Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices.

Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm's low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices.

Optical Potential-Well Array for High-Selectivity, Massive Trapping and Sorting at Nanoscale.

Optical tweezers are versatile tools capable of sorting microparticles, yet formidable challenges are present in the separation of nanoparticles smaller than 200 nm. The difficulties arise from the controversy on the requirement of a tightly focused light spot in order to create strong optical forces while a large area is kept for the sorting. To overcome this problem, we create a near-field potential well array with connected tiny hotspots in a large scale. This situation can sort nanoparticles with sizes from 100 to 500 nm, based on the differentiated energy depths of each potential well. In this way, nanoparticles of 200, 300, and 500 nm can be selectively trapped in this microchannel by appropriately tuning the laser power. Our approach provides a robust and unprecedented recipe for optical trapping and separation of nanoparticles and biomolecules, such that it presents a huge potential in the physical and biomedical sciences.

Regulation of lipid droplets in live preadipocytes using optical diffraction tomography and Raman spectroscopy.

Lipid droplets have gained strong interest in recent years to comprehend how they function and coordinate with other parts of the cell. However, it remains challenging to study the regulation of lipid droplets in live preadipocytes using conventional microscopic techniques. In this paper, we study the effects of fatty acid stimulation and cell starvation on lipid droplets using optical diffraction tomography and Raman spectroscopy by measuring size, refractive index, volume, dry mass and degree of unsaturation. The increase of fatty acids causes an increase in the number and dry mass of lipid droplets. During starvation, the number of lipid droplets increases drastically, which are released to mitochondria to release energy. Studying lipid droplets under different chemical stimulations could help us understand the regulation of lipid droplets for metabolic disorders, such as obesity and diabetes.

Automatic System for Obstructive Sleep Apnea Events Detection Using Convolutional Neural Network.

Obstructive Sleep Apnea (OSA) is characterized by repetitive episodes of airflow reduction (hypopnea) or cessation (apnea), which, as a prevalent sleep disorder, can cause people to stop breathing for 10 to 30 seconds at a time and lead to serious problems such as daytime fatigue, impaired memory, and depression. This work intends to explore automatic detection of OSA events with 1-second annotation based on blood oxygen saturation, oronasal airflow, and ribcage and abdomen movements. Deep Learning (DL) technology, specifically, Convolutional Neural Network (CNN), is employed as a feature detector to learn the characteristics of the highorder correlation among visible data and corresponding labels. A fully-connected layer in the last stage of the CNN is connected to the output layer and constructs the desired number of outputs for sleep apnea events classification. A leave-one-out cross-validation has been conducted on the PhysioNet Sleep Database provided by St. Vincents University Hospital and University College Dublin, and an average accuracy of $79 .61$% across normal, hypopnea, and apnea, classes is achieved.

Obstructive Sleep Apnea Detection Using Difference in Feature and Modified Minimum Distance Classifier.

The current gold standard of Obstructive Sleep Apnea (OSA) diagnosis involves the use of a Polysomnography (PSG) system which requires the patient to stay in the hospital for overnight recording. The process is uncomfortable for the patient and it disturbs the patient's sleep pattern. On the other hand, it is well known that some acoustic features of the snoring sounds are good indicators of the presence of OSA, and a variety of acoustic OSA detection algorithms have been reported in the literature. Typically, these algorithms use multiple features and a relatively complex classifier, which are not ideal for handling the huge over-night data. In this paper, we propose an algorithm that uses a single feature and a relatively simple classifier. The proposed feature is the difference between two carefully selected Mel-frequency cepstral coefficients (MFCCs) of the snoring sound samples. The proposed classifier is derived based on a modified minimum distance criterion. The proposed algorithm has been tested with patient data. The results show that the proposed algorithm outperforms existing algorithms and is able to achieve up to 97.1% detection accuracy.

An mRMR-SVM Approach for Opto-Fluidic Microorganism Classification.

The detection of microorganisms is important in numerous applications such as water quality monitoring, blood analysis, and food testing. The conventional detection methods are tedious and labour-intensive. Establish methods involve culturing, counting and identification of the pathogen by an experienced technician which typically can take several days. The use of opto-fluidic technology to capture microorganism images offers 0 route to reduce the overall assay time. However, the detection still requires a trained technician. This paper proposes an image processing method that can be used to classify microorganism images captured by an opto-fluidic set up in an automatic manner. The proposed algorithm incorporates some of the features used in other microorganism image detection methods and proposes two new features-Entropy of Histogram of Oriented Gradients (HOG) and the filtered intensities. In addition, we propose to apply the minimal-Redundancy-Maximal-Relevance (mRMR) criterion to select and rank these features. The probability and joint probability distribution functions of the mRMR are estimated using a Gaussian model and the Kernel Density Estimation model. The performance of the proposed method was validated using SVM and data collected from an experimental setup. The results show that our proposed method outperforms existing methods and is capable of achieving a classification accuracy up to 95.8%.

Classification of non-tumorous skin pigmentation disorders using voting based probabilistic linear discriminant analysis.

Non-tumorous skin pigmentation disorders can have a huge negative emotional impact on patients. The correct diagnosis of these disorders is essential for proper treatments to be instituted. In this paper, we present a computerized method for classifying five non-tumorous skin pigmentation disorders (i.e., freckles, lentigines, Hori's nevus, melasma and nevus of Ota) based on probabilistic linear discriminant analysis (PLDA). To address the large within-class variance problem with pigmentation images, a voting based PLDA (V-PLDA) approach is proposed. The proposed V-PLDA method is tested on a dataset that contains 150 real-world images taken from patients. It is shown that the proposed V-PLDA method obtains significantly higher classification accuracy (4% or more with p< 0.001 in the analysis of variance (ANOVA) test) than the original PLDA method, as well as several state-of-the-art image classification methods. To the authors' best knowledge, this is the first study that focuses on the non-tumorous skin pigmentation image classification problem. Therefore, this paper could provide a benchmark for subsequent research on this topic. Additionally, the proposed V-PLDA method demonstrates promising performance in clinical applications related to skin pigmentation disorders.

Nanometer-precision linear sorting with synchronized optofluidic dual barriers.

The past two decades have witnessed the revolutionary development of optical trapping of nanoparticles, most of which deal with trapping stiffness larger than 10-8 N/m. In this conventional regime, however, it remains a formidable challenge to sort out sub-50-nm nanoparticles with single-nanometer precision, isolating us from a rich flatland with advanced applications of micromanipulation. With an insightfully established roadmap of damping, the synchronization between optical force and flow drag force can be coordinated to attempt the loosely overdamped realm (stiffness, 10-10 to 10-8 N/m), which has been challenging. This paper intuitively demonstrates the remarkable functionality to sort out single gold nanoparticles with radii ranging from 30 to 50 nm, as well as 100- and 150-nm polystyrene nanoparticles, with single nanometer precision. The quasi-Bessel optical profile and the loosely overdamped potential wells in the microchannel enable those aforementioned nanoparticles to be separated, positioned, and microscopically oscillated. This work reveals an unprecedentedly meaningful damping scenario that enriches our fundamental understanding of particle kinetics in intriguing optical systems, and offers new opportunities for tumor targeting, intracellular imaging, and sorting small particles such as viruses and DNA.

Feature extraction techniques for low-power ambulatory wheeze detection wearables.

Presence of wheezes in breathing sounds has been associated with several respiratory and pulmonary diseases. In this paper we present a novel low-complexity wheeze detection method based on frequency contour tracking for automatic wheeze detection. Two hardware friendly variants of the algorithm have also been proposed. Applying the proposed feature extraction algorithm we achieved very high classification accuracy (> 99%) at considerably low computational complexity (3×-6×) compared to earlier methods and the power consumption of the proposed method is shown to be significantly less (70×-100×) compared to `record and transmit' strategy in wearable devices.

3D characterization and trend analysis of scents from EEG recordings of repeated scent exposure.

Scent plays an important role in influencing the brain and has been commonly used in psychological research. Much of such research has been conducted without the use of electroencephalography (EEG) to measure the response of the human brain to scent stimulus. Recent studies have involved the use of EEG to perform comparative studies on how different scents can affect brain activity. However, little has been done to analyze the trend of brain activity when a subject is repeatedly exposed to the same scent. This paper discusses the use of 4 features - Entropy Difference, Entropy Ratio, Entropy Time and Root Mean Square (RMS) to perform trend analysis of EEG signals in a repeated scent-exposure setting. The results show that different types of scents cause the brain to be stimulated at different degrees for each repeated exposure, giving rise to different trend patterns. It is also observed that the 4 features give similar trends for the same scent. This similarity allows us to combine the 4 features by forming a feature vector and plotting them in 3 dimensional (3D) space, using 3 repeated scent exposures as the axes. The region of space where the feature vector lies is represented by an ellipsoid, which can be used to characterize a particular scent. Unlike previous work, which did not characterize scent from EEG recordings, this paper investigates the different trends of scent after its repeated exposure to the human subject and by using the 3D representation to characterize the scent.

Discrete wavelet transform coefficients for emotion recognition from EEG signals.

In this paper, we propose to use DWT coefficients as features for emotion recognition from EEG signals. Previous feature extraction methods used power spectra density values dervied from Fourier Transform or sub-band energy and entropy derived from Wavelet Transform. These feature extracion methods eliminate temporal information which are essential for analyzing EEG signals. The DWT coefficients represent the degree of correlation between the analyzed signal and the wavelet function at different instances of time; therefore, DWT coefficients contain temporal information of the analyzed signal. The proposed feature extraction method fully utilizes the simultaneous time-frequency analysis of DWT by preserving the temporal information in the DWT coefficients. In this paper, we also study the effects of using different wavelet functions (Coiflets, Daubechies and Symlets) on the performance of the emotion recognition system. The input EEG signals were obtained from two electrodes according to 10-20 system: F(p1) and F(p2). Visual stimuli from International Affective Picture System (IAPS) were used to induce two emotions: happy and sad. Two classifiers were used: Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Experimental results confirmed that the proposed DWT coefficients method showed improvement of performance compared to previous methods.

Lung water detection using acoustic techniques.

The presence of an excessive amount of water in lung is a sign of pulmonary edema which can be caused by heart failure. The current solutions for lung water detection involve the use of X-ray, CT scan or serum biomarkers, which require bulky and expensive instruments as well as long measurement duration. This paper reports on a study conducted on the use of a different sensing modality to detect the presence of water in lung. The main contributions of the paper are twofold: 1) we propose to employ acoustic (or sound) based techniques for lung water detection. The design is simple and can be implemented on a portable or wearable system; 2) we establish the feasibility of sound-based techniques for lung water detection, by carrying out experimental studies using four feature extraction methods combined with two classification methods. The findings of this study will be beneficial to the design of portable devices for rapid and accurate lung water detection.

An intelligent scoring system and its application to cardiac arrest prediction.

Traditional risk score prediction is based on vital signs and clinical assessment. In this paper, we present an intelligent scoring system for the prediction of cardiac arrest within 72 h. The patient population is represented by a set of feature vectors, from which risk scores are derived based on geometric distance calculation and support vector machine. Each feature vector is a combination of heart rate variability (HRV) parameters and vital signs. Performance evaluation is conducted on the leave-one-out cross-validation framework, and receiver operating characteristic, sensitivity, specificity, positive predictive value, and negative predictive value are reported. Experimental results reveal that the proposed scoring system not only achieves satisfactory performance on determining the risk of cardiac arrest within 72 h but also has the ability to generate continuous risk scores rather than a simple binary decision by a traditional classifier. Furthermore, the proposed scoring system works well for both balanced and imbalanced datasets, and the combination of HRV parameters and vital signs shows superiority in prediction to using HRV parameters only or vital signs only.

A novel respiratory rate estimation method for sound-based wearable monitoring systems.

The respiratory rate is a vital sign that can provide important information about the health of a patient, especially that of the respiratory system. The aim of this study is to develop a simple method that can be applied in wearable systems to monitor the respiratory rate automatically and continuously over extended periods of time. In this paper, a novel respiratory rate estimation method is presented to achieve this target. The proposed method has been evaluated in both the open-source data as well as the local-hospital data, and the results are encouraging. The findings of this study revealed strong linear correlation to the reference respiratory rate. The correlation coefficients for the open-source data and the in-hospital data are 0.99 and 0.96 respectively. The standard deviation of the estimation error is less than 7% for both types of data.

Predictive fine granularity successive elimination for fast optimal block-matching motion estimation.

Given the number of checking points, the speed of block motion estimation depends on how fast the block matching is. In this paper, a new framework, fine granularity successive elimination (FGSE), is proposed for fast optimal block matching in motion estimation. The FGSE features providing a sequence of nondecreasing fine-grained boundary levels to reject a checking point using as little computation as possible, where block complexity is utilized to determine the order of partitioning larger sub-blocks into smaller subblocks in the creation of the fine-grained boundary levels. It is shown that the well-known successive elimination algorithm (SEA) and multilevel successive elimination algorithm (MSEA) are just two special cases in the FGSE framework. Moreover, in view that two adjacent checking points (blocks) share most of the block pixels with just one pixel shifting horizontally or vertically, we develop a scheme to predict the rejection level for a candidate by exploiting the correlation of matching errors between two adjacent checking points. The resulting predictive FGSE algorithm can further reduce computation load by skipping some redundant boundary levels. Experimental results are presented to verify substantial computational savings of the proposed algorithm in comparison with the SEA/MSEA.