An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals

Smartphone-Based Inertial Odometry for Blind Walkers

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.

Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach

Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried’s frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor.

Automatic Pulse Classification for Artefact Removal Using SAX Strings, a CENTER-TBI Study

High-resolution, waveform-level data from bedside monitors carry important information about a patient's physiology but is also polluted with artefactual data. Manual mark-up is the standard practice for detecting and eliminating artefacts, but it is time-consuming, prone to errors, biased and not suitable for real-time processing.In this paper we present a novel automatic artefact detection technique based on a Symbolic Aggregate approXimation (SAX) technique which makes it possible to represent individual pulses as 'words'. It does that by coding each pulse with a specified number of letters (here six) from a predefined alphabet of characters (here six). The word is then fed to a support vector machine (SVM) and classified as artefactual or physiological.To define the universe of acceptable pulses, the arterial blood pressure from 50 patients was analysed, and acceptable pulses were manually chosen by looking at the average pulse that each 'word' generated. This was then used to train a SVM classifier. To test this algorithm, a dataset with a balanced ratio of clean and artefactual pulses was built, classified and independently evaluated by two observers achieving a sensitivity of 0.972 and 0.954 and a specificity of 0.837 and 0.837 respectively.

RAQ: A Noise-Resistant Calibration-Independent Compliance Surrogate

The intracranial pressure (ICP)-volume relationship contains important information for diagnosing hydrocephalus and other space-occupying pathologies. We aimed to design a new parameter which quantifies the relationship and can be calculated from overnight recordings.The new parameter, the respiratory amplitude quotient (RAQ), characterizes the modulation of the pulse amplitude by the respiratory wave in the ICP time course. RAQ is defined as the ratio of the amplitude of the respiratory wave in the ICP signal to the amplitude of the respiration-induced wave in the course of the heartbeat-dependent pulse amplitude.We tested RAQ on synthetically generated ICP waveforms and found a mean difference of <0.5% between the calculated values of RAQ and the theoretically determined values. We further extracted RAQ from datasets obtained by overnight recording in hydrocephalus patients with a stenosis of the aqueduct and a comparison group finding a significant difference between the RAQ values of either group.

Comparison of Two Algorithms Analysing the Intracranial Pressure Curve in Terms of the Accuracy of Their Start-Point Detection and Resistance to Artefacts

OBJECTIVES

For further insight into the possibly predictive quality of the intracranial pressure (ICP) waveform morphology a definite and reliable identification of its components is a prerequisite but presents the problem of artefacts in physiological signals.

METHODS

ICP and electrocardiogram (ECG) data were recorded to depict not only their numerical value but also their respective waveforms and were analysed by two algorithms, which were then compared for their artefact resistance.The algorithms in question identify the start point of every ICP wave, one (AR[SA]) by scale analysis, the other (AR[ECG]) by analysing the ICP wave linked to the ECG.

RESULTS

Start-point identification accuracy in rhythmic patients showed sensitivity of 95.14% for AR[SA] and 99.99% for AR[ECG], with a positive predictive value (ppv) of 98.30% for AR[SA] and 99.76% for AR[ECG].In arrhythmic patients sensitivity was 98.05% for AR[SA] and 99.73% for AR[ECG], with a ppv of 100% for AR[SA] and 99.78% for AR[ECG].

CONCLUSIONS

AR[ECG] has proven to be more resistant to artefacts than AR[SA], even in cases such as cardiac arrhythmia. It facilitates reliable, three-dimensional visualisation of long-term changes in ICP-wave morphology and is thus suited for analysis in cases of more complex or irregular vital parameters.

Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings

Respiration is an essential and primary mechanism for speech production. We first inhale and then produce speech while exhaling. When we run out of breath, we stop speaking and inhale. Though this process is involuntary, speech production involves a systematic outflow of air during exhalation characterized by linguistic content and prosodic factors of the utterance. Thus speech and respiration are closely related, and modeling this relationship makes sensing respiratory dynamics directly from the speech plausible, however is not well explored. In this article, we conduct a comprehensive study to explore techniques for sensing breathing signal and breathing parameters from speech using deep learning architectures and address the challenges involved in establishing the practical purpose of this technology. Estimating the breathing pattern from the speech would give us information about the respiratory parameters, thus enabling us to understand the respiratory health using one's speech.

Closed-Loop Acoustic Stimulation During Sedation with Dexmedetomidine (CLASS-D): Protocol for a Within-Subject, Crossover, Controlled, Interventional Trial with Healthy Volunteers

INTRODUCTION

The relative power of slow-delta oscillations in the electroencephalogram (EEG), termed slow-wave activity (SWA), correlates with level of unconsciousness. Acoustic enhancement of SWA has been reported for sleep states, but it remains unknown if pharmacologically induced SWA can be enhanced using sound. Dexmedetomidine is a sedative whose EEG oscillations resemble those of natural sleep. This pilot study was designed to investigate whether SWA can be enhanced using closed-loop acoustic stimulation during sedation (CLASS) with dexmedetomidine.

METHODS

Closed-Loop Acoustic Stimulation during Sedation with Dexmedetomidine (CLASS-D) is a within-subject, crossover, controlled, interventional trial with healthy volunteers. Each participant will be sedated with a dexmedetomidine target-controlled infusion (TCI). Participants will undergo three CLASS conditions in a multiple crossover design: in-phase (phase-locked to slow-wave upslopes), anti-phase (phase-locked to slow-wave downslopes) and sham (silence). High-density EEG recordings will assess the effects of CLASS across the scalp. A volitional behavioral task and sequential thermal arousals will assess the anesthetic effects of CLASS. Ambulatory sleep studies will be performed on nights immediately preceding and following the sedation session. EEG effects of CLASS will be assessed using linear mixed-effects models. The impacts of CLASS on behavior and arousal thresholds will be assessed using logistic regression modeling. Parametric modeling will determine differences in sleepiness and measures of sleep homeostasis before and after sedation.

RESULTS

The primary outcome of this pilot study is the effect of CLASS on EEG slow waves. Secondary outcomes include the effects of CLASS on the following: performance of a volitional task, arousal thresholds, and subsequent sleep.

DISCUSSION

This investigation will elucidate 1) the potential of exogenous sensory stimulation to potentiate SWA during sedation; 2) the physiologic significance of this intervention; and 3) the connection between EEG slow-waves observed during sleep and sedation.

Evaluation of the Fetal Left Ventricular Myocardial Performance Index (MPI) by Using an Automated Measurement of Doppler Signals in Normal Pregnancies

The myocardial performance index is widely used as an indicator of the heart’s performance. However, due to the subjective nature of ultrasonic testing, there are differences in the measurements among inspectors, requiring a quantitative and objective assessment. In this study, an automated program was developed to quantitatively evaluate the myocardial performance index (MPI) and the cardiac time intervals in the left ventricle for each trimester. One hundred and thirty-three pregnant women who visited the hospital for prenatal examinations were studied, and skilled inspectors obtained left ventricular blood flow waveforms from 47 fetuses in the 12 weeks, 54 fetuses in the 22 weeks, and 32 fetuses in the 31 weeks of pregnancy using a pulse Doppler mode of ultrasound equipment. The acquired images automatically measured the isovolumetric contraction time (IVCT), isovolumetric relaxation time (IVRT,) ejection time (ET), and filling time (FT), and calculated the Tei index (TI) and the K-index (KI); each interval was manually measured during the actual inspection for comparison. In this study, the ultrasonic Doppler waveform was objectively analyzed and measured by the automated program, and it will help with the evaluation of fetal heart function.

Assessing an Automatic Procedure of Extraction of Physiological Parameters from Skin using Video Photoplethysmography

Video Photoplethysmography (vPPG) allows for estimation of blood volume pulse (BVP) from the skin by means of a video camera recording at high frequency rate. The estimation procedure presents several drawbacks in its application to real world conditions, such as light changes or movements that often generate artifacts in the extracted BVP waveform. In addition, the process requires a skin segmentation algorithm to distinguish skin pixels from the background. To date, even the most refined skin segmentation algorithms still need a manual definition that could lead to incorrect pixel classification, and consequently to a decrease in the signal-to-noise ratio (SNR). We here propose a fully autonomic procedure able to extract BVP from video recordings of the skin in real world conditions.The experimental protocol is designed to record the signals of interest and to evaluate changes in the Autonomic Nervous System modulation of the heart during a baseline condition and a controlled breathing phase. Video recordings are gathered from 4 young healthy subjects (age: 21±1 years). vPPG signals are processed in order to extract the BVP waveform, and a peak detection algorithm detects pulse wave peaks that are then used to compute specific measures of heart rate variability (HRV).The procedure is successfully validated by comparing the extracted HRV measures against those extracted using a finger photoplethysmograph (fPPG) using three different skin segmentation algorithms from BVP signals.

RAQ: a novel surrogate for the craniospinal pressure-volume relationship

OBJECTIVE

The intracranial pressure-volume relation contains information relevant for diagnostics of hydrocephalus and other space-occupying pathologies. We aimed to design a noise-resilient surrogate for this relationship that can be calculated from intracranial pressure (ICP) signals.

APPROACH

The new surrogate, termed respiratory amplitude quotient (RAQ), characterizes the modulation of the cardiac pulse wave amplitude by the respiratory wave in the ICP time course. RAQ is defined as the ratio of the amplitude of the respiratory wave in the ICP signal to the amplitude of the respiration-induced variation in the course of the cardiac pulse wave amplitude. We validated the calculation of RAQ on synthetically generated ICP waveforms. We further extracted RAQ retrospectively from overnight ICP recordings in a cohort of hydrocephalus patients with aqueductal stenosis, age 55.8 ± 18.0 years, and a comparison group with hydrocephalus diagnosed by morphology in MRI, but not responsive to either external lumbar drainage or ventriculo-peritoneal shunting, age 72.5 ± 6.1 years. RAQ was determined for the full recordings, and separately for periods containing B-waves.

MAIN RESULTS

We found a mean difference of less than 2% between the calculated values of RAQ and the theoretically determined equivalent descriptors of the synthetic ICP waveforms. In the overnight recordings, we found significantly different RAQ values during B-waves in the aqueductal stenosis (0.86 ± 0.11) and non-responsive hydrocephalus patient groups (1.07 ± 0.20), p = 0.027. In contrast, there was no significant difference in other tested parameters, namely pressure-volume index, elastance coefficient, and resistance to outflow. Neither did we find significant difference when considering RAQ over the full recordings.

SIGNIFICANCE

Our results indicate that RAQ may function as a potential surrogate for the intracranial pressure-volume relation.

Heart Rate Variability Analysis: How Much Artifact Can We Remove?

OBJECTIVE

Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objective of this study was to determine the impact of small errors in BBI selection on HRV analysis and produce a foundation for future research in mental health wearable technology.

METHODS

This was a sub-analysis from a prospective observational clinical trial registered with clinicaltrials.gov (NCT03030924). A cohort of 10 subject's HRV tracings from a wearable wrist monitor without any artifact were manipulated by the study team to represent the most common forms of artifact encountered.

RESULTS

Root mean square of successive differences stayed below a clinically significant change when up to 5 beats were selected at the wrong time interval and up to 36% of BBIs was removed. Standard deviation of next normal intervals stayed below a clinically significant change when up to 3 beats were selected at the wrong time interval and up to 36% of BBIs were removed. High frequency HRV shows significant changes when more than 2 beats were selected at the wrong time interval and any BBIs were removed.

CONCLUSION

Time domain HRV metrics appear to be more robust to artifact compared to frequency domains. Investigators examining wearable technology for mental health should be aware of these values for future analysis of HRV studies to improve data quality.

A simple model to detect atrial fibrillation via visual imaging

Automatic detection of atrial fibrillation (AF) is a challenging issue. In this study we proposed and validated a model to identify AF by using facial video recordings. We analyzed photoplethysmographic imaging (PPGi) signals, extracted from video of a subject's face. Sixty-eight patients were included: 30 in sinus rhythm (SR), 25 in AF and 13 presenting with atrial flutter or frequent ectopic beats (ARR). Twenty-six indexes were computed. The dataset was divided in three subsets: the training, validation, and test set, containing, respectively, 58, 29, and 13% of the data. Mean of inter-systolic interval series (M), Local Maxima Similarity (LMS), and pulse harmonic strength (PHS) indexes were significantly different among all groups. Variability and irregularity parameters had the lowest values in SR, the highest in AF, with intermediate values in ARR. The PHS was higher in SR than in ARR, and higher in ARR than in AF. The LMS index was the highest in SR, intermediate in ARR and the lowest in AF. Similarity indexes were higher in SR than in AF and ARR. A model with three features, namely M, Similarity1 and LMS was chosen. With this model, the accuracy for the validation set was 0.947±0.007 for SR, 0.954±0.004 for AF and 0.919±0.006 for ARR; for the test set (never-seen data), accuracy was 0.876±0.021 for SR, 0.870±0.030 for AF and 0.863±0.029 for ARR. A contactless video-based monitoring can be used to detect AF, differentiating it from SR and from frequent ectopies.

Identification and Tracking of Physiological Parameters from Skin using Video Photoplethysmography

In recent years, there has been a growing interest in video Photoplethysmography (vPPG), a technique able to estimate cardiovascular parameters from video recordings of the skin. Despite the growing interest in vPPG technology, there are still problems in extracting the correct waveform of blood volume pulse, mainly due to real world artifacts, such as changes in light condition and movement artifacts. Another important issue is the correct definition of skin against background. Therefore, we propose an algorithm of skin detection that is able to recognize skin pixels solid to variations of luminosity. We recorded the signals of interest during an experimental protocol designed to provide thermal stimulation and observe the resulting Autonomic Nervous System changes. Experimental data were gathered from 10 young healthy subjects (age: 21±2 years). Video recordings are processed using a band-pass filter and then an automatic algorithm of peak detection is applied to detect the pulse wave peaks, then used to estimate heart rate variability (HRV). The efficiency and stability of the algorithm are compared against finger-PPG waveforms. Preliminary results show an overall statistical agreement between time and frequency domain indexes. However, further efforts are required to improve the estimation of frequency components, particularly during rest.

Proposing a convolutional neural network for stress assessment by means of derived heart rate from functional near infrared spectroscopy

BACKGROUND

Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals.

METHOD

In this study, a method based on the Convolutional Neural Network (CNN) approach is proposed to assess stress induced by the Montreal Imaging Stress Task. The proposed model is trained on the heart rate signal derived from functional Near-Infrared Spectroscopy (fNIRS), which is referred to as HRF. In this regard, fNIRS signals of 20 healthy volunteers were recorded using a configuration of 23 channels located on the prefrontal cortex. The proposed deep learning system consists of two main parts where in the first part, the one-dimensional convolutional neural network is employed to build informative activation maps, and then in the second part, a stack of deep fully connected layers is used to predict the stress existence probability. Thereafter, the employed CNN method is compared with the Dense Neural Network, Support Vector Machine, and Random Forest regarding various classification metrics.

RESULTS

Results clearly showed the superiority of CNN over all other methods. Additionally, the trained HRF model significantly outperforms the model trained on the filtered fNIRS signals, where the HRF model could achieve 98.69 ± 0.45% accuracy, which is 10.09% greater than the accuracy obtained by the fNIRS model.

CONCLUSIONS

Employment of the proposed deep learning system trained on the HRF measurements leads to higher stress classification accuracy than the accuracy reported in the existing studies where the same experimental procedure has been done. Besides, the proposed method suggests better stability with lower variation in prediction. Furthermore, its low computational cost opens up the possibility to be applied in real-time monitoring of stress assessment.

Atrial fibrillation detection from raw photoplethysmography waveforms: A deep learning application

Background

Atrial fibrillation (AF), a common cause of stroke, often is asymptomatic. Smartphones and smartwatches can detect AF using heart rate patterns inferred using photoplethysmography (PPG); however, enhanced accuracy is required to reduce false positives in screening populations.

Objective

The purpose of this study was to test the hypothesis that a deep learning algorithm given raw, smartwatch-derived PPG waveforms would discriminate AF from normal sinus rhythm better than algorithms using heart rate alone.

Methods

Patients presenting for cardioversion of AF (n = 51) were given wrist-worn fitness trackers containing PPG sensors (Jawbone Health). Standard 12-lead electrocardiograms over-read by board-certified cardiac electrophysiologists were used as the reference standard. The accuracy of PPG signals to discriminate AF from sinus rhythm was evaluated by conventional measures of heart rate variability, a long short-term memory (LSTM) neural network given heart rate data only, and a deep convolutional-recurrent neural net (DNN) given the raw PPG data.

Results

From among 51 patients with persistent AF (age 63.6 ± 11.3 years; 78% male; 88% white), we randomly assigned 40 to train and 11 to test the algorithms. Whereas logistic regression analysis of heart rate variability yielded an area under the receiver operating characteristic curve (AUC) of 0.717 (sensitivity 0.741; specificity 0.584), the LSTM model given heart rate data exhibited AUC of 0.954 (sensitivity 0.810; specificity 0.921), and the DNN model given raw PPG data yielded the highest AUC of 0.983 (sensitivity 0.985; specificity 0.880).

Conclusion

A deep learning model given the raw PPG-based signal resulted in AF detection with high accuracy, performing better than conventional analyses relying on heart rate series data alone.

Photoplethysmographic Time-Domain Heart Rate Measurement Algorithm for Resource-Constrained Wearable Devices and its Implementation

This paper presents an algorithm for the measurement of the human heart rate, using photoplethysmography (PPG), i.e., the detection of the light at the skin surface. The signal from the PPG sensor is processed in time-domain; the peaks in the preprocessed and conditioned PPG waveform are detected by using a peak detection algorithm to find the heart rate in real time. Apart from the PPG sensor, the accelerometer is also used to detect body movement and to indicate the moments in time, for which the PPG waveform can be unreliable. This paper describes in detail the signal conditioning path and the modified algorithm, and it also gives an example of implementation in a resource-constrained wrist-wearable device. The algorithm was evaluated by using the publicly available PPG-DaLia dataset containing samples collected during real-life activities with a PPG sensor and accelerometer and with an ECG signal as ground truth. The quality of the results is comparable to the other algorithms from the literature, while the required hardware resources are lower, which can be significant for wearable applications.

A Wearable RF Sensor for Monitoring Respiratory Patterns

We present a non-invasive approach for continuous monitoring of respiration dynamics using a wearable radio-frequency (RF) sensor based on near-field coherent sensing. A continuous-wave RF signal at 1.8 GHz is generated by a software-defined radio, with both transmitter (Tx) and receiver (Rx) antennas placed close to the xiphoid process. The experimental prototype of the mobile sensor can modulate the internal organ motion in the near-field region of the Tx antenna and is then received by the nearby Rx antenna to be demodulated and sampled. Through peak detection, we have identified inhalation and exhalation peaks of each breath cycle to estimate the breath rate and the lung volume. The extracted respiratory parameters are compared with the conventional chest belts data for various simulated respiratory conditions including voluntary deep, fast-shallow and slow-shallow breathing. We also characterized simulated central sleep apneas, Cheyne-Stokes, Biot's, ataxic and coughing conditions. To accurately identify obstructive apnea, we presented a two-sensor approach that can capture paradoxical movement of thorax and abdomen. The on-line recognition of these respiratory patterns can be employed not only to continuously monitor patients with chronic respiratory disorders but also to provide real-time feedback for future therapeutic purposes.

New physiological bench test reproducing nocturnal breathing pattern of patients with sleep disordered breathing

Previous studies have shown that Automatic Positive Airway Pressure devices display different behaviors when connected to a bench using theoretical respiratory cycle scripts. However, these scripts are limited and do not simulate physiological behavior during the night. Our aim was to develop a physiological bench that is able to simulate patient breathing airflow by integrating polygraph data. We developed an algorithm analyzing polygraph data and transformed this information into digital inputs required by the bench hardware to reproduce a patient breathing profile on bench. The inputs are respectively the simulated respiratory muscular effort pressure input for an artificial lung and the sealed chamber pressure to regulate the Starling resistor. We did simulations on our bench for a total of 8 hours and 59 minutes for a breathing profile from the demonstration recording of a Nox T3 Sleep Monitor. The simulation performance results showed that in terms of relative peak-valley amplitude of each breathing cycle, simulated bench airflow was biased by only 1.48% ± 6.80% compared to estimated polygraph nasal airflow for a total of 6,479 breathing cycles. For total respiratory cycle time, the average bias ± one standard deviation was 0.000 ± 0.288 seconds. For patient apnea events, our bench simulation had a sensitivity of 84.7% and a positive predictive value equal to 90.3%, considering 149 apneas detected both in polygraph nasal simulated bench airflows. Our new physiological bench would allow personalizing APAP device selection to each patient by taking into account individual characteristics of a sleep breathing profile.

Simulation and in vivo investigation of light-emitting diode, near infrared Gaussian beam profiles

Near infrared spectroscopy is an optical imaging technique which offers a non-invasive, portable, and low-cost method for continuously measuring the oxygenation of tissues. In particular, it can provide the brain activation through measuring the blood oxygenation and blood volume in the cortex. Understanding and then improving the spatial and depth sensitivity of near infrared spectroscopy measurements to brain tissue are essential for designing experiments as well as interpreting research findings. In this study, we investigate the effect of applying two common light beam profiles including Uniform and Gaussian on the penetration depth of an LED-based near infrared spectroscopy. In this regard, two Gaussian profiles were produced by adjusting plano-convex and bi-convex lenses and the Uniform profile was provided by applying a flat lens. Two experiments were conducted in this study. First, a simulation experiment was carried out based on scanning the intra space of a liquid phantom by using static and pulsating absorbers to compare the penetration depth of the configurations applied on the LED-based near infrared spectroscopy with that of a laser-based near infrared spectroscopy. Second, to show the feasibility of the best proposed configuration applied, an in vivo experiment of stress assessment has been performed and its results have been compared with that results obtained by laser one. The results showed that the LED-based near infrared spectroscopy equipped with bi-convex lens provides a penetration depth and hence quality measurements of near infrared spectroscopy and its extracted heart rate variability signals as well as laser-based near infrared spectroscopy especially in the application of stress assessment.

Methods for Gait Analysis During Obstacle Avoidance Task

In this study, we present algorithms developed for gait analysis, but suitable for many other signal processing tasks. A novel general-purpose algorithm for extremum estimation of quasi-periodic noisy signals is proposed. This algorithm is both flexible and robust, and allows custom adjustments to detect a predetermined wave pattern while being immune to signal noise and variability. A method for signal segmentation was also developed for analyzing kinematic data recorded while performing on obstacle avoidance task. The segmentation allows detecting preparation and recovery phases related to obstacle avoidance. A simple kernel-based clustering method was used for classification of unsupervised data containing features of steps within the walking trial and discriminating abnormal from regular steps. Moreover, a novel algorithm for missing data approximation and adaptive signal filtering is also presented. This algorithm allows restoring faulty data with high accuracy based on the surrounding information. In addition, a predictive machine learning technique is proposed for supervised multiclass labeling with non-standard label structure.

A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating

Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that is, high-frequency precordial vibrations and low-frequency respiratory movements for prospective gating in positron emission tomography (PET), computed tomography (CT), and radiotherapy. Our sensing modality in the context of this paper is a single dual sensor unit, including accelerometer and gyroscope sensors to measure chest movements in three different orientations. Since accelerometer- and gyroscope-derived respiration signals represent the inclination of the chest, they are similar in morphology and have the same units. Therefore, we use principal component analysis (PCA) to combine them into a single signal. In contrast to this, the accelerometer- and gyroscope-derived cardiac signals correspond to the translational and rotational motions of the chest, and have different waveform characteristics and units. To combine these signals, we use independent component analysis (ICA) in order to obtain the underlying cardiac motion. From this cardiac motion signal, we obtain the systolic and diastolic phases of cardiac cycles by using an adaptive multi-scale peak detector and a short-time autocorrelation function. Three groups of subjects, including healthy controls (n = 7), healthy volunteers (n = 12), and patients with a history of coronary artery disease (n = 19) were studied to establish a quantitative framework for assessing the performance of the presented work in prospective imaging applications. The results of this investigation showed a fairly strong positive correlation (average r = 0.73 to 0.87) between the MEMS-derived (including corresponding PCA fusion) respiration curves and the reference optical camera and respiration belt sensors. Additionally, the mean time offset of MEMS-driven triggers from camera-driven triggers was 0.23 to 0.3 ± 0.15 to 0.17 s. For each cardiac cycle, the feature of the MEMS signals indicating a systolic time interval was identified, and its relation to the total cardiac cycle length was also reported. The findings of this study suggest that the combination of chest angular velocity and accelerations using ICA and PCA can help to develop a robust dual cardiac and respiratory gating solution using only MEMS sensors. Therefore, the methods presented in this paper should help improve predictions of the cardiac and respiratory quiescent phases, particularly with the clinical patients. This study lays the groundwork for future research into clinical PET/CT imaging based on dual inertial sensors.

Intelligent Framework for Learning Physics with Aikido (Martial Art) and Registered Sensors

Physics is considered a tough academic subject by learners. To leverage engagement in the learning of this STEM area, teachers try to come up with creative ideas about the design of their classroom lessons. Sports-related activities can foster intuitive knowledge about physics (gravity, speed, acceleration, etc.). In this context, martial arts also provide a novel way of visualizing these ideas when performing the predefined motions needed to master the associated techniques. The recent availability of cheap monitoring hardware (accelerometers, cameras, etc.) allows an easy tracking of the aforementioned movements, which in the case of aikido, usually involve genuine circular motions. In this paper, we begin by reporting a user study among high-school students showing that the physics concept of moment of inertia can be understood by watching live exhibitions of specific aikido techniques. Based on these findings, we later present Phy + Aik, a tool for educators that enables the production of innovative visual educational material consisting of high-quality videos (and live demonstrations) synchronized/tagged with the inertial data collected by sensors and visual tracking devices. We think that a similar approach, where sensors are automatically registered within an intelligent framework, can be explored to teach other difficult-to-learn STEM concepts.

Influence of acquisition frame-rate and video compression techniques on pulse-rate variability estimation from vPPG signal

In this paper, common time- and frequency-domain variability indexes obtained by pulse rate variability (PRV) series extracted from video-photoplethysmographic signal (vPPG) were compared with heart rate variability (HRV) parameters calculated from synchronized ECG signals. The dual focus of this study was to analyze the effect of different video acquisition frame-rates starting from 60 frames-per-second (fps) down to 7.5 fps and different video compression techniques using both lossless and lossy codecs on PRV parameters estimation. Video recordings were acquired through an off-the-shelf GigE Sony XCG-C30C camera on 60 young, healthy subjects (age 23±4 years) in the supine position. A fully automated, signal extraction method based on the Kanade-Lucas-Tomasi (KLT) algorithm for regions of interest (ROI) detection and tracking, in combination with a zero-phase principal component analysis (ZCA) signal separation technique was employed to convert the video frames sequence to a pulsatile signal. The frame-rate degradation was simulated on video recordings by directly sub-sampling the ROI tracking and signal extraction modules, to correctly mimic videos recorded at a lower speed. The compression of the videos was configured to avoid any frame rejection caused by codec quality leveling, FFV1 codec was used for lossless compression and H.264 with variable quality parameter as lossy codec. The results showed that a reduced frame-rate leads to inaccurate tracking of ROIs, increased time-jitter in the signals dynamics and local peak displacements, which degrades the performances in all the PRV parameters. The root mean square of successive differences (RMSSD) and the proportion of successive differences greater than 50 ms (PNN50) indexes in time-domain and the low frequency (LF) and high frequency (HF) power in frequency domain were the parameters which highly degraded with frame-rate reduction. Such a degradation can be partially mitigated by up-sampling the measured signal at a higher frequency (namely 60 Hz). Concerning the video compression, the results showed that compression techniques are suitable for the storage of vPPG recordings, although lossless or intra-frame compression are to be preferred over inter-frame compression methods. FFV1 performances are very close to the uncompressed (UNC) version with less than 45% disk size. H.264 showed a degradation of the PRV estimation directly correlated with the increase of the compression ratio.

Clinical assessment of a non-invasive wearable MEMS pressure sensor array for monitoring of arterial pulse waveform, heart rate and detection of atrial fibrillation

There is an unmet clinical need for a low cost and easy to use wearable devices for continuous cardiovascular health monitoring. A flexible and wearable wristband, based on microelectromechanical sensor (MEMS) elements array was developed to support this need. The performance of the device in cardiovascular monitoring was investigated by (i) comparing the arterial pressure waveform recordings to the gold standard, invasive catheter recording (n = 18), (ii) analyzing the ability to detect irregularities of the rhythm (n = 7), and (iii) measuring the heartrate monitoring accuracy (n = 31). Arterial waveforms carry important physiological information and the comparison study revealed that the recordings made with the wearable device and with the gold standard device resulted in almost identical (r = 0.9–0.99) pulse waveforms. The device can measure the heart rhythm and possible irregularities in it. A clustering analysis demonstrates a perfect classification accuracy between atrial fibrillation (AF) and sinus rhythm. The heartrate monitoring study showed near perfect beat-to-beat accuracy (sensitivity = 99.1%, precision = 100%) on healthy subjects. In contrast, beat-to-beat detection from coronary artery disease patients was challenging, but the averaged heartrate was extracted successfully (95% CI: −1.2 to 1.1 bpm). In conclusion, the results indicate that the device could be useful in remote monitoring of cardiovascular diseases and personalized medicine.

Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.

Development of an innovative technology to segment luminal borders of intravascular ultrasound image sequences in a fully automated manner

Although intravascular ultrasound (IVUS) is the commonest intravascular imaging modality, it still is inefficient for clinical use as it requires laborious manual analysis. This study demonstrates the feasibility of a near real-time fully automated technology for accurate identification, detection, and quantification of luminal borders in intravascular images. This technology uses a combination of the novel approaches of a self-tuning engine, dynamic and static masking systems, radar-wise scan, and contour correction cycle method. The performance of the computer algorithm developed based on this technology was tested on a sequence of IVUS and True Vessel Characterization (TVC) images obtained from the left anterior descending (LAD) artery of 6 patients with coronary artery disease. The accuracy of the algorithm was evaluated by comparing luminal borders traced manually with those detected automatically. The processing time of the developed algorithm was also tested on a Dell laptop with an Intel Core i7-8750H Processor (4.1 GHz with 6 cores, 9 MB Cache). Linear regression and Bland-Altman analyses indicated high correlation between manual and automatic tracings (Y = 0.80 × X+1.70, R² = 0.88 & 0.67 ± 1.31 (bias±SD)). Whereas analysis of 2000 IVUS images using one CPU core with a 30% load took 23.12 min, the same analysis using six CPU cores with 90% load took 1.0 min. The performance, accuracy, and speed of the presented state-of-the-art technology demonstrates its capacity for use in clinical settings.

Objective assessment of visual acuity: a refined model for analyzing the sweep VEP

PURPOSE

The aim of this study was to develop a simple and reliable method for the objective assessment of visual acuity by optimizing the stimulus used in commercially available systems and by improving the methods of evaluation using a nonlinear function, the modified Ricker model.

METHODS

Subjective visual acuity in the normal subjects was measured with Snellen targets, best-corrected, and in some cases also uncorrected and with plus lenses (+ 1 D, + 2 D, + 3 D). In patients, subjective visual acuity was measured best-corrected using the Freiburg Visual Acuity Test. Sweep VEP recordings to 11 spatial frequencies, with check sizes in logarithmically equidistant steps (0.6, 0.9, 1.4, 2.1, 3.3, 4.9, 7.3, 10.4, 18.2, 24.4, and 36.5 cpd), were obtained from 56 healthy subjects aged between 17 and 69 years (mean 42.5 ± 15.3 SD years) and 20 patients with diseases of the lens (n = 6), retina (n = 8) or optic nerve (n = 6). The results were fit by a multiple linear regression (2nd-order polynomial) or a nonlinear regression (modified Ricker model) and parameters compared (limiting spatial frequency (sf_limiting) and the spatial frequency of the vertex (sf_vertex) of the parabola for the 2nd-order polynomial fitting, and the maximal spatial frequency (sf_max), and the spatial frequency where the amplitude is 2 dB higher than the level of noise (sf_threshold) for the modified Ricker model.

RESULTS

Recording with 11 spatial frequencies allows a more accurate determination of acuities above 1.0 logMAR. Tuning curves fitted to the results show that compared to the normal 2nd-order polynomial analysis, the modified Ricker model is able to describe closely the amplitudes of the sweep VEP in relation to the spatial frequencies of the presented checkerboards. In patients with a visual acuity better than about 0.5 (decimal), the predicted acuities based on the different parameters show a good match of the predicted visual acuities based on the models established in healthy volunteers to the subjective visual acuities. However, for lower visual acuities, both models tend to overestimate the visual acuity (up to ~ 0.4 logMAR), especially in patients suffering from AMD.

CONCLUSIONS

Both models, the 2nd-order polynomial and the modified Ricker model performed equally well in the prediction of the visual acuity based on the amplitudes recorded using the sweep VEP. However, the modified Ricker model does not require the exclusion of data points from the fit, as necessary when fitting the 2nd-order polynomial model making it more reliable and robust against outliers, and, in addition, provides a measure for the noise of the recorded results.

Stress assessment by means of heart rate derived from functional near-infrared spectroscopy

Many studies have been carried out in order to detect and quantify the level of mental stress by means of different physiological signals. From the physiological point of view, stress promptly affects brain and cardiac function; therefore, stress can be assessed by analyzing the brain- and heart-related signals more efficiently. Signals produced by functional near-infrared spectroscopy (fNIRS) of the brain together with the heart rate (HR) are employed to assess the stress induced by the Montreal Imaging Stress Task. Two different versions of the HR are used in this study. The first one is the commonly used HR derived from the electrocardiogram (ECG) and is considered as the reference HR (RHR). The other is the HR computed from the fNIRS signal (EHR) by means of an effective combinational algorithm. fNIRS and ECG signals were simultaneously recorded from 10 volunteers, and EHR and RHR are derived from them, respectively. Our results showed a high degree of agreement [r  >  0.9, BAR (Bland Altman ratio) <5  %  ] between the two HR. A principal component analysis/support vector machine-based algorithm for stress classification is developed and applied to the three measurements of fNIRS, EHR, and RHR and a classification accuracy of 78.8%, 94.6%, and 62.2% were obtained for the three measurements, respectively. From these observations, it can be concluded that the EHR carries more useful information with regards to the mental stress than the RHR and fNIRS signals. Therefore, EHR can be used alone or in combination with the fNIRS signal for a more accurate and real-time stress detection and classification.

Pulse Ultrasonic Cure Monitoring of the Pultrusion Process

This article discusses the results of a series of experiments on pulse ultrasonic cure monitoring of carbon fiber reinforced plastics applied to the pultrusion process. The aim of this study is to validate the hypothesis that pulse ultrasonic cure monitoring can be applied (a) for profiles having small cross sections such as 7 mm × 0.5 mm and (b) within the environment of the pultrusion process. Ultrasonic transducers are adhesively bonded to the pultrusion tool as actuators and sensors. The time-of-flight and the amplitude of an ultrasonic wave are analyzed to deduce the current curing state of the epoxy matrix. The experimental results show that ultrasonic cure monitoring is indeed applicable even to very thin cross sections. However, significant challenges can be reported when the techniques are used during the pultrusion process.

The complex analysis of X-ray mesh scans for macromolecular crystallography

A method and software program, MeshBest, for the detection of individual crystals based on two-dimensional X-ray mesh scans are presented.

In macromolecular crystallography, mesh (raster) scans are carried out either as part of X-ray-based crystal-centring routines or to identify positions on the sample holder from which diffraction images can be collected. Here, the methods used in MeshBest, software which automatically analyses diffraction images collected during a mesh scan and produces a two-dimensional crystal map showing estimates of the dimensions, centre positions and diffraction qualities of each crystal contained in the mesh area, are presented. Sample regions producing diffraction images resulting from the superposition of more than one crystal are also distinguished from regions with single-crystal diffraction. The applicability of the method is demonstrated using several cases.

WeAllWalk

We introduce WeAllWalk, a dataset of inertial sensor time series collected from blind and sighted walkers using a long cane or a guide dog. Ten blind volunteers (seven using a long cane, one using a guide dog, and two alternating use of a long cane and of a guide dog) as well as five sighted volunteers contributed to the data collection. The participants walked through fairly long and complex indoor routes that included obstacles to be avoided and doors to be opened. Inertial data were recorded by two iPhone 6s carried by our participants in their pockets and carefully annotated. Ground-truth heel strike times were measured by two small inertial sensor units clipped to the participants’ shoes. We also present an in-depth comparative analysis of various step counting and turn detection algorithms as tested on WeAllWalk. This analysis reveals interesting differences between the achievable accuracy of step and turn detection across different communities of sighted and blind walkers.

Functional video-based analysis of 3D cardiac structures generated from human embryonic stem cells

Human embryonic stem cells (hESCs) differentiated into cardiomyocytes (CM) often develop into complex 3D structures that are composed of various cardiac cell types. Conventional methods to study the electrophysiology of cardiac cells are patch clamp and microelectrode array (MEAs) analyses. However, these methods are not suitable to investigate the contractile features of 3D cardiac clusters that detach from the surface of the culture dishes during differentiation. To overcome this problem, we developed a video-based motion detection software relying on the optical flow by Farnebäck that we call cBRA (cardiac beat rate analyzer). The beating characteristics of the differentiated cardiac clusters were calculated based on the local displacement between two subsequent images. Two differentiation protocols, which profoundly differ in the morphology of cardiac clusters generated and in the expression of cardiac markers, were used and the resulting CM were characterized. Despite these differences, beat rates and beating variabilities could be reliably determined using cBRA. Likewise, stimulation of β-adrenoreceptors by isoproterenol could easily be identified in the hESC-derived CM. Since even subtle changes in the beating features are detectable, this method is suitable for high throughput cardiotoxicity screenings.

Multi-Scale Peak and Trough Detection Optimised for Periodic and Quasi-Periodic Neuroscience Data

OBJECTIVES

The reliable detection of peaks and troughs in physiological signals is essential to many investigative techniques in medicine and computational biology. Analysis of the intracranial pressure (ICP) waveform is a particular challenge due to multi-scale features, a changing morphology over time and signal-to-noise limitations. Here we present an efficient peak and trough detection algorithm that extends the scalogram approach of Scholkmann et al., and results in greatly improved algorithm runtime performance.

MATERIALS AND METHODS

Our improved algorithm (modified Scholkmann) was developed and analysed in MATLAB R2015b. Synthesised waveforms (periodic, quasi-periodic and chirp sinusoids) were degraded with white Gaussian noise to achieve signal-to-noise ratios down to 5 dB and were used to compare the performance of the original Scholkmann and modified Scholkmann algorithms.

RESULTS

The modified Scholkmann algorithm has false-positive (0%) and false-negative (0%) detection rates identical to the original Scholkmann when applied to our test suite. Actual compute time for a 200-run Monte Carlo simulation over a multicomponent noisy test signal was 40.96 ± 0.020 s (mean ± 95%CI) for the original Scholkmann and 1.81 ± 0.003 s (mean ± 95%CI) for the modified Scholkmann, demonstrating the expected improvement in runtime complexity from [Formula: see text] to [Formula: see text].

CONCLUSIONS

The accurate interpretation of waveform data to identify peaks and troughs is crucial in signal parameterisation, feature extraction and waveform identification tasks. Modification of a standard scalogram technique has produced a robust algorithm with linear computational complexity that is particularly suited to the challenges presented by large, noisy physiological datasets. The algorithm is optimised through a single parameter and can identify sub-waveform features with minimal additional overhead, and is easily adapted to run in real time on commodity hardware.

Non-neuronal evoked and spontaneous hemodynamic changes in the anterior temporal region of the human head may lead to misinterpretations of functional near-infrared spectroscopy signals

Several functional near-infrared spectroscopy (fNIRS) studies report their findings based on changes of a single chromophore, usually concentration changes of oxygenated hemoglobin ([[Formula: see text]]) or deoxygenated hemoglobin (HHb). However, influence of physiological actions may differ depending on which element is considered and the assumption that the chosen measure correlates with the neural response of interest might not hold. By assessing the correlation between [[Formula: see text]] and [HHb] in task-evoked activity as well as resting-state data, we identified a spatial dependency of non-neuronal hemodynamic changes in the anterior temporal region of the human head. Our findings support the importance of reporting and discussing fNIRS outcomes obtained with both chromophores ([[Formula: see text]] and [HHb]), in particular, for studies concerning the anterior temporal region of the human head. This practice should help to achieve a physiologically correct interpretation of the results when no measurements with short-distance channels are available while employing continuous-wave fNIRS systems.

Effect of short-term colored-light exposure on cerebral hemodynamics and oxygenation, and systemic physiological activity

There is not yet a comprehensive view of how the color of light affects the cerebral and systemic physiology in humans. The aim was to address this deficit through basic research. Since cerebral and systemic physiological parameters are likely to interact, it was necessary to establish an approach, which we have termed "systemic-physiology-augmented functional near-infrared spectroscopy (SPA-fNIRS) neuroimaging." This multimodal approach measures the systemic and cerebral physiological response to exposure to light of different colors. In 14 healthy subjects (9 men, 5 women, age: [Formula: see text] years, range: 24 to 57 years) exposed to red, green, and blue light (10-min intermittent wide-field visual color stimulation; [Formula: see text] blocks of visual stimulation), brain hemodynamics and oxygenation were measured by fNIRS on the prefrontal cortex (PFC) and visual cortex (VC) simultaneously, in addition with systemic parameters. This study demonstrated that (i) all colors elicited responses in the VC, whereas only blue evoked a response in the PFC; (ii) there was a color-dependent effect on cardiorespiratory activity; (iii) there was significant change in neurosystemic functional connectivity; (iv) cerebral hemodynamic responses in the PFC and changes in the cardiovascular system were gender and age dependent; and (v) electrodermal activity and psychological state showed no stimulus-evoked changes, and there was no dependence on color of light, age, and gender. We showed that short-term light exposure caused color-dependent responses in cerebral hemodynamics/oxygenation as well as cardiorespiratory dynamics. Additionally, we showed that neurosystemic functional connectivity changes even during apparently stress-free tasks-an important consideration when using any of the hemodynamic neuroimaging methods (e.g. functional magnetic resonance imaging, positron emission tomography, and fNIRS). Our findings are important for future basic research and clinical applications as well as being relevant for everyday life.

Identification and annotation of multiple periodic pulse trains using dominant frequency and graph search: Applications in atrial fibrillation rotor detection

Biological signals, such as intracardiac electrograms during atrial fibrillation (AF), can contain multiple periodic components or peaks. We propose a method for identifying individual periodic peak trains in signals containing multiple such periodic sequences. We use dominant frequency-based periodicity detection along with a graph search algorithm to identify the most dominant periodic activation set or peaks of interest. We then remove these peaks and iterate until all periodic sequences are identified. The proposed method is tested on simulated AF intra-cardiac electrograms with periodic activation trains of three distinct frequencies corrupted by noise and complex aperiodic signal features. The method is shown to have high accuracy (up to 100% sensitivity and 100% specificity) in detecting the three individual periodic peak trains. The method has application in biomedical signal analysis, such as detecting the periodic activations of a rotor, amidst other periodic activations during AF.

An algorithm for rotor tracking in atrial fibrillation using graph search-based periodic peak detection

Rotors are rotating electrical waves that may sustain atrial fibrillation (AF); thereby providing therapeutic targets for catheter ablation. We propose a method for identifying rotors from circular catheter recordings of bipolar intracardiac electrograms (EGM) during AF. We use dominant frequency-based periodicity detection along with a graph search algorithm to identify the most dominant periodic activations or peaks of interest in each bipolar EGM recorded by a multipolar circular catheter. We then track the activations across catheter bipoles to determine whether they conform to the rotational pattern of a rotor. The performance of the proposed method is tested on simulated bipolar EGM arrays containing rotor activation corrupted by noise and complex aperiodic signal features. The method is shown to perform with high accuracy (up to 98% sensitivity and 100% specificity) in detecting simulated rotors and may serve to guide rotor ablation in patients with AF.

A study of nano-biosensors and their output amplitude analysis algorithms

The aim of this paper is to discuss the latest nano-biosensor technologies and existing signal analyser algorithm methods so that an automatic and portable nano-biosensor analyser can be realised. In this paper, the latest nano-biosensors are reviewed, and particular attention is given to sensors that provide amplitude changes at their output. To provide an automatic signal analysis of these changes, existing signal processing algorithms for peak detection are also discussed in detail.

Revealing aperiodic aspects of solenoid proteins from sequence information

MOTIVATION

Repeat proteins, which contain multiple repeats of short sequence motifs, form a large but seldom-studied group of proteins. Methods focusing on the analysis of 3D structures of such proteins identified many subtle effects in length distribution of individual motifs that are important for their functions. However, similar analysis was yet not applied to the vast majority of repeat proteins with unknown 3D structures, mostly because of the extreme diversity of the underlying motifs and the resulting difficulty to detect those.

RESULTS

We developed FAIT, a sequence-based algorithm for the precise assignment of individual repeats in repeat proteins and introduced a framework to classify and compare aperiodicity patterns for large protein families. FAIT extracts repeat positions by post-processing FFAS alignment matrices with image processing methods. On examples of proteins with Leucine Rich Repeat (LRR) domains and other solenoids like proteins, we show that the automated analysis with FAIT correctly identifies exact lengths of individual repeats based entirely on sequence information.

AVAILABILITY AND IMPLEMENTATION

https://github.com/GodzikLab/FAIT CONTACT: adam@godziklab.org

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Distribution of Response Time, Cortical, and Cardiac Correlates during Emotional Interference in Persons with Subclinical Psychotic Symptoms

A psychosis phenotype can be observed below the threshold of clinical detection. The study aimed to investigate whether subclinical psychotic symptoms are associated with deficits in controlling emotional interference, and whether cortical brain and cardiac correlates of these deficits can be detected using functional near-infrared spectroscopy (fNIRS). A data set derived from a community sample was obtained from the Zurich Program for Sustainable Development of Mental Health Services. 174 subjects (mean age 29.67 ± 6.41, 91 females) were assigned to four groups ranging from low to high levels of subclinical psychotic symptoms (derived from the Symptom Checklist-90-R). Emotional interference was assessed using the emotional Stroop task comprising neutral, positive, and negative conditions. Statistical distributional methods based on delta plots [behavioral response time (RT) data] and quantile analysis (fNIRS data) were applied to evaluate the emotional interference effects. Results showed that both interference effects and disorder-specific (i.e., group-specific) effects could be detected, based on behavioral RTs, cortical hemodynamic signals (brain correlates), and heart rate variability (cardiac correlates). Subjects with high compared to low subclinical psychotic symptoms revealed significantly reduced amplitudes in dorsolateral prefrontal cortices (interference effect, p < 0.001) and middle temporal gyrus (disorder-specific group effect, p < 0.001), supported by behavioral and heart rate results. The present findings indicate that distributional analyses methods can support the detection of emotional interference effects in the emotional Stroop. The results suggested that subjects with high subclinical psychosis exhibit enhanced emotional interference effects. Based on these observations, subclinical psychosis may therefore prove to represent a valid extension of the clinical psychosis phenotype.

Short-term pulse rate variability is better characterized by functional near-infrared spectroscopy than by photoplethysmography

Pulse rate variability (PRV) can be extracted from functional near-infrared spectroscopy (fNIRS) (PRV(NIRS)) and photoplethysmography (PPG) (PRV(PPG)) signals. The present study compared the accuracy of simultaneously acquired PRV(NIRS) and PRV(PPG), and evaluated their different characterizations of the sympathetic (SNS) and parasympathetic (PSNS) autonomous nervous system activity. Ten healthy subjects were recorded during resting-state (RS) and respiratory challenges in two temperature conditions, i.e., room temperature (23°C) and cold temperature (4°C). PRV(NIRS) was recorded based on fNIRS measurement on the head, whereas PRV(PPG) was determined based on PPG measured at the finger. Accuracy between PRV(NIRS) and PRV(PPG), as assessed by cross-covariance and cross-sample entropy, demonstrated a high degree of correlation (r > 0.9), which was significantly reduced by respiration and cold temperature. Characterization of SNS and PSNS using frequency-domain, time-domain, and nonlinear methods showed that PRV(NIRS) provided significantly better information on increasing PSNS activity in response to respiration and cold temperature than PRV(PPG). The findings show that PRV(NIRS) may outperform PRV(PPG) under conditions in which respiration and temperature changes are present, and may, therefore, be advantageous in research and clinical settings, especially if characterization of the autonomous nervous system is desired.

Assessment of beat-to-beat heart rate detection method using a camera as contactless sensor

Video photoplethysmography (videoPPG) has emerged as area of great interest thanks to the possibility of remotely assessment of cardiovascular parameters, as heart rate (HR), respiration rate (RR) and heart rate variability (HRV). The present article proposes a fully automated method based on chrominance model, that selects for each subject the best region of interest (ROI) to detect and evaluate the accuracy of beat detection and interbeat intervals (IBI) measurements. The experimental recordings were conducted on 26 subjects which underwent a rest-to-stand maneuver. The results show that the accuracy of beat detection is slightly better during supine position (95%) compared to the standing one (92%), due to the maintenance of the balance that introduces larger motion artifact in the signal dynamic. The error in the measurement (expressed as mean±sd) of instantaneous heart rate is of +0.04 ±3.29 bpm in rest and +0.01±4.26 bpm in stand.

Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach

Heart rate variability (HRV) has become a marker for various health and disease conditions. Photoplethysmography (PPG) sensors integrated in wearable devices such as smart watches and phones are widely used to measure heart activities. HRV requires accurate estimation of time interval between consecutive peaks in the PPG signal. However, PPG signal is very sensitive to motion artefact which may lead to poor HRV estimation if false peaks are detected. In this Letter, the authors propose a probabilistic approach based on Bayesian learning to better estimate HRV from PPG signal recorded by wearable devices and enhance the performance of the automatic multi scale-based peak detection (AMPD) algorithm used for peak detection. The authors' experiments show that their approach enhances the performance of the AMPD algorithm in terms of number of HRV related metrics such as sensitivity, positive predictive value, and average temporal resolution.

Enhancing Sensitivity of a Miniature Spectrometer Using a Real-Time Image Processing Algorithm

A real-time image processing algorithm is developed to enhance the sensitivity of a planar single-mode waveguide miniature spectrometer with integrated waveguide gratings. A novel approach of averaging along the arcs in a curved coordinate system is introduced which allows for collecting more light, thereby enhancing the sensitivity. The algorithm is tested using CdSeS/ZnS quantum dots drop casted on the surface of a single-mode waveguide. Measurements indicate that a monolayer of quantum dots is expected to produce guided mode attenuation approximately 11 times above the noise level.