nerve. As the disease progresses, it is not necessarily noticeable to patients, requiring elaborate solutions to manage this critical condition. In this paper, we propose a novel detection model that provides improved accuracy and performance in the detection of glaucoma using retinographies as input. After careful consideration, we adopted multiple features suitable for distinguishing healthy and diseased eyes. Moreover, the adoption, integration, and feature extraction of 3D meshes was a significant factor in developing our glaucoma detection system. With our acquired dataset, we compared the performance of Classification Decision Trees (CDT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) in classifying retinographies as being healthy or having glaucoma. Experimental results show that the proposed model methodology can efficiently predict glaucoma detection with 100%, 100%, and 83.3% accuracy from CDT, SVM, and KNN, respectively.

Competing Interests: The authors declare no competing interests.

Competing Interests: Conflict of InterestThe authors declare no competing interests.

The abbreviated injury score (AIS) is commonly used as a grading system for inhalation injuries. While inhalation injury grades have inconsistently been shown to correlate positively with the time mechanical ventilation is needed, grading is subjective and relies heavily on the clinicians' experience and expertise. Additionally, no correlation has been shown between these patients' inhalation injury grades and outcomes. In this paper, we propose a novel inhalation injury grading method which uses deep learning algorithms in bronchoscopy images to determine the injury grade from the carbonaceous deposits, blistering, and fibrin casts in the bronchoscopy images. The proposed method adopts transfer learning and data augmentation concepts to enhance the accuracy performance to avoid overfitting. We tested our proposed model on the bronchoscopy images acquired from eighteen patients who had suffered inhalation injuries, with the degree of severity 1, 2, 3, 4, 5, or 6. As performance metrics, we consider accuracy, sensitivity, specificity, F-1 score, and precision. Experimental results show that our proposed method, with both transfer learning and data augmentation components, provides an overall 86.11% accuracy. Moreover, the experimental results also show that the performance of the proposed method outperforms the method without transfer learning or data augmentation.

Competing Interests: Conflict of InterestThe authors declare no competing interests.

Global demand for plastic materials has severely harm the environment and marine sea life. Therefore, bioplastics have emerged as an environmentally friendly alternative due to sustainability, minimal carbon footprint, less toxicity and high degradability. This review highlights the sustainable and environmentally friendly approach towards bioplastic production by utilizing microalgae as a feed source in several ways. First, the microalgae biomass obtained through the biorefinery approach can be processed into PHA under certain nutrient limitations. Additionally, microalgae biomass can act as potential filler and reinforcement towards the enhancement of bioplastic either blending with conventional bioplastic or synthetic polymer. The downstream processing of microalgae via suitable extraction and pre-treatment of bioactive compounds such as lipids and cellulose are found to be promising for the production of bioplastics. Moving on, the intermediate processing of bioplastic via lactic acid synthesized from microalgae has favoured the microwave-assisted synthesis of polylactic acid due to cost efficiency, minimum solvent usage, low energy consumption, and fast rate of reaction. Moreover, the reliability and effectiveness of microalgae-based bioplastics are further evaluated in terms of techno-economic analysis and degradation mechanism. Future improvement and recommendations are listed towards proper genetic modification of algae strains, large-scale biofilm technology, low-cost cultivation medium, and novel avocado seed-microalgae bioplastic blend., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objective: Cataract, which is the clouding of the crystalline lens, is the most prevalent eye disease accounting for 51% of all eye diseases in the U.S. Cataract is a progressive disease, and its early detection is critical for preventing blindness. In this paper, an efficient approach to identify cataract disease by adopting luminance features using a smartphone is proposed., Methods: Initially, eye images captured by a smartphone were cropped to extract the lens, and the images were preprocessed to remove irrelevant background and noise by utilizing median filter and watershed transformation. Then, a novel luminance transformation from pixel brightness algorithm was introduced to extract lens image features. The luminance and texture features of different types of cataract disease images could be obtained accurately in this stage. Finally, by adopting support vector machines (SVM) as the classification method, cataract eyes were identified., Results: From all the images that we fed into our system, our method could diagnose diseased eyes with 96.6% accuracy, 93.4% specificity, and 93.75% sensitivity., Conclusion: The proposed method provides an affordable, rapid, easy-to-use, and versatile method for detecting cataracts by using smartphones without the use of bulky and expensive imaging devices. This methodcan be used for bedside telemedicine applications or in remote areas that have medical shortages. Previous smartphone-based cataract detection methods include texture feature analysis with 95 % accuracy, Gray Level Co-occurrence Matrix (GLCM) method with 89% accuracy, red reflex measurement method, and RGB color feature extraction method using cascade classifier with 90% accuracy. The accuracy of cataract detection in these studies is subject to changes in smartphone models and/or environmental conditions. However, our novel luminance-based method copes with different smartphone camera sensors and chroma variations, while operating independently from sensors' color characteristics and changes in distances and camera angle. Clinical and Translational Impact-This study is an early/pre-clinical research proposing a novel luminance-based method of detecting cataract using smartphones for remote/at-home monitoring and telemedicine application.

As a reliable indicator for individual's healthiness conditions, heart rate (HR) has been widely considered and used. Imaging photoplethysmography (iPPG) is recently highlighted as a promising HR measurement method, due to its non-contact characteristics, by extracting the HR from facial video recordings. In this study, we propose a camera-based HR monitoring technique that estimates HR information from iPPG signals extracted from a video sequence. Videos were recorded using a smartphone or a laptop camera. We adopted the plane-orthogonal-to-skin (POS) method to compute iPPG. The proposed method is evaluated by applying it to extract HR of 9 subjects at rest and during two motion conditions (lateral and frontal) while they were performing several respiratory maneuvers-spontaneous, metronome, and forced. Automatic face detection algorithms were implemented in the proposed method. Our experimental results show that mean values of HR have 0.56% error and 99.4% accuracy when compared to HR calculated from the gold-standard electrocardiography (ECG) reference in diverse conditions of motions and respiratory maneuvers.

Competing Interests: There are no conflicts of interest.

Lateral flow assay (LFA) technology has recently received interest in the biochemical field since it is simple, low-cost, and rapid, while conventional laboratory test procedures are complicated, expensive, and time-consuming. In this paper, we propose a robust smartphone-based analyte detection method that estimates the amount of analyte on an LFA strip using a smartphone camera. The proposed method can maintain high estimation accuracy under various illumination conditions without additional devices, unlike conventional methods. The robustness and simplicity of the proposed method are enabled by novel image processing and machine learning techniques. For the performance analysis, we applied the proposed method to LFA strips where the target analyte is albumin protein of human serum. We use two sets of training LFA strips and one set of testing LFA strips. Here, each set consists of five strips having different quantities of albumin-10 femtograms, 100 femtograms, 1 picogram, 10 picograms, and 100 picograms. A linear regression analysis approximates the analyte quantity, and then machine learning classifier, support vector machine (SVM), which is trained by the regression results, classifies the analyte quantity on the LFA strip in an optimal way. Experimental results show that the proposed smartphone application can detect the quantity of albumin protein on a test LFA set with 98% accuracy, on average, in real time.

In this paper, we propose a novel strep throat detection method using a smartphone with an add-on gadget. Our smartphone-based strep throat detection method is based on the use of camera and flashlight embedded in a smartphone. The proposed algorithm acquires throat image using a smartphone with a gadget, processes the acquired images using color transformation and color correction algorithms, and finally classifies streptococcal pharyngitis (or strep) throat from healthy throat using machine learning techniques. Our developed gadget was designed to minimize the reflection of light entering the camera sensor. The scope of this paper is confined to binary classification between strep and healthy throats. Specifically, we adopted k -fold validation technique for classification, which finds the best decision boundary from training and validation sets and applies the acquired best decision boundary to the test sets. Experimental results show that our proposed detection method detects strep throats with 93.75% accuracy, 88% specificity, and 87.5% sensitivity on average.

Photoplethysmography (PPG) is a commonly used in determining heart rate and oxygen saturation (SpO 2 ). However, PPG measurements and its accuracy are heavily affected by the measurement procedure and environmental factors such as light, temperature, and medium. In this paper, we analyzed the effects of different mediums (water vs. air) and temperature on the PPG signal quality and heart rate estimation. To evaluate the accuracy, we compared our measurement output with a gold-standard PPG device (NeXus-10 MKII). The experimental results show that the average PPG signal amplitude values of the underwater environment decreased considerably (22% decrease) compared to PPG signals of dry environments, and the heart rate measurement deviated 7% (5 beats per minute on average. The experimental results also show that the signal to noise ratio (SNR) and signal amplitude decrease as temperature decreases. Paired t -test which compares amplitude and heart rate values between the underwater and dry environments was performed and the test results show statistically significant differences for both amplitude and heart rate values ( p < 0.05). Moreover, experimental results indicate that decreasing the temperature from 45 °C to 5 °C or changing the medium from air to water decreases PPG signal quality, (e.g., PPG signal amplitude decreases from 0.560 to 0.112). The heart rate is estimated within 5.06 bpm deviation at 18 °C in underwater environment, while estimation accuracy decreases as temperature goes down.

The advent of smartphones has advanced the use of embedded sensors to acquire various physiological information. For example, smartphone camera sensors and accelerometers can provide heart rhythm signals to the subjects, while microphones can give respiratory signals. However, the acquired smartphone-based physiological signals are more vulnerable to motion and noise artifacts (MNAs) compared to using medical devices, since subjects need to hold the smartphone with proper contact to the smartphone camera and lens stably and tightly for a duration of time without any movement in the hand or finger. This results in more MNA than traditional methods, such as placing a finger inside a tightly enclosed pulse oximeter to get PPG signals, which provides stable contact between the sensor and the subject's finger. Moreover, a smartphone lens does not block ambient light in an effective way, while pulse oximeters are designed to block the ambient light effectively. In this paper, we propose a novel diversity method for smartphone signals that reduces the effect of MNAs during heart rhythm signal detection by 1) acquiring two heterogeneous signals from a color intensity signal and a fingertip movement signal, and 2) selecting the less MNA-corrupted signal of the two signals. The proposed method has advantages in that 1) diversity gain can be obtained from the two heterogeneous signals when one signal is clean while the other signal is corrupted, and 2) acquisition of the two heterogeneous signals does not double the acquisition procedure but maintains a single acquisition procedure, since two heterogeneous signals can be obtained from a single smartphone camera recording. In our diversity method, we propose to choose the better signal based on the signal quality indices (SQIs), i.e., standard deviation of instantaneous heart rate (STD-HR), root mean square of the successive differences of peak-to-peak time intervals (RMSSD-T), and standard deviation of peak values (STD-PV). As a performance metric evaluating the proposed diversity method, the ratio of usable period is considered. Experimental results show that our diversity method increases the usable period 19.53% and 6.25% compared to the color intensity or the fingertip movement signals only, respectively., Competing Interests: The authors have declared that no competing interests exist.

In this work, a 24-GHz frequency-modulated continuous-wave (FMCW) radar system with two sectoral horn antennas and one transmitting lens antenna for long-range drone detection is presented. The present work demonstrates the detection of a quadcopter-type drone using the implemented radar system up to a distance of 1 km. Moreover, a 3D subspace-based algorithm is proposed for the joint range-azimuth-Doppler estimation of long-range drone detection. The effectiveness of the long-range drone detection is verified with the implemented radar system through a variety of experiments in outdoor environments. This is the first such demonstration for long-range drone detection with a 24-GHz FMCW radar.

We have recently found that our previously-developed atrial fibrillation (AF) detection algorithm for smartphones can give false positives when subjects' fingers or hands move, as we rely on proper finger placement over the smartphone camera to collect the signal of interest. Specifically, smartphone camera pulsatile signals that are obtained from normal sinus rhythm (NSR) subjects but are corrupted by motion and noise artifacts (MNAs) are frequently detected as AF. AF and motion-corrupted episodes have the similar characteristic that pulse-to-pulse intervals (PPIs) are irregular. We have developed an MNA-resilient smartphone-based AF detection algorithm that first discriminates and eliminates MNA-corrupted episodes in smartphone camera recordings, and then detects AF in MNA-free recordings. We found that MNA-corrupted episodes have highly-varying pulse slope, large turning point ratio, or large kurtosis values in smartphone signals compared to MNA-free AF and NSR episodes. We first use these three metrics for MNA discrimination and exclusion. Then, AF is detected in MNA-free signals using our previous algorithm. The capability to discriminate MNAs and AFs separately in smartphone signals increases the specificity of AF detection. To evaluate the performance of the proposed MNA-resilient AF algorithm, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion as well as 11 participants with MNA-corrupted NSR, were recruited. Using iPhone 4S, 5S, and 6S models, we collected 2-minute pulsatile time series from each subject. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.97, 0.98, 0.97, respectively, which are higher than those of the previous AF algorithm.

In this paper, a modified 3D multiple signal classification (MUSIC) algorithm is proposed for joint estimation of range, azimuth, and elevation angles of K-band radar with a small 2 × 2 horn antenna array. Three channels of the 2 × 2 horn antenna array are utilized as receiving channels, and the other one is a transmitting antenna. The proposed modified 3D MUSIC is designed to make use of a stacked autocorrelation matrix, whose element matrices are related to each other in the spatial domain. An augmented 2D steering vector based on the stacked autocorrelation matrix is proposed for the modified 3D MUSIC, instead of the conventional 3D steering vector. The effectiveness of the proposed modified 3D MUSIC is verified through implementation with a K-band frequency-modulated continuous-wave (FMCW) radar with the 2 × 2 horn antenna array through a variety of experiments in a chamber., Competing Interests: The authors declare no conflict of interest.

This article deals with the development of a dual channel S-Band frequency-modulated continuous wave (FMCW) system for a through-the-wall imaging (TWRI) system. Most existing TWRI systems using FMCW were developed for synthetic aperture radar (SAR) which has many drawbacks such as the need for several antenna elements and movement of the system. Our implemented TWRI system comprises a transmitting antenna and two receiving antennas, resulting in a significant reduction of the number of antenna elements. Moreover, a proposed algorithm for range-angle-Doppler 3D estimation based on a 3D shift invariant structure is utilized in our implemented dual channel S-band FMCW TWRI system. Indoor and outdoor experiments were conducted to image the scene beyond a wall for water targets and person targets, respectively. The experimental results demonstrate that high-quality imaging can be achieved under both experimental scenarios., Competing Interests: Authors declare no conflict of interest.

Photoplethysmography (PPG) is a technique to detect blood volume changes in an optical way. Representative PPG applications are the measurements of oxygen saturation, heart rate, and respiratory rate. However, PPG signals are sensitive to motion and noise artifacts (MNAs) especially when they are obtained from smartphone cameras. Moreover, PPG signals are different among users and each individual's PPG signal has a unique characteristic. Hence, an effective MNA detection and reduction method for smartphone PPG signals, which adapts itself to each user in a personalized way, is highly demanded. Here, a concept of the probabilistic neural network (PNN) is introduced to be used with the proposed extracted parameters. The signal amplitude, standard deviation of peak to peak time intervals and amplitudes, along with the mean of moving standard deviation, signal slope changes, and the optimal autoregressive (AR) model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As performance metrics, we considered accuracy, sensitivity, and specificity. The results show that the overall performance of the personalized MNA detection is enhanced compared to the generalized algorithm. The average values of the accuracy, sensitivity and specificity of the personalized one are 98.07%, 92.6%, and 99.78%, respectively, while these are 89.92%, 84.21%, and 93.63% for the general one.

This correspondence is to address the corrections to the published paper titled "An efficient adaptive binary arithmetic coder based on logarithmic domain".

Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study, we present a novel approach, "TifMA," based on using the time-frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data. The term "nonusable" refers to segments of PPG data from which the HR signal cannot be recovered accurately. Two sequential classification procedures were included in the TifMA algorithm. The first classifier distinguishes between MNA-corrupted and MNA-free PPG data. Once a segment of data is deemed MNA-corrupted, the next classifier determines whether the HR can be recovered from the corrupted segment or not. A support vector machine (SVM) classifier was used to build a decision boundary for the first classification task using data segments from a training dataset. Features from time-frequency spectra of PPG were extracted to build the detection model. Five datasets were considered for evaluating TifMA performance: (1) and (2) were laboratory-controlled PPG recordings from forehead and finger pulse oximeter sensors with subjects making random movements, (3) and (4) were actual patient PPG recordings from UMass Memorial Medical Center with random free movements and (5) was a laboratory-controlled PPG recording dataset measured at the forehead while the subjects ran on a treadmill. The first dataset was used to analyze the noise sensitivity of the algorithm. Datasets 2-4 were used to evaluate the MNA detection phase of the algorithm. The results from the first phase of the algorithm (MNA detection) were compared to results from three existing MNA detection algorithms: the Hjorth, kurtosis-Shannon entropy, and time-domain variability-SVM approaches. This last is an approach recently developed in our laboratory. The proposed TifMA algorithm consistently provided higher detection rates than the other three methods, with accuracies greater than 95% for all data. Moreover, our algorithm was able to pinpoint the start and end times of the MNA with an error of less than 1 s in duration, whereas the next-best algorithm had a detection error of more than 2.2 s. The final, most challenging, dataset was collected to verify the performance of the algorithm in discriminating between corrupted data that were usable for accurate HR estimations and data that were nonusable. It was found that on average 48% of the data segments were found to have MNA, and of these, 38% could be used to provide reliable HR estimation.