Highlights of 2017

Sport-related muscle injury classifications are based basically on imaging criteria such as ultrasound (US) and magnetic resonance imaging (MRI) without consensus because of a lack of clinical prognostics for return-to-play (RTP), which is conditioned upon the severity of the injury, and this in turn with the muscle gap (muscular fibers retraction). Recently, Futbol Club Barcelona's medical department proposed a new muscle injury classification in which muscle gap plays an important role, with the drawback that it is not always possible to identify by MRI. Localized bioimpedance measurement (L-BIA) has emerged as a non-invasive technique for supporting US and MRI to quantify the disrupted soft tissue structure in injured muscles. Objective: To correlate the severity of the injury according to the gap with the RTP, through the percent of change in resistance (R), reactance (Xc) and phase-angle (PA) by L-BIA measurements in 22 muscle injuries. Main results: After grouping the data according to the muscle gap (by MRI exam), there were significant differences in R between grade 1 and grade 2f (myotendinous or myofascial muscle injury with feather-like appearance), as well as between grade 2f and grade 2g (myotendinous or myofascial muscle injury with feather and gap). The Xc and PA values decrease significantly between each grade (i.e. 1 versus 2f, 1 versus 2g and 2f versus 2g). In addition, the severity of the muscle gap adversely affected the RTP with significant differences observed between 1 and 2g as well as between 2f and 2g. Significance: These results show that L-BIA could aid MRI and US in identifying the severity of an injured muscle according to muscle gap and therefore to accurately predict the RTP.

Objective: In this letter, we propose a novel method for classifying daily wrist activities by using a smart band. Approach: Triaxial acceleration data are collected by built-in sensors of the smart band during experiments regarding five activities, i.e. texting, calling, placing a hand in a pocket, carrying a suitcase, and swinging a hand. We analyze patterns in the sensor signals during these activities based on three types of features, i.e. norm, norm-variance, and frequency-domain features. After extracting the significant features, a multi-class support vector machine algorithm is applied to classify these activities. Main results: We obtained recognition error rates of approximately 2.7% by applying the proposed method to the experimental dataset.

Objective. Blood flow is one such available observable promoting a wealth of physiological insight both individually and in combination with other metrics.

Approach. Near-infrared diffuse correlation spectroscopy (DCS) and, to a lesser extent, diffuse correlation tomography (DCT), have increasingly received interest over the past decade as noninvasive methods for tissue blood flow measurements and imaging. DCS/DCT offers several attractive features for tissue blood flow measurements/imaging such as noninvasiveness, portability, high temporal resolution, and relatively large penetration depth (up to several centimeters).

Main results. This review first introduces the basic principle and instrumentation of DCS/DCT, followed by presenting clinical application examples of DCS/DCT for the diagnosis and therapeutic monitoring of diseases in a variety of organs/tissues including brain, skeletal muscle, and tumor.

Significance. Clinical study results demonstrate technical versatility of DCS/DCT in providing important information for disease diagnosis and intervention monitoring.

In a broad view, fatigue is used to indicate a degree of weariness. On a muscular level, fatigue posits the reduced capacity of muscle fibres to produce force, even in the presence of motor neuron excitation via either spinal mechanisms or electric pulses applied externally. Prior to decreased force, when sustaining physically demanding tasks, alterations in the muscle electrical properties take place. These alterations, termed myoelectric manifestation of fatigue, can be assessed non-invasively with a pair of surface electrodes positioned appropriately on the target muscle; traditional approach. A relatively more recent approach consists of the use of multiple electrodes. This multi-channel approach provides access to a set of physiologically relevant variables on the global muscle level or on the level of single motor units, opening new fronts for the study of muscle fatigue; it allows for: (i) a more precise quantification of the propagation velocity, a physiological variable of marked interest to the study of fatigue; (ii) the assessment of regional, myoelectric manifestations of fatigue; (iii) the analysis of single motor units, with the possibility to obtain information about motor unit control and fibre membrane changes. This review provides a methodological account on the multi-channel approach for the study of myoelectric manifestation of fatigue and on the experimental conditions to which it applies, as well as examples of their current applications.

Objective: Neurologists and physiatrists need improved tools for the evaluation of skeletal muscle condition. Here we evaluate needle electrical impedance myography (EIM), a new minimally invasive approach to determine muscle status that could ultimately become a bedside tool for the assessment of neuromuscular disorders. Approach: We design and study the recording characteristics of tetrapolar EIM needle electrodes combining theory and finite-element model simulations. We then use these results to build and pilot in vivo an EIM needle electrode in the rat gastrocnemius muscle ($n=2$). The dielectric properties of muscle are reported (mean  ±  standard deviation). Results: The numerical simulations show that the contribution of subcutaneous fat and muscle tissues to needle EIM data is <3% and >97%, respectively, and the sensed volume is $0.70$ cm³. Apparent resistivity $277\pm 30$ $\Omega$ cm and relative permittivity $(1\pm 0.4)\times 10^5$ (dimensionless) measured at 10 kHz are in good agreement with in vivo dielectric properties reported in the literature. Significance: The results presented show the feasibility of measuring muscle impedivity in vivo using a needle electrode from 10 kHz to 1 MHz. The development of needle EIM technology can open up a new field of study in electrodiagnostic medicine, with potential applications to both disease diagnosis and biomarker assessment of therapy.

Objective and Approach: Sepsis, a dysregulated immune-mediated host response to infection, is the leading cause of morbidity and mortality in critically ill patients. Indices of heart rate variability and complexity (such as entropy) have been proposed as surrogate markers of neuro-immune system dysregulation with diseases such as sepsis. However, these indices only provide an average, one dimensional description of complex neuro-physiological interactions. We propose a novel multiscale network construction and analysis method for multivariate physiological time series, and demonstrate its utility for early prediction of sepsis. Main results: We show that features derived from a multiscale heart rate and blood pressure time series network provide approximately 20% improvement in the area under the receiver operating characteristic (AUROC) for four-hour advance prediction of sepsis over traditional indices of heart rate entropy ($0.78$ versus $0.66$). Our results indicate that this improvement is attributable to both the improved network construction method proposed here, as well as the information embedded in the higher order interaction of heart rate and blood pressure time series dynamics. Our final model, which included the most commonly available clinical measurements in patients' electronic medical records and multiscale entropy features, as well as the proposed network-based features, achieved an AUROC of $0.80$. Significance: Prediction of the onset of sepsis prior to clinical recognition will allow for meaningful earlier interventions (e.g. antibiotic and fluid administration), which have the potential to decrease sepsis-related morbidity, mortality and healthcare costs.

Early identification of areas of inflammation may aid prevention of diabetic foot ulcers. A new bespoke thermal camera system has been developed to thermally image feet at risk. Hotspots (areas at least 2.2 °C hotter than the contralateral site) may indicate areas of inflammation prior to any apparent visual signs. This article describes the thermal pattern and symmetry of 103 healthy pairs of feet. 68% of participants were thermally symmetric at the 33 foot sites measured. 32% of participants had at least one hotspot, but hotspots overall only accounted for 5% of the measurements made. Refinements to the definition of hotspots are proposed when considering feet at risk of ulceration.

Monitoring surface electromyographic (EMG) signals can provide useful insights for characterizing muscle fatigue, which is defined as an exercise-induced strength loss. This experiment investigated the muscle fiber conduction velocity (CV) changes induced by an endurance run. The day before and immediately after a half-marathon run (21.097 km) 11 amateur runners performed maximum voluntary contractions (MVCs) of knee extensor muscles. During the MVC, multichannel EMG was recorded from the vastus lateralis and EMG amplitude and CV were calculated. After the run, knee extensors showed a decreased strength (−13  ±  9%, p  =  0.001) together with a reduction in EMG amplitude (−13  ±  10%, p  =  0.003) and in CV (−6  ±  8%, p  =  0.032). Knee extensor strength loss positively correlated with vastus lateralis CV differences (r  =  0.76, p  =  0.006). Thus, the exercises-induced muscle fatigue was associated not only with a decrease in EMG amplitude, but also with a reduction in CV. This finding suggests that muscle fibers with higher CV (i.e. those with greater fiber size) were the most impaired during strength production after an endurance run.

Objective: Recent evidence of gadolinium (Gd) deposition in bones of healthy individuals who have previously received Gd-based contrast agents (GBCAs) for MRI has led to a demand for in vivo measurement techniques. The technique of x-ray fluorescence provides a low risk and painless method to assess Gd deposition in bone, and has the potential to be a useful clinical tool. However, interpatient variability creates a challenge while performing in vivo measurements. Approach: We explored the use of coherent normalization, which involves normalizing the Gd K x-rays to the coherent scattered γ-ray from the excitation source, for bone Gd measurements through a series of phantom-based experiments and Monte Carlo simulations. Main results: We found coherent normalization is able to correct for variation in overlying tissue thickness over a wide range (0–12.2 mm). The Gd signal to coherent signal ratio is independent of tissue thickness for both experiments and Monte Carlo simulations. Significance: Coherent normalization has been demonstrated to be used in practice with normal healthy adults to improve in vivo bone Gd measurements.

Objective: Thoracic trauma is one of the most common and lethal types of injury, causing over a quarter of traumatic deaths. Severe thoracic injuries are often occult and difficult to diagnose in the field. There is a need for a point-of-care diagnostic device for severe thoracic injuries in the prehospital setting. Electrical bioimpedance (EBI) is non-invasive, portable, rapid and easy to use technology that can provide objective and quantitative diagnostic information for the prehospital environment. Here, we evaluated the performance of EBI to detect thoracic injuries. Approach: In this open study, EBI resistance (R), reactance (X) and phase angle (PA) of both sides of the thorax were measured at 50 kHz on patients suffering from thoracic injuries (n  =  20). In parallel, a control group consisting of healthy subjects (n  =  20) was recruited. A diagnostic mathematical algorithm, fed with input parameters derived from EBI data, was designed to differentiate patients from healthy controls. Main results: Ratios between the X and PA measurements of both sides of the thorax were significantly different (p  <  0.05) between healthy volunteers and patients with left- and right-sided injuries. The diagnostic algorithm achieved a performance evaluated by leave-one-out cross-validation analysis and derived area under the receiver operating characteristic curve of 0.88. Significance: A diagnostic algorithm that accurately discriminates between patients suffering thoracic injuries and healthy subjects was designed using EBI technology. A larger, prospective and blinded study is thus warranted to validate the feasibility of EBI technology as a prehospital tool.

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r  =  0.71–0.88, RMSE: 1.11–1.61 METs; p  >  0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN—correlations: r  =  0.89, RMSE: 1.07–1.08 METs. Linear models—correlations: r  =  0.88, RMSE: 1.10–1.11 METs; p  >  0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN—correlation: r  =  0.88, RMSE: 1.12 METs. Linear models—correlations: r  =  0.86, RMSE: 1.18–1.19 METs; p  <  0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN—correlations: r  =  0.82–0.84, RMSE: 1.26–1.32 METs. Linear models—correlations: r  =  0.71–0.73, RMSE: 1.55–1.61 METs; p  <  0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

The wrist is increasingly being used as the preferred site for objectively assessing physical activity but the relative accuracy of processing methods for wrist data has not been determined. Objective: This study evaluates the validity of four processing methods for wrist-worn ActiGraph (AG) data against energy expenditure (EE) measured using a portable metabolic analyzer (OM; Oxycon mobile) and the Compendium of physical activity. Approach: Fifty-one adults (ages 18–40) completed 15 activities ranging from sedentary to vigorous in a laboratory setting while wearing an AG and the OM. Estimates of EE and categorization of activity intensity were obtained from the AG using a linear method based on Hildebrand cutpoints (HLM), a non-linear modification of this method (HNLM), and two methods developed by Staudenmayer based on a Linear Model (SLM) and using random forest (SRF). Estimated EE and classification accuracy were compared to the OM and Compendium using Bland–Altman plots, equivalence testing, mean absolute percent error (MAPE), and Kappa statistics. Main results: Overall, classification agreement with the Compendium was similar across methods ranging from a Kappa of 0.46 (HLM) to 0.54 (HNLM). However, specificity and sensitivity varied by method and intensity, ranging from a sensitivity of 0% (HLM for sedentary) to a specificity of ~99% for all methods for vigorous. None of the methods was significantly equivalent to the OM (p  >  0.05). Significance: Across activities, none of the methods evaluated had a high level of agreement with criterion measures. Additional research is needed to further refine the accuracy of processing wrist-worn accelerometer data.

Objective. Heart rate variability (HRV) characterizes changes in autonomic nervous system function and varies with posttraumatic stress disorder (PTSD). In this study we developed a classifier based on heart rate (HR) and HRV measures, and improved classifier performance using a novel HR-based window segmentation.

Approach. Single-channel ECG data were collected from 23 subjects with current PTSD, and 25 control subjects with no history of PTSD over 24 h. RR intervals were derived from these data, cleaned, and used to calculate HR and HRV metrics. These metrics were used as features in a logistic regression classifier. Performance was assessed via repeated random sub-sampling validation. To reduce noise and activity-related effects, we calculated features from five non-overlapping ten-minute quiescent segments of RR intervals defined by lowest HR, as well as random ten-minute segments as a control.

Main Results. Using a combination of the four most predictive features derived from quiescent segments we achieved a median area under the receiver operating curve (AUC) of 0.86 on out-of-sample test set data. This was significantly higher than the AUC using 24 h of data (0.72) or random segments (0.67).

Significance. These results demonstrate our segmentation approach improves the classification of PTSD from HR and HRV measures, and suggest the potential for tracking PTSD illness severity via objective physiological monitoring. Future studies should prospectively evaluate if classifier output changes significantly with worsening or effective treatment of PTSD.

Objective: A novel single-lead f-wave extraction algorithm based on the modern diffusion geometry data analysis framework is proposed. Approach: The algorithm is essentially an averaged beat subtraction algorithm, where the ventricular activity template is estimated by combining a newly designed metric, the 'diffusion distance', and the non-local Euclidean median based on the non-linear manifold setup. We coined the algorithm $\texttt{DD-NLEM}$. Main results: Two simulation schemes are considered, and the new algorithm $\texttt{DD-NLEM}$ outperforms traditional algorithms, including the average beat subtraction, principal component analysis, and adaptive singular value cancellation, in different evaluation metrics with statistical significance. Significance: The clinical potential is shown in the real Holter signal, and we introduce a new score to evaluate the performance of the algorithm.

The autonomic nervous system plays a major role in the integrative control of circulation, possibly contributing to the 'complex' dynamics responsible for fractal components in heart rate variability. Aim of this study is to evaluate whether an altered autonomic integrative control is identified by fractal analysis of heart rate variability. We enrolled 14 spinal cord injured individuals with complete lesion between the 5th and 11th thoracic vertebra (SCI_H), 14 with complete lesion between 12th thoracic and 5th lumbar vertebra (SCI_L), and 34 able-bodied controls (AB). These paraplegic subjects have an altered autonomic integrative regulation, but intact autonomic cardiac control and, as to SCI_L individuals, intact autonomic splanchnic control. Power spectral and fractal analysis (temporal spectrum of scale coefficients) were performed on 10 min tachograms. AB and SCI_L power spectra were similar, while the SCI_L fractal spectrum had higher coefficients between 12 and 48 s. SCI_H individuals had lower power than controls at 0.1 Hz; their fractal spectrum was morphologically different, diverging from that of controls at the largest scales (120 s). Therefore, when the lesion compromises the autonomic control of lower districts, fractal analysis reveals alterations undetected by power spectral analysis of heart rate variability.

Objective: Doppler ultrasound (US) is the most commonly applied method to measure the fetal heart rate (fHR). When the fetal heart is not properly located within the ultrasonic beam, fHR measurements often fail. As a consequence, clinical staff need to reposition the US transducer on the maternal abdomen, which can be a time consuming and tedious task. Approach: In this article, a method is presented to aid clinicians with the positioning of the US transducer to produce robust fHR measurements. A maximum likelihood estimation (MLE) algorithm is developed, which provides information on fetal heart location using the power of the Doppler signals received in the individual elements of a standard US transducer for fHR recordings. The performance of the algorithm is evaluated with simulations and in vitro experiments performed on a beating-heart setup. Main results: Both the experiments and the simulations show that the heart location can be accurately determined with an error of less than 7 mm within the measurement volume of the employed US transducer. Significance: The results show that the developed algorithm can be used to provide accurate feedback on fetal heart location for improved positioning of the US transducer, which may lead to improved measurements of the fHR.

Objective: Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals.

Approach: Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient.

Main results: Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of  <250 Hz and  <16 Hz respectively. The results informing clinical application were: (i) frequency modulation-based respiratory signals were generally of lower quality in elderly subjects compared to young subjects; (ii) the qualities of 23 out of 26 respiratory signals were reduced at elevated BRs; (iii) there were no differences associated with gender.

Significance: Recommendations based on the results are provided regarding device designs for BR estimation, and clinical applications. The dataset and code used in this study are publicly available.

Objective. We developed an automatic algorithm to determine rapid eye movement (REM) sleep on the basis of the autonomic activities reflected in heart rate variations. Approach. The heart rate variability (HRV) parameters were calculated using the R–R intervals from an electrocardiogram (ECG). A major autonomic variation associated with the sleep cycle was extracted from a combination of the obtained parameters. REM sleep was determined with an adaptive threshold applied to the acquired feature. The algorithm was optimized with the data from 26 healthy subjects and obstructive sleep apnea (OSA) patients and was validated with data from a separate group of 25 healthy and OSA subjects. Main results. According to an epoch-by-epoch (30 s) analysis, the average of Cohen's kappa and the accuracy were respectively 0.63 and 87% for the training set and 0.61 and 87% for the validation set. In addition, the REM sleep-related information extracted from the results of the proposed method revealed a significant correlation with those from polysomnography (PSG). Significance. The current algorithm only using R–R intervals can be applied to mobile and wearable devices that acquire heart-rate-related signals; therefore, it is appropriate for sleep monitoring in the home and ambulatory environments. Further, long-term sleep monitoring could provide useful information to clinicians and patients for the diagnosis and treatments of sleep-related disorders and individual health care.

Objective: In this paper, a miniaturized wearable core body temperature (CBT) monitoring system based on the dual heat flux (DHF) principle was developed. Approach: By interspersing calcium carbonate powder in PolyDimethylsiloxane (PDMS), a reformative heat transfer medium was produced to reduce the thermal equilibrium time. Besides, a least mean square (LMS) algorithm based active noise cancellation (ANC) method was adopted to diminish the impact of ambient temperature fluctuations. Theoretical analyses, finite element simulation, experiments on a hot plate and human volunteers were performed. Main results: The results showed that the proposed system had the advantages of small size, reduced initial time (~23.5 min), and good immunity to fluctuations of the air temperature. For the range of 37–41 °C on the hot plate, the error compared with a Fluke high accuracy thermometer was 0.08  ±  0.20 °C. In the human experiments, the measured temperature in the rest trial (34 subjects) had a difference of 0.13  ±  0.22 °C compared with sublingual temperature, while a significant increase of 1.36  ±  0.44 °C from rest to jogging was found in the exercise trial (30 subjects). Significance: This system has the potential for reliable continuous CBT measurement in rest and can reflect CBT variations during exercise.