Wearable telemonitoring of electrocardiogram (ECG) based on wireless body Area networks (WBAN) is a promising approach in next-generation patient-centric telecardiology solutions. In order to guarantee long-term effective operation of monitoring systems, the power consumption of the sensors must be strictly limited. Compressed sensing (CS) is an effective method to alleviate this problem. However, ECG signals in WBAN are usually non-sparse, and most traditional compressed sensing recovery algorithms have difficulty recovering non-sparse signals. In this paper, we proposed a fast and robust non-sparse signal recovery algorithm for wearable ECG telemonitoring. In the proposed algorithm, the alternating direction method of multipliers (ADMM) is used to accelerate the speed of block sparse Bayesian learning (BSBL) framework. We used the famous MIT-BIH Arrhythmia Database, MIT-BIH Long-Term ECG Database and ECG datasets collected in our practical wearable ECG telemonitoring system to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm can directly recover ECG signals with a satisfactory accuracy in a time domain without a dictionary matrix. Due to acceleration by ADMM, the proposed algorithm has a fast speed, and also it is robust for different ECG datasets. These results suggest that the proposed algorithm is very promising for wearable ECG telemonitoring. Full article

In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only $20\%$ of the labelled data and also improve the prediction accuracy even under the noisy condition. Full article

Despite the wide literature on R-wave detection algorithms for ECG Holter recordings, the long-term monitoring applications are bringing new requirements, and it is not clear that the existing methods can be straightforwardly used in those scenarios. Our aim in this work was twofold: First, we scrutinized the scope and limitations of existing methods for Holter monitoring when moving to long-term monitoring; Second, we proposed and benchmarked a beat detection method with adequate accuracy and usefulness in long-term scenarios. A longitudinal study was made with the most widely used waveform analysis algorithms, which allowed us to tune the free parameters of the required blocks, and a transversal study analyzed how these parameters change when moving to different databases. With all the above, the extension to long-term monitoring in a database of 7-day Holter monitoring was proposed and analyzed, by using an optimized simultaneous-multilead processing. We considered both own and public databases. In this new scenario, the noise-avoid mechanisms are more important due to the amount of noise that exists in these recordings, moreover, the computational efficiency is a key parameter in order to export the algorithm to the clinical practice. The method based on a Polling function outperformed the others in terms of accuracy and computational efficiency, yielding 99.48% sensitivity, 99.54% specificity, 99.69% positive predictive value, 99.46% accuracy, and 0.85% error for MIT-BIH arrhythmia database. We conclude that the method can be used in long-term Holter monitoring systems. Full article

The neurological disorder epilepsy causes substantial problems to the patients with uncontrolled seizures or even sudden deaths. Accurate detection and prediction of epileptic seizures will significantly improve the life quality of epileptic patients. Various feature extraction algorithms were proposed to describe the EEG signals in frequency or time domains. Both invasive intracranial and non-invasive scalp EEG signals have been screened for the epileptic seizure patterns. This study extracted a comprehensive list of 24 feature types from the scalp EEG signals and found 170 out of the 2794 features for an accurate classification of epileptic seizures. An accuracy (Acc) of 99.40% was optimized for detecting epileptic seizures from the scalp EEG signals. A balanced accuracy (bAcc) was calculated as the average of sensitivity and specificity and our seizure detection model achieved 99.61% in bAcc. The same experimental procedure was applied to predict epileptic seizures in advance, and the model achieved Acc = 99.17% for predicting epileptic seizures 10 s before happening. Full article

Commercially available electrodes can only provide quality surface electromyography (sEMG) measurements for a limited duration due to user discomfort and signal degradation, but in many applications, collecting sEMG data for a full day or longer is desirable to enhance clinical care. Few studies for long-term sEMG have assessed signal quality of electrodes using clinically relevant tests. The goal of this research was to evaluate flexible, gold-based epidermal sensor system (ESS) electrodes for long-term sEMG recordings. We collected sEMG and impedance data from eight subjects from ESS and standard clinical electrodes on upper extremity muscles during maximum voluntary isometric contraction tests, dynamic range of motion tests, the Jebsen Taylor Hand Function Test, and the Box & Block Test. Four additional subjects were recruited to test the stability of ESS signals over four days. Signals from the ESS and traditional electrodes were strongly correlated across tasks. Measures of signal quality, such as signal-to-noise ratio and signal-to-motion ratio, were also similar for both electrodes. Over the four-day trial, no significant decrease in signal quality was observed in the ESS electrodes, suggesting that thin, flexible electrodes may provide a robust tool that does not inhibit movement or irritate the skin for long-term measurements of muscle activity in rehabilitation and other applications. Full article

Detection of cholesterol crystals (Chcs) in atherosclerosis disease is important for understanding the pathophysiology of atherosclerosis. Polarization microscopy (PM) has been in use traditionally for detecting Chcs, but they have difficulty in distinguishing Chcs with other crystalline materials in tissue, such as collagens. Thus, most studies using PM have been limited to studying cell-level samples. Although various methods have been proposed to detect Chcs with high specificity, most of them have low signal-to-noise ratios, a high system construction cost, and are difficult to operate due to a complex protocol. To address these problems, we have developed a simple and inexpensive universal serial bus (USB) PM system equipped with a 5700 K cool-white light-emitting diode (LED). In this system, Chcs are shown in a light blue color while collagen is shown in a yellow color. More importantly, the contrast between Chcs and collagens is improved by a factor of 2.3 under an aqueous condition in these PM images. These imaging results are well-matched with the ones acquired with two-photon microscopy (TPM). The system can visualize the features of atherosclerosis that cannot be visualized by the conventional hematoxylin and eosin and oil-red-o staining methods. Thus, we believe that this simple USB PM system can be widely used to identify Chcs in atherosclerosis. Full article

The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the accuracy achievable with such technology is highly reliant on separating the useful from irrelevant sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as recording unexpected user behaviors. These behaviors often violate the assumptions of clinimetric testing protocols, where these protocols are designed to probe for specific symptoms. Such violations are frequent outside the lab and affect the accuracy of the subsequent data analysis and scientific conclusions. To address these problems, we report on a unified algorithmic framework for automated sensor data quality control, which can identify those parts of the sensor data that are sufficiently reliable for further analysis. Combining both parametric and nonparametric signal processing and machine learning techniques, we demonstrate that across 100 subjects and 300 clinimetric tests from three different types of behavioral clinimetric protocols, the system shows an average segmentation accuracy of around 90%. By extracting reliable sensor data, it is possible to strip the data of confounding factors in the environment that may threaten reproducibility and replicability. Full article

Body temperature and bio-signals are important health indicators that reflect the human health condition. However, monitoring these indexes is inconvenient and time-consuming, requires various instruments, and needs professional skill. In this study, a composite microneedle array electrode (CMAE) was designed and fabricated. It simultaneously detects body temperature and bio-signals. The CMAE consists of a 6 × 6 microneedles array with a height of 500 μm and a base diameter of 200 μm. Multiple insertion experiments indicate that the CMAE possesses excellent mechanical properties. The CMAE can pierce porcine skin 100 times without breaking or bending. A linear calibration relationship between temperature and voltage are experimentally obtained. Armpit temperature (35.8 °C) and forearm temperature (35.3 °C) are detected with the CMAE, and the measurements agree well with the data acquired with a clinical thermometer. Bio-signals including EII, ECG, and EMG are recorded and compared with those obtained by a commercial Ag/AgCl electrode. The CMAE continuously monitors bio-signals and is more convenient to apply because it does not require skin preparation and gel usage. The CMAE exhibits good potential for continuous and repetitive monitoring of body temperature and bio-signals. Full article

Heart rate variability (HRV) is commonly used to assess autonomic functions and responses to environmental stimuli. It is usually derived from electrocardiographic signals; however, in the last few years, photoplethysmography has been successfully used to evaluate beat-to-beat time intervals and to assess changes in the human heart rate under several conditions. The present work describes a simple design of a photoplethysmograph, using a wearable earlobe sensor. Beat-to-beat time intervals were evaluated as the time between subsequent pulses, thus generating a signal representative of heart rate variability, which was compared to RR intervals from classic electrocardiography. Twenty-minute pulse photoplethysmography and ECG recordings were taken simultaneously from 10 healthy individuals. Ten additional subjects were recorded for 24 h. Comparisons were made of raw signals and on time-domain and frequency-domain HRV parameters. There were small differences between the inter-beat intervals evaluated with the two techniques. The current findings suggest that our wearable earlobe pulse photoplethysmograph may be suitable for short and long-term home measuring and monitoring of HRV parameters. Full article

Surface electromyography (sEMG) signals are commonly used in activity monitoring and rehabilitation applications as they reflect effectively the motor intentions of users. However, real-time sEMG signals are non-stationary and vary to a large extent within the time frame of signals. Although previous studies have focused on the issues, their results have not been satisfactory. Therefore, we present a new method of conducting feature-level fusion to obtain a new feature space for sEMG signals. Eight activities of daily life (ADLs), including falls, were performed to obtain raw data from EMG signals from the lower limb. A feature set combining the time domain, time–frequency domain, and entropy domain was applied to the raw data to establish an initial feature space. A new projection method, the weighting genetic algorithm for GCCA (WGA-GCCA), was introduced to obtain the final feature space. Different tests were carried out to evaluate the performance of the new feature space. The new feature space created with the WGA-GCCA effectively reduced the dimensions and selected the best feature vectors dynamically while improving monotonicity. The Davies–Bouldin index (DBI) based on fuzzy c-means algorithms of the space obtained the lowest value compared with several fusion methods. It also achieved the highest accuracy when applied to support vector machine classifier. Full article

Recurrent joint bleeds and silent bleeds are the most common clinical feature in patients with hemophilia. Every bleed causes an immediate inflammatory response and is the leading cause of chronic crippling arthropathy. With the help of infrared thermography we wanted to detect early differences between a group of clinical non-symptomatic children with hemophilia (CWH) with no history of clinically detected joint bleeds and a healthy age-matched group of children. This could help to discover early inflammation and help implement early treatment and preventative strategies. It could be demonstrated that infrared thermography is sensitive enough to detect more signs of early inflammatory response in the CWH than in healthy children. It seems to detect more side differences in temperature than clinical examination of silent symptoms detects tender points. Silent symptoms/tender points seem to be combined with early local inflammation. Using such a non-invasive and sensor-based early detection, prevention of overloading and bleeding might be achieved. Full article

Faradaic electrochemical impedance spectroscopy (f-EIS) in the presence of redox reagent, e.g., [Fe(CN)₆]^3−/4−, is widely used in biosensors owing to its high sensitivity. However, in sensors detecting amyloid beta (Aβ), the redox reagent can cause the aggregation of Aβ, which is a disturbance factor in accurate detection. Here, we propose an interdigitated microelectrode (IME) based f-EIS technique that can alleviate the aggregation of Aβ and achieve high sensitivity by buffer control. The proposed method was verified by analyzing three different EIS-based sensors: non-faradaic EIS (nf-EIS), f-EIS, and the proposed f-EIS with buffer control. We analyzed the equivalent circuits of nf-EIS and f-EIS sensors. The dominant factors of sensitivity were analyzed, and the impedance change rates via Aβ reaction was compared. We measured the sensitivity of the IME sensors based on nf-EIS, f-EIS, and the proposed f-EIS. The results demonstrate that the proposed EIS-based IME sensor can detect Aβ with a sensitivity of 7.40-fold and 10.93-fold higher than the nf-EIS and the f-EIS sensors, respectively. Full article

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG. Full article

We propose a motion-robust laser Doppler flowmetry (LDF) system that can be used as a non-contact blood perfusion sensor for medical diagnosis. Endoscopic LDF systems are typically limited in their usefulness in clinical contexts by the need for the natural organs to be immobilized, as serious motion artifacts due to the axial surface displacement can interfere with blood perfusion measurements. In our system, the focusing lens moves to track the motion of the target using a low-frequency reference signal in the optical data, enabling the suppression of these motion artifacts in the axial direction. This paper reports feasibility tests on a prototype of this system using a microfluidic phantom as a measurement target moving in the direction of the optical axis. The frequency spectra detected and the perfusion values calculated from those spectra show that the motion tracking system is capable of suppressing motion artifacts in perfusion readings. We compared the prototype LDF system’s measurements with and without motion feedback, and found that motion tracking improves the fidelity of the perfusion signal by as much as 87%. Full article

Veterinarians and pet owners have limited ability to assess pruritic behaviors in dogs. This pilot study assessed the capacity of the Vetrax^® triaxial accelerometer to measure these behaviors in six dogs with pruritus likely due to environmental allergens. Dogs wore the activity monitor for two weeks while consuming their usual pet food (baseline), then for eight weeks while consuming a veterinary-exclusive pet food for dogs with suspected non-food-related skin conditions (Hill’s Prescription Diet^® Derm Defense^TM Canine dry food). Veterinarians and owners completed questionnaires during baseline, phase 1 (days 1–28) and phase 2 (days 29–56) without knowledge of the activity data. Continuous 3-axis accelerometer data was processed using proprietary behavior recognition algorithms and analyzed using general linear mixed models with false discovery rate-adjusted p values. Veterinarian-assessed overall clinical signs of pruritus were significantly predicted by scratching (β 0.176, p = 0.008), head shaking (β 0.197, p < 0.001) and sleep quality (β −0.154, p < 0.001), while owner-assessed quality of life was significantly predicted by scratching (β −0.103, p = 0.013) and head shaking (β −0.146, p < 0.001). Among dogs exhibiting pruritus signs eating the veterinary-exclusive food, the Vetrax^® sensor provided an objective assessment of clinically relevant pruritic behaviors that agreed with owner and veterinarian reports. Full article

Robust gait segmentation is the basis for mobile gait analysis. A range of methods have been applied and evaluated for gait segmentation of healthy and pathological gait bouts. However, a unified evaluation of gait segmentation methods in Parkinson’s disease (PD) is missing. In this paper, we compare four prevalent gait segmentation methods in order to reveal their strengths and drawbacks in gait processing. We considered peak detection from event-based methods, two variations of dynamic time warping from template matching methods, and hierarchical hidden Markov models (hHMMs) from machine learning methods. To evaluate the methods, we included two supervised and instrumented gait tests that are widely used in the examination of Parkinsonian gait. In the first experiment, a sequence of strides from instructed straight walks was measured from 10 PD patients. In the second experiment, a more heterogeneous assessment paradigm was used from an additional 34 PD patients, including straight walks and turning strides as well as non-stride movements. The goal of the latter experiment was to evaluate the methods in challenging situations including turning strides and non-stride movements. Results showed no significant difference between the methods for the first scenario, in which all methods achieved an almost 100% accuracy in terms of F-score. Hence, we concluded that in the case of a predefined and homogeneous sequence of strides, all methods can be applied equally. However, in the second experiment the difference between methods became evident, with the hHMM obtaining a 96% F-score and significantly outperforming the other methods. The hHMM also proved promising in distinguishing between strides and non-stride movements, which is critical for clinical gait analysis. Our results indicate that both the instrumented test procedure and the required stride segmentation algorithm have to be selected adequately in order to support and complement classical clinical examination by sensor-based movement assessment. Full article

The KRAS oncogene is involved in the pathogenesis of several types of cancer, particularly colorectal cancer (CRC). The most frequent mutations in this gene are associated with poor survival, increased tumor aggressiveness and resistance to therapy with anti-epidermal growth factor receptor (EGFR) antibodies. For this reason, KRAS mutation testing has become increasingly common in clinical practice for personalized cancer treatments of CRC patients. Detection methods for KRAS mutations are currently expensive, laborious, time-consuming and often lack of diagnostic sensitivity and specificity. In this study, we describe the development of a Lab-on-Chip assay for genotyping of KRAS mutational status. This assay, based on the In-Check platform, integrates microfluidic handling, a multiplex polymerase chain reaction (PCR) and a low-density microarray. This integrated sample-to-result system enables the detection of KRAS point mutations, including those occurring in codons 12 and 13 of exon 2, 59 and 61 of exon 3, 117 and 146 of exon 4. Thanks to its miniaturization, automation, rapid analysis, minimal risk of sample contamination, increased accuracy and reproducibility of results, this Lab-on-Chip platform may offer immediate opportunities to simplify KRAS genotyping into clinical routine. Full article

The increase in the number of adolescents with internet gaming disorder (IGD), a type of behavioral addiction is becoming an issue of public concern. Teaching adolescents to suppress their craving for gaming in daily life situations is one of the core strategies for treating IGD. Recent studies have demonstrated that computer-aided treatment methods, such as neurofeedback therapy, are effective in relieving the symptoms of a variety of addictions. When a computer-aided treatment strategy is applied to the treatment of IGD, detecting whether an individual is currently experiencing a craving for gaming is important. We aroused a craving for gaming in 57 adolescents with mild to severe IGD using numerous short video clips showing gameplay videos of three addictive games. At the same time, a variety of biosignals were recorded including photoplethysmogram, galvanic skin response, and electrooculogram measurements. After observing the changes in these biosignals during the craving state, we classified each individual participant’s craving/non-craving states using a support vector machine. When video clips edited to arouse a craving for gaming were played, significant decreases in the standard deviation of the heart rate, the number of eye blinks, and saccadic eye movements were observed, along with a significant increase in the mean respiratory rate. Based on these results, we were able to classify whether an individual participant felt a craving for gaming with an average accuracy of 87.04%. This is the first study that has attempted to detect a craving for gaming in an individual with IGD using multimodal biosignal measurements. Moreover, this is the first that showed that an electrooculogram could provide useful biosignal markers for detecting a craving for gaming. Full article

Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNR ${}_{\mathrm{S}}$ is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario. Full article

Brain disease including any conditions or disabilities that affect the brain is fast becoming a leading cause of death. The traditional diagnostic methods of brain disease are time-consuming, inconvenient and non-patient friendly. As more and more individuals undergo examinations to determine if they suffer from any form of brain disease, developing noninvasive, efficient, and patient friendly detection systems will be beneficial. Therefore, in this paper, we propose a novel noninvasive brain disease detection system based on the analysis of facial colors. The system consists of four components. A facial image is first captured through a specialized sensor, where four facial key blocks are next located automatically from the various facial regions. Color features are extracted from each block to form a feature vector for classification via the Probabilistic Collaborative based Classifier. To thoroughly test the system and its performance, seven facial key block combinations were experimented. The best result was achieved using the second facial key block, where it showed that the Probabilistic Collaborative based Classifier is the most suitable. The overall performance of the proposed system achieves an accuracy −95%, a sensitivity −94.33%, a specificity −95.67%, and an average processing time (for one sample) of <1 min at brain disease detection. Full article

Porous silicon seems to be an appropriate material platform for the development of high-sensitivity and low-cost optical sensors, as their porous nature increases the interaction with the target substances, and their fabrication process is very simple and inexpensive. In this paper, we present the experimental development of a porous silicon microcavity sensor and its use for real-time in-flow sensing application. A high-sensitivity configuration was designed and then fabricated, by electrochemically etching a silicon wafer. Refractive index sensing experiments were realized by flowing several dilutions with decreasing refractive indices, and measuring the spectral shift in real-time. The porous silicon microcavity sensor showed a very linear response over a wide refractive index range, with a sensitivity around 1000 nm/refractive index unit (RIU), which allowed us to directly detect refractive index variations in the 10⁻⁷ RIU range. Full article

Coronary artery disease and its related complications pose great threats to human health. In this work, we aim to clinically evaluate a CMOS field-effect biomolecular sensor for cardiac biomarkers, cardiac-specific troponin-I (cTnI), N-terminal prohormone brain natriuretic peptide (NT-proBNP), and interleukin-6 (IL-6). The CMOS biosensor is implemented via a standard commercialized 0.35 μm CMOS process. To validate the sensing characteristics, in buffer conditions, the developed CMOS biosensor has identified the detection limits of IL-6, cTnI, and NT-proBNP as being 45 pM, 32 pM, and 32 pM, respectively. In clinical serum conditions, furthermore, the developed CMOS biosensor performs a good correlation with an enzyme-linked immuno-sorbent assay (ELISA) obtained from a hospital central laboratory. Based on this work, the CMOS field-effect biosensor poses good potential for accomplishing the needs of a point-of-care testing (POCT) system for heart disease diagnosis. Full article

Detection and control of infectious diseases is a major problem, especially in developing countries. Lateral flow immunoassays can be used with great success for the detection of infectious diseases. However, for the quantification of their results an electronic reader is required. This paper presents an optimized handheld electronic reader for developing countries. It features a potentially low-cost, low-power, battery-operated device with no added optical accessories. The operation of this proof of concept device is based on measuring the reflected light from the lateral flow immunoassay and translating it into the concentration of the specific analyte of interest. Characterization of the surface of the lateral flow immunoassay has been performed in order to accurately model its response to the incident light. Ray trace simulations have been performed to optimize the system and achieve maximum sensitivity by placing all the components in optimum positions. A microcontroller enables all the signal processing to be performed on the device and a Bluetooth module allows transmission of the results wirelessly to a mobile phone app. Its performance has been validated using lateral flow immunoassays with influenza A nucleoprotein in the concentration range of 0.5 ng/mL to 200 ng/mL. Full article

The object of the proposed paper is to design and analyze the performance of a non-contact heart rate variability (HRV) measuring device based on ultrasound transducers. The rationale behind non-contact HRV measurement is the goal of obtaining a means of long term monitoring of a patient’s heart performance. Due to its complexity as a non-contact measuring device, influential physical quantities, error source and other perturbations were thoroughly investigated. For medical purposes it is of utmost importance to define the target uncertainty of a measuring method from the side of physicians, while it is the role of scientists to realistically evaluate all uncertainty contributions. Within this paper we present a novelty method of non-contact HRV measurement based on ultrasound transducers operating at two frequencies simultaneously. We report laboratory results and clinical evaluations are given for healthy subjects as well as patients with known heart conditions. Furthermore, laboratory tests were conducted on subjects during a relaxation period, and after 1 min physical activity Full article

Non-invasive real time evaluation of living cell conditions and functions are increasingly desired in the field of clinical diagnosis. For diagnosis of type I allergy, the identification of antigens that induces activation of mast cells and basophils is crucial to avoid symptoms of allergic diseases. However, conventional tests, such as detection of antigen-specific IgE antibody and skin tests, are either of low reliability or are invasive. To overcome such problems, we hereby applied an impedance sensor for label-free and real-time monitoring of mast cell reactions in response to stimuli. When IgE-sensitized RBL-2H3 cells cultured on the electrodes were stimulated with various concentrations of antigens, dose-dependent cell index (CI) increases were detected. Moreover, we confirmed that the impedance sensor detected morphological changes rather than degranulation as the indicator of cell activation. Furthermore, the CI of human IgE receptor-expressing cells (RBL-48 cells) treated with serum of a sweat allergy-positive patient, but not with serum from a sweat allergy-negative patient, significantly increased in response to purified human sweat antigen. We thus developed a technique to detect the activation of living cells in response to stimuli without any labeling using the impedance sensor. This system may represent a high reliable tool for the diagnosis of type I allergy. Full article

The aim of this work is the development of a contactless capacitive sensory system for the detection of (Electrocardiographic) ECG-like signals. The acquisition approach is based on a capacitive coupling with the patient body performed by electrodes integrated in a front-end circuit. The proposed system is able to detect changes in the electric charge related to the heart activity. Due to the target signal weakness and to the presence of other undesired signals, suitable amplification stages and analogue filters are required. Simulated results allowed us to evaluate the effectiveness of the approach, whereas experimental measurements, recorded without contact to the skin, have validated the practical effectiveness of the proposed architecture. The system operates with a supply voltage of ±9 V with an overall power consumption of about 10 mW. The analogue output of the electronic interface is connected to an ATmega328 microcontroller implementing the A/D conversion and the data acquisition. The collected data can be displayed on any multimedia support for real-time tracking applications. Full article

Dynamic accurate heart-rate (HR) estimation using a photoplethysmogram (PPG) during intense physical activities is always challenging due to corruption by motion artifacts (MAs). It is difficult to reconstruct a clean signal and extract HR from contaminated PPG. This paper proposes a robust HR-estimation algorithm framework that uses one-channel PPG and tri-axis acceleration data to reconstruct the PPG and calculate the HR based on features of the PPG and spectral analysis. Firstly, the signal is judged by the presence of MAs. Then, the spectral peaks corresponding to acceleration data are filtered from the periodogram of the PPG when MAs exist. Different signal-processing methods are applied based on the amount of remaining PPG spectral peaks. The main MA-removal algorithm (NFEEMD) includes the repeated single-notch filter and ensemble empirical mode decomposition. Finally, HR calibration is designed to ensure the accuracy of HR tracking. The NFEEMD algorithm was performed on the 23 datasets from the 2015 IEEE Signal Processing Cup Database. The average estimation errors were 1.12 BPM (12 training datasets), 2.63 BPM (10 testing datasets) and 1.87 BPM (all 23 datasets), respectively. The Pearson correlation was 0.992. The experiment results illustrate that the proposed algorithm is not only suitable for HR estimation during continuous activities, like slow running (13 training datasets), but also for intense physical activities with acceleration, like arm exercise (10 testing datasets). Full article

Noise and artifacts are inherent contaminating components and are particularly present in Holter electrocardiogram (ECG) monitoring. The presence of noise is even more significant in long-term monitoring (LTM) recordings, as these are collected for several days in patients following their daily activities; hence, strong artifact components can temporarily impair the clinical measurements from the LTM recordings. Traditionally, the noise presence has been dealt with as a problem of non-desirable component removal by means of several quantitative signal metrics such as the signal-to-noise ratio (SNR), but current systems do not provide any information about the true impact of noise on the ECG clinical evaluation. As a first step towards an alternative to classical approaches, this work assesses the ECG quality under the assumption that an ECG has good quality when it is clinically interpretable. Therefore, our hypotheses are that it is possible (a) to create a clinical severity score for the effect of the noise on the ECG, (b) to characterize its consistency in terms of its temporal and statistical distribution, and (c) to use it for signal quality evaluation in LTM scenarios. For this purpose, a database of external event recorder (EER) signals is assembled and labeled from a clinical point of view for its use as the gold standard of noise severity categorization. These devices are assumed to capture those signal segments more prone to be corrupted with noise during long-term periods. Then, the ECG noise is characterized through the comparison of these clinical severity criteria with conventional quantitative metrics taken from traditional noise-removal approaches, and noise maps are proposed as a novel representation tool to achieve this comparison. Our results showed that neither of the benchmarked quantitative noise measurement criteria represent an accurate enough estimation of the clinical severity of the noise. A case study of long-term ECG is reported, showing the statistical and temporal correspondences and properties with respect to EER signals used to create the gold standard for clinical noise. The proposed noise maps, together with the statistical consistency of the characterization of the noise clinical severity, paves the way towards forthcoming systems providing us with noise maps of the noise clinical severity, allowing the user to process different ECG segments with different techniques and in terms of different measured clinical parameters. Full article

The total analytical error of a photonic crystal (PC) biosensor in the determination of ferritin and soluble transferrin receptor (sTfR) as biomarkers of iron deficiency anemia in chronic kidney disease (CKD) patients was evaluated against certified ELISAs. Antigens were extracted from sera of CKD patients using functionalized iron-oxide nanoparticles (fAb-IONs) followed by magnetic separation. Immuno-complexes were recognized by complementary detection Ab affixed to the PC biosensor surface, and their signals were followed using the BIND instrument. Quantification was conducted against actual protein standards. Total calculated error (TEcalc) was estimated based on systematic (SE) and random error (RE) and compared against total allowed error (TEa) based on established quality specifications. Both detection platforms showed adequate linearity, specificity, and sensitivity for biomarkers. Means, SD, and CV were similar between biomarkers for both detection platforms. Compared to ELISA, inherent imprecision was higher on the PC biosensor for ferritin, but not for sTfR. High SE or RE in the PC biosensor when measuring either biomarker resulted in TEcalc higher than the TEa. This did not influence the diagnostic ability of the PC biosensor to discriminate CKD patients with low iron stores. The performance of the PC biosensor is similar to certified ELISAs; however, optimization is required to reduce TEcalc. Full article

This paper presents a simple total power radiometer to noninvasively measure the temperature of the human body. The proposed 3-GHz radiometer consists of an antenna collecting the noise power generated by a target, a low-noise and high-gain receiver amplifying the noise power, and a detector converting the noise power to voltage. A single-pole-triple-throw (SP3T) switch is placed between the antenna and the receiver, while a personal computer is used to control the SP3T switch, collect and process the data such as detector output voltages and physical temperatures of the reference noise sources and the target. The fabricated radiometer shows a good performance agreement with a thermometer in the temperature measurement of water from 25.0 to 43.1 °C. For the accurate prediction of the target temperature, the radiometer is calibrated adaptively to the environment and radiometer variations. For this purpose, two reference noise sources (hot and cold) are proposed using matched and mismatched resistors at room temperature. These resistor-based noise sources offer a reliable performance without complex temperature control systems. Furthermore, they can be easily calibrated in real time by periodically measuring the physical temperatures of the resistors. In addition, the logarithmic detector with wide dynamic range is adopted and logarithmically-fitted based on the measurement results instead of linear approximation, which reduces the error caused by the limited dynamic range of resistor-based noise sources. In order to further increase the accuracy, the performance imbalances between ports in the SP3T switch are also taken into account by employing offsets in the radiometer output voltages. Full article

Algorithms for locomotion mode recognition (LMR) based on surface electromyography and mechanical sensors have recently been developed and could be used for the neural control of powered prosthetic legs. However, the variations in input signals, caused by physical changes at the sensor interface and human physiological changes, may threaten the reliability of these algorithms. This study aimed to investigate the effectiveness of applying adaptive pattern classifiers for LMR. Three adaptive classifiers, i.e., entropy-based adaptation (EBA), LearnIng From Testing data (LIFT), and Transductive Support Vector Machine (TSVM), were compared and offline evaluated using data collected from two able-bodied subjects and one transfemoral amputee. The offline analysis indicated that the adaptive classifier could effectively maintain or restore the performance of the LMR algorithm when gradual signal variations occurred. EBA and LIFT were recommended because of their better performance and higher computational efficiency. Finally, the EBA was implemented for real-time human-in-the-loop prosthesis control. The online evaluation showed that the applied EBA effectively adapted to changes in input signals across sessions and yielded more reliable prosthesis control over time, compared with the LMR without adaptation. The developed novel adaptive strategy may further enhance the reliability of neurally-controlled prosthetic legs. Full article

Copper (I) oxide (Cu₂O) is an appealing semiconducting oxide with potential applications in various fields ranging from photovoltaics to biosensing. The precise control of size and shape of Cu₂O nanostructures has been an area of intense research. Here, the electrodeposition of Cu₂O nanoparticles is presented with precise size variations by utilizing ethylenediamine (EDA) as a size controlling agent. The size of the Cu₂O nanoparticles was successfully varied between 54.09 nm to 966.97 nm by changing the concentration of EDA in the electrolytic bath during electrodeposition. The large surface area of the Cu₂O nanoparticles present an attractive platform for immobilizing glucose oxidase for glucose biosensing. The fabricated enzymatic biosensor exhibited a rapid response time of <2 s. The limit of detection was 0.1 μM and the sensitivity of the glucose biosensor was 1.54 mA/cm². mM. The Cu₂O nanoparticles were characterized by UV-Visible spectroscopy, scanning electron microscopy and X-ray diffraction. Full article

A microsized slit-embedded cantilever sensor (slit cantilever) was fabricated and evaluated as a biosensing platform in a liquid environment. In order to minimize the degradation caused by viscous damping, a 300 × 100 µm² (length × width) sized cantilever was released by a 5 µm gap-surrounding and vibrated by an internal piezoelectric-driven self-actuator. Owing to the structure, when the single side of the slit cantilever was exposed to liquid a significant quality factor (Q = 35) could be achieved. To assess the sensing performance, the slit cantilever was exploited to study the biophysical kinetics related to Aβ peptide. First, the quantification of Aβ peptide with a concentration of 10 pg/mL to 1 μg/mL was performed. The resonant responses exhibited a dynamic range from 100 pg/mL to 100 ng/mL (−56.5 to −774 ΔHz) and a dissociation constant (K_D) of binding affinity was calculated as 1.75 nM. Finally, the Aβ self-aggregation associated with AD pathogenesis was monitored by adding monomeric Aβ peptides. As the concentration of added analyte increased from 100 ng/mL to 10 µg/mL, both the frequency shift values (−813 to −1804 ΔHz) and associate time constant increased. These results showed the excellent sensing performance of the slit cantilever overcoming a major drawback in liquid environments to become a promising diagnostic tool candidate. Full article

Exhaled breath is a body secretion, and the sampling process of this is simple and cost effective. It can be non-invasively collected for diagnostic procedures. Variations in the chemical composition of exhaled breath resulting from gaseous exchange in the extensive capillary network of the body are proposed to be associated with pathophysiological changes. In light of the foreseeable potential of exhaled breath as a diagnostic specimen, we used gas chromatography and mass spectrometry (GC-MS) to study the chemical compounds present in exhaled breath samples from patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), and from healthy individuals as a control group. In addition, we also designed and developed a chemical-based exhaled breath sensor system to examine the distribution pattern in the patient and control groups. The results of our study showed that several chemical compounds, such as 1-phenantherol and ethyl 3-cyano-2,3-bis (2,5,-dimethyl-3-thienyl)-acrylate, had a higher percentage area in the AD group than in the PD and control groups. These results may indicate an association of these chemical components in exhaled breath with the progression of disease. In addition, in-house fabricated exhaled breath sensor systems, containing several types of gas sensors, showed significant differences in terms of the normalized response of the sensitivity characteristics between the patient and control groups. A subsequent clustering analysis was able to distinguish between the AD patients, PD patients, and healthy individuals using principal component analysis, Sammon’s mapping, and a combination of both methods, in particular when using the exhaled breath sensor array system A consisting of eight sensors. With this in mind, the exhaled breath sensor system could provide alternative option for diagnosis and be applied as a useful, effective tool for the screening and diagnosis of AD in the near future. Full article

To address the need for asthma self-management in pediatrics, the authors present the feasibility of a mobile health (mHealth) platform built on their prior work in an asthmatic adult and child. Real-time asthma attack risk was assessed through physiological and environmental sensors. Data were sent to a cloud via a smartwatch application (app) using Health Insurance Portability and Accountability Act (HIPAA)-compliant cryptography and combined with online source data. A risk level (high, medium or low) was determined using a random forest classifier and then sent to the app to be visualized as animated dragon graphics for easy interpretation by children. The feasibility of the system was first tested on an adult with moderate asthma, then usability was examined on a child with mild asthma over several weeks. It was found during feasibility testing that the system is able to assess asthma risk with 80.10 ± 14.13% accuracy. During usability testing, it was able to continuously collect sensor data, and the child was able to wear, easily understand and enjoy the use of the system. If tested in more individuals, this system may lead to an effective self-management program that can reduce hospitalization in those who suffer from asthma. Full article

An attractive electrochemical sensor of poly(3,4-ethylenedioxythiophene)/reduced graphene oxide electrode (PrGO) was developed for an electrochemical technique for uric acid (UA) detection in the presence of ascorbic acid (AA). PrGO composite film showed an improved electrocatalytic activity towards UA oxidation in pH 6.0 (0.1 M PBS). The PrGO composite exhibited a high current signal and low charge transfer resistance (R_ct) compared to a reduced graphene oxide (rGO) electrode or a bare glassy carbon electrode (GCE). The limit of detection and sensitivity of PrGO for the detection of UA are 0.19 μM (S/N = 3) and 0.01 μA/μM, respectively, in the range of 1–300 μM of UA. Full article

Wearable health monitoring systems have gained considerable interest in recent years owing to their tremendous promise for personal portable health watching and remote medical practices. The sensors with excellent flexibility and stretchability are crucial components that can provide health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous uncomfortableness and invasiveness. The signals acquired by these sensors, such as body motion, heart rate, breath, skin temperature and metabolism parameter, are closely associated with personal health conditions. This review attempts to summarize the recent progress in flexible and stretchable sensors, concerning the detected health indicators, sensing mechanisms, functional materials, fabrication strategies, basic and desired features. The potential challenges and future perspectives of wearable health monitoring system are also briefly discussed. Full article

Cardiovascular disease (CVD) and cancer are two leading causes of death worldwide. CVD and cancer share risk factors such as obesity and diabetes mellitus and have common diagnostic biomarkers such as interleukin-6 and C-reactive protein. Thus, timely and accurate diagnosis of these two correlated diseases is of high interest to both the research and healthcare communities. Most conventional methods for CVD and cancer biomarker detection such as microwell plate-based immunoassay and polymerase chain reaction often suffer from high costs, low test speeds, and complicated procedures. Recently, lab-on-a-chip (LoC)-based platforms have been increasingly developed for CVD and cancer biomarker sensing and analysis using various molecular and cell-based diagnostic biomarkers. These new platforms not only enable better sample preparation, chemical manipulation and reaction, high-throughput and portability, but also provide attractive features such as label-free detection and improved sensitivity due to the integration of various novel detection techniques. These features effectively improve the diagnostic test speed and simplify the detection procedure. In addition, microfluidic cell assays and organ-on-chip models offer new potential approaches for CVD and cancer diagnosis. Here we provide a mini-review focusing on recent development of LoC-based methods for CVD and cancer diagnostic biomarker measurements, and our perspectives of the challenges, opportunities and future directions. Full article

Chagas disease (CD), which mostly affects those living in deprived areas, has become one of Latin America’s main public health problems. Effective prevention of the disease requires early diagnosis, initiation of therapy, and regular blood monitoring of the infected individual. However, the majority of the Trypanosoma cruzi infections go undiagnosed because of mild symptoms, limited access to medical attention and to a high variability in the sensitivity and specificity of diagnostic tests. Consequently, more affordable and accessible detection technologies capable of providing early diagnosis and T. cruzi load measurements in settings where CD is most prevalent are needed to enable enhanced intervention strategies. This work analyzes the potential contribution of biosensing technologies, reviewing examples that have been tested and contrasted with traditional methods, both serological and parasitological (i.e., molecular detection by PCR), and discusses some emerging biosensing technologies that have been applied for this public health issue. Even if biosensing technologies still require further research efforts to develop portable systems, we arrive at the conclusion that biosensors could improve the accuracy of CD diagnosis and the follow-up of patients’ treatments in terms of the rapidity of results, small sample volume, high integration, ease of use, real-time and low cost detection when compared with current conventional technologies. Full article

With a rapid improvement of smartphone hardware and software, especially complementary metal oxide semiconductor (CMOS) cameras, many optical biosensors based on smartphone platforms have been presented, which have pushed the development of the point-of-care testing (POCT). Imaging-based and spectrometry-based detection techniques have been widely explored via different approaches. Combined with the smartphone, imaging-based and spectrometry-based methods are currently used to investigate a wide range of molecular properties in chemical and biological science for biosensing and diagnostics. Imaging techniques based on smartphone-based microscopes are utilized to capture microscale analysts, while spectrometry-based techniques are used to probe reactions or changes of molecules. Here, we critically review the most recent progress in imaging-based and spectrometry-based smartphone-integrated platforms that have been developed for chemical experiments and biological diagnosis. We focus on the analytical performance and the complexity for implementation of the platforms. Full article

Early diagnosis of lung cancer helps to reduce the cancer death rate significantly. Over the years, investigators worldwide have extensively investigated many screening modalities for lung cancer detection, including computerized tomography, chest X-ray, positron emission tomography, sputum cytology, magnetic resonance imaging and biopsy. However, these techniques are not suitable for patients with other pathologies. Developing a rapid and sensitive technique for early diagnosis of lung cancer is urgently needed. Biosensor-based techniques have been recently recommended as a rapid and cost-effective tool for early diagnosis of lung tumor markers. This paper reviews the recent development in screening and biosensor-based techniques for early lung cancer detection. Full article

Early disease diagnostics require rapid, sensitive, and selective detection methods for target analytes. Specifically, early viral detection in a point-of-care setting is critical in preventing epidemics and the spread of disease. However, conventional methods such as enzyme-linked immunosorbent assays or cell cultures are cumbersome and difficult for field use due to the requirements of extensive lab equipment and highly trained personnel, as well as limited sensitivity. Recent advances in nanoparticle concentration have given rise to many novel detection methodologies, which address the shortcomings in modern clinical assays. Here, we review the primary, well-characterized methods for nanoparticle concentration in the context of viral detection via diffusion, centrifugation and microfiltration, electric and magnetic fields, and nano-microfluidics. Details of the concentration mechanisms and examples of related applications provide valuable information to design portable, integrated sensors. This study reviews a wide range of concentration techniques and compares their advantages and disadvantages with respect to viral particle detection. We conclude by highlighting selected concentration methods and devices for next-generation biosensing systems. Full article