Showing papers in "Physiological Measurement in 2012"

Infrared thermal imaging in medicine

Abstract: This review describes the features of modern infrared imaging technology and the standardization protocols for thermal imaging in medicine. The technique essentially uses naturally emitted infrared radiation from the skin surface. Recent studies have investigated the influence of equipment and the methods of image recording. The credibility and acceptance of thermal imaging in medicine is subject to critical use of the technology and proper understanding of thermal physiology. Finally, we review established and evolving medical applications for thermal imaging, including inflammatory diseases, complex regional pain syndrome and Raynaud's phenomenon. Recent interest in the potential applications for fever screening is described, and some other areas of medicine where some research papers have included thermal imaging as an assessment modality. In certain applications thermal imaging is shown to provide objective measurement of temperature changes that are clinically significant.

Wavelet-based motion artifact removal for functional near-infrared spectroscopy.

Abstract: Functional near-infrared spectroscopy (fNIRS) is a powerful tool for monitoring brain functional activities. Due to its non-invasive and non-restraining nature, fNIRS has found broad applications in brain functional studies. However, for fNIRS to work well, it is important to reduce its sensitivity to motion artifacts. We propose a new wavelet-based method for removing motion artifacts from fNIRS signals. The method relies on differences between artifacts and fNIRS signal in terms of duration and amplitude and is specifically designed for spike artifacts. We assume a Gaussian distribution for the wavelet coefficients corresponding to the underlying hemodynamic signal in detail levels and identify the artifact coefficients using this distribution. An input parameter controls the intensity of artifact attenuation in trade-off with the level of distortion introduced in the signal. The method only modifies wavelet coefficients in levels adaptively selected based on the degree of contamination with motion artifact. To demonstrate the feasibility of the method, we tested it on experimental fNIRS data collected from three infant subjects. Normalized mean-square error and artifact energy attenuation were used as criteria for performance evaluation. The results show 18.29 and 16.42 dB attenuation in motion artifacts energy for 700 and 830 nm wavelength signals in a total of 29 motion events with no more than −16.7 dB distortion in terms of normalized mean-square error in the artifact-free regions of the signal.

Dynamic time warping and machine learning for signal quality assessment of pulsatile signals

Abstract: In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal.

Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms

Abstract: A completely automated algorithm to detect poor-quality electrocardiograms (ECGs) is described. The algorithm is based on both novel and previously published signal quality metrics, originally designed for intensive care monitoring. The algorithms have been adapted for use on short (5-10 s) single- and multi-lead ECGs. The metrics quantify spectral energy distribution, higher order moments and inter-channel and inter-algorithm agreement. Seven metrics were calculated for each channel (84 features in all) and presented to either a multi-layer perceptron artificial neural network or a support vector machine (SVM) for training on a multiple-annotator labelled and adjudicated training dataset. A single-lead version of the algorithm was also developed in a similar manner. Data were drawn from the PhysioNet Challenge 2011 dataset where binary labels were available, on 1500 12-lead ECGs indicating whether the entire recording was acceptable or unacceptable for clinical interpretation. We re-annotated all the leads in both the training set (1000 labelled ECGs) and test dataset (500 12-lead ECGs where labels were not publicly available) using two independent annotators, and a third for adjudication of differences. We found that low-quality data accounted for only 16% of the ECG leads. To balance the classes (between high and low quality), we created extra noisy data samples by adding noise from PhysioNet's noise stress test database to some of the clean 12-lead ECGs. No data were shared between training and test sets. A classification accuracy of 98% on the training data and 97% on the test data were achieved. Upon inspection, incorrectly classified data were found to be borderline cases which could be classified either way. If these cases were more consistently labelled, we expect our approach to achieve an accuracy closer to 100%.

Supersonic shear imaging provides a reliable measurement of resting muscle shear elastic modulus.

Abstract: The aim of the present study was to assess the reliability of shear elastic modulus measurements performed using supersonic shear imaging (SSI) in nine resting muscles (i.e. gastrocnemius medialis, tibialis anterior, vastus lateralis, rectus femoris, triceps brachii, biceps brachii, brachioradialis, adductor pollicis obliquus and abductor digiti minimi) of different architectures and typologies. Thirty healthy subjects were randomly assigned to the intra-session reliability (n = 20), inter-day reliability (n = 21) and the inter-observer reliability (n = 16) experiments. Muscle shear elastic modulus ranged from 2.99 (gastrocnemius medialis) to 4.50 kPa (adductor digiti minimi and tibialis anterior). On the whole, very good reliability was observed, with a coefficient of variation (CV) ranging from 4.6% to 8%, except for the inter-operator reliability of adductor pollicis obliquus (CV = 11.5%). The intraclass correlation coefficients were good (0.871 ± 0.045 for the intra-session reliability, 0.815 ± 0.065 for the inter-day reliability and 0.709 ± 0.141 for the inter-observer reliability). Both the reliability and the ease of use of SSI make it a potentially interesting technique that would be of benefit to fundamental, applied and clinical research projects that need an accurate assessment of muscle mechanical properties.

Whither lung EIT: where are we, where do we want to go and what do we need to get there?

Abstract: Breathing moves volumes of electrically insulating air into and out of the lungs, producing conductivity changes which can be seen by electrical impedance tomography (EIT) It has thus been apparent, since the early days of EIT research, that imaging of ventilation could become a key clinical application of EIT In this paper, we review the current state and future prospects for lung EIT, by a synthesis of the presentations of the authors at the 'special lung sessions' of the annual biomedical EIT conferences in 2009-2011 We argue that lung EIT research has arrived at an important transition It is now clear that valid and reproducible physiological information is available from EIT lung images We must now ask the question: How can these data be used to help improve patient outcomes? To answer this question, we develop a classification of possible clinical scenarios in which EIT could play an important role, and we identify clinical and experimental research programmes and engineering developments required to turn EIT into a clinically useful tool for lung monitoring

Measurement of endogenous acetone and isoprene in exhaled breath during sleep

Abstract: This explorative study aims at characterizing the breath behavior of two prototypic volatile organic compounds, acetone and isoprene, during normal human sleep and to possibly relate changes in the respective concentration time courses to the underlying sleep architecture. For this purpose, six normal healthy volunteers (two females, four males, age 20-29 years) were monitored over two consecutive nights (the first one being an adaption night) by combining real-time proton-transfer-reaction mass spectrometry measurements from end-tidal exhalation segments with laboratory-based polysomnographic data. Breath acetone concentrations increased overnight in all measurements, with an average relative change by a factor of up to 4 (median 2.5). Nighttime concentration maxima were usually recorded 2-3 h before lights on. For breath isoprene, a nocturnal increase in baseline concentrations of about 74% was observed, with individual changes ranging from 36-110%. Isoprene profiles exhibited pronounced concentration peaks, which were highly specific for leg movements as scored by tibial electromyography. Furthermore, relative to a linear trend, baseline isoprene concentrations decreased during the transition from the NREM to the REM phase of a complete sleep cycle.

Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation

Abstract: Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO2). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.

A review on electrical impedance tomography for pulmonary perfusion imaging

Abstract: Although electrical impedance tomography (EIT) for ventilation monitoring is on the verge of clinical trials, pulmonary perfusion imaging with EIT remains a challenge, especially in spontaneously breathing subjects. In anticipation of more research on this subject, we believe a thorough review is called for. In this paper, findings related to the physiological origins and electrical characteristics of this signal are summarized, highlighting properties that are particularly relevant to EIT. The perfusion impedance change signal is significantly smaller in amplitude compared with the changes due to ventilation. Therefore, the hardware used for this purpose must be more sensitive and more resilient to noise. In previous works, some signal- or image-processing methods have been required to separate these two signals. Three different techniques are reviewed in this paper, including the ECG-gating method, frequency-domain-filtering-based methods and a principal-component-analysis-based method. In addition, we review a number of experimental studies on both human and animal subjects that employed EIT for perfusion imaging, with promising results in the diagnosis of pulmonary embolism (PE) and pulmonary arterial hypertension as well as other potential applications. In our opinion, PE is most likely to become the main focus for perfusion EIT in the future, especially for heavily instrumented patients in the intensive care unit (ICU).

Food intake monitoring: an acoustical approach to automated food intake activity detection and classification of consumed food

Abstract: Obesity and nutrition-related diseases are currently growing challenges for medicine. A precise and timesaving method for food intake monitoring is needed. For this purpose, an approach based on the classification of sounds produced during food intake is presented. Sounds are recorded non-invasively by miniature microphones in the outer ear canal. A database of 51 participants eating seven types of food and consuming one drink has been developed for algorithm development and model training. The database is labeled manually using a protocol with introductions for annotation. The annotation procedure is evaluated using Cohen's kappa coefficient. The food intake activity is detected by the comparison of the signal energy of in-ear sounds to environmental sounds recorded by a reference microphone. Hidden Markov models are used for the recognition of single chew or swallowing events. Intake cycles are modeled as event sequences in finite-state grammars. Classification of consumed food is realized by a finite-state grammar decoder based on the Viterbi algorithm. We achieved a detection accuracy of 83% and a food classification accuracy of 79% on a test set of 10% of all records. Our approach faces the need of monitoring the time and occurrence of eating. With differentiation of consumed food, a first step toward the goal of meal weight estimation is taken.

Human pose recovery using wireless inertial measurement units.

Abstract: Many applications in rehabilitation and sports training require the assessment of the patient's status based on observation of their movement. Small wireless sensors, such as accelerometers and gyroscopes, can be utilized to provide a quantitative measure of the human movement for assessment. In this paper, a kinematics-based approach is developed to estimate human leg posture and velocity from wearable sensors during the performance of typical physiotherapy and training exercises. The proposed approach uses an extended Kalman filter to estimate joint angles from accelerometer and gyroscopic data and is capable of recovering joint angles from arbitrary 3D motion. Additional joint limit constraints are implemented to reduce drift, and an automated approach is developed for estimating and adapting the process noise during online estimation. The approach is validated through a user study consisting of 20 subjects performing knee and hip rehabilitation exercises. When compared to motion capture, the approach achieves an average root-mean-square error of 4.27 cm for unconstrained motion, with an average joint error of 6.5°. The average root-mean-square error is 3.31 cm for sagittal planar motion, with an average joint error of 4.3°.

A new algorithm for detecting central apnea in neonates

Abstract: Apnea of prematurity is an important and common clinical problem, and is often the rate-limiting process in NICU discharge. Accurate detection of episodes of clinically important neonatal apnea using existing chest impedance (CI) monitoring is a clinical imperative. The technique relies on changes in impedance as the lungs fill with air, a high impedance substance. A potential confounder, however, is blood coursing through the heart. Thus, the cardiac signal during apnea might be mistaken for breathing. We report here a new filter to remove the cardiac signal from the CI that employs a novel resampling technique optimally suited to remove the heart rate signal, allowing improved apnea detection. We also develop an apnea detection method that employs the CI after cardiac filtering. The method has been applied to a large database of physiological signals, and we prove that, compared to the presently used monitors, the new method gives substantial improvement in apnea detection.

Extracting respiratory information from seismocardiogram signals acquired on the chest using a miniature accelerometer

Abstract: Seismocardiography (SCG) is a non-invasive measurement of the vibrations of the chest caused by the heartbeat. SCG signals can be measured using a miniature accelerometer attached to the chest, and are thus well-suited for unobtrusive and long-term patient monitoring. Additionally, SCG contains information relating to both cardiovascular and respiratory systems. In this work, algorithms were developed for extracting three respiration-dependent features of the SCG signal: intensity modulation, timing interval changes within each heartbeat, and timing interval changes between successive heartbeats. Simultaneously with a reference respiration belt, SCG signals were measured from 20 healthy subjects and a respiration rate was estimated using each of the three SCG features and the reference signal. The agreement between each of the three accelerometer-derived respiration rate measurements was computed with respect to the respiration rate derived from the reference respiration belt. The respiration rate obtained from the intensity modulation in the SCG signal was found to be in closest agreement with the respiration rate obtained from the reference respiration belt: the bias was found to be 0.06 breaths per minute with a 95% confidence interval of??0.99 to 1.11 breaths per minute. The limits of agreement between the respiration rates estimated using SCG (intensity modulation) and the reference were within the clinically relevant ranges given in existing literature, demonstrating that SCG could be used for both cardiovascular and respiratory monitoring. Furthermore, phases of each of the three SCG parameters were investigated at four instances of a respiration cycle?start inspiration, peak inspiration, start expiration, and peak expiration?and during breath hold (apnea). The phases of the three SCG parameters observed during the respiration cycle were congruent with existing literature and physiologically expected trends.

QRS detection based ECG quality assessment.

Abstract: Although immediate feedback concerning ECG signal quality during recording is useful, up to now not much literature describing quality measures is available. We have implemented and evaluated four ECG quality measures. Empty lead criterion (A), spike detection criterion (B) and lead crossing point criterion (C) were calculated from basic signal properties. Measure D quantified the robustness of QRS detection when applied to the signal. An advanced Matlab-based algorithm combining all four measures and a simplified algorithm for Android platforms, excluding measure D, were developed. Both algorithms were evaluated by taking part in the Computing in Cardiology Challenge 2011. Each measure's accuracy and computing time was evaluated separately. During the challenge, the advanced algorithm correctly classified 93.3% of the ECGs in the training-set and 91.6 % in the test-set. Scores for the simplified algorithm were 0.834 in event 2 and 0.873 in event 3. Computing time for measure D was almost five times higher than for other measures. Required accuracy levels depend on the application and are related to computing time. While our simplified algorithm may be accurate for real-time feedback during ECG self-recordings, QRS detection based measures can further increase the performance if sufficient computing power is available.

Device-based monitoring in physical activity and public health research.

Abstract: Measurement of physical activity is important, given the vital role of this behavior in physical and mental health. Over the past quarter of a century, the use of small, non-invasive, wearable monitors to assess physical activity has become commonplace. This review is divided into three sections. In the first section, a brief history of physical activity monitoring is provided, along with a discussion of the strengths and weaknesses of different devices. In the second section, recent applications of physical activity monitoring in physical activity and public health research are discussed. Wearable monitors are being used to conduct surveillance, and to determine the extent and distribution of physical activity and sedentary behaviors in populations around the world. They have been used to help clarify the dose-response relation between physical activity and health. Wearable monitors that provide feedback to users have also been used in longitudinal interventions to motivate research participants and to assess their compliance with program goals. In the third section, future directions for research in physical activity monitoring are discussed. It is likely that new developments in wearable monitors will lead to greater accuracy and improved ease-of-use.

Low-complexity R-peak detection for ambulatory fetal monitoring

Abstract: Non-invasive fetal health monitoring during pregnancy is becoming increasingly important because of the increasing number of high-risk pregnancies. Despite recent advances in signal-processing technology, which have enabled fetal monitoring during pregnancy using abdominal electrocardiogram (ECG) recordings, ubiquitous fetal health monitoring is still unfeasible due to the computational complexity of noise-robust solutions. In this paper, an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm. The proposed algorithm is optimized to reduce computational complexity, without reducing the R-peak detection performance compared to the existing R-peak detection schemes. Validation of the algorithm is performed on three manually annotated datasets. With a detection error rate of 0.23%, 1.32% and 9.42% on the MIT/BIH Arrhythmia and in-house maternal and fetal databases, respectively, the detection rate of the proposed algorithm is comparable to the best state-of-the-art algorithms, at a reduced computational complexity.

Short-term heart rate variability--age dependence in healthy subjects.

Abstract: Heart rate variability (HRV) analysis is an established method to characterize the autonomic regulation and is based mostly on 24h Holter recordings. The importance of short-term HRV (less than 30?min) for various applications is growing consistently. Major reasons for this are the suitability for ambulatory care and patient monitoring and the ability to provide an almost immediate test result. So far, there have been only a few studies that provided statistically relevant reference values for short-term HRV. In our study, 5?min short-term HRV indices were determined from 1906 healthy subjects. From these records, linear and nonlinear indices were extracted. To determine general age-related influences, HRV indices were compared from subjects aged 25?49?years with subjects aged 50?74?years. In a second approach, we examined the development of HRV indices by age in terms of age decades (25?34, 35?44, 45?54, 55?64 and 65?74?years). Our results showed significant variations of HRV indices by age in almost all domains. While marked dynamics in terms of parameter change (variability reduction) were observed in the first age decades, in particular the last two age decades showed certain constancy with respect to the HRV indices examined.

Electrocardiogram signal quality measures for unsupervised telehealth environments

Abstract: The use of telehealth paradigms for the remote management of patients suffering from chronic conditions has become more commonplace with the advancement of Internet connectivity and enterprise software systems. To facilitate clinicians in managing large numbers of telehealth patients, and in digesting the vast array of data returned from the remote monitoring environment, decision support systems in various guises are often utilized. The success of decision support systems in interpreting patient conditions from physiological data is dependent largely on the quality of these recorded data. This paper outlines an algorithm to determine the quality of single-lead electrocardiogram (ECG) recordings obtained from telehealth patients. Three hundred short ECG recordings were manually annotated to identify movement artifact, QRS locations and signal quality (discrete quality levels) by a panel of three experts, who then reconciled the annotation as a group to resolve any discrepancies. After applying a published algorithm to remove gross movement artifact, the proposed method was then applied to estimate the remaining ECG signal quality, using a Parzen window supervised statistical classifier model. The three-class classifier model, using a number of time-domain features and evaluated using cross validation, gave an accuracy in classifying signal quality of 78.7% (κ = 0.67) when using fully automated preprocessing algorithms to remove gross motion artifact and detect QRS locations. This is a similar level of accuracy to the reported human inter-scorer agreement when generating the gold standard annotation (accuracy = 70-89.3%, κ = 0.54-0.84). These results indicate that the assessment of the quality of single-lead ECG recordings, acquired in unsupervised telehealth environments, is entirely feasible and may help to promote the acceptance and utility of future decision support systems for remotely managing chronic disease conditions.

Telemetry pill versus rectal and esophageal temperature during extreme rates of exercise-induced core temperature change.

Abstract: Core temperature measurement with an ingestible telemetry pill has been scarcely investigated during extreme rates of temperature change, induced by short high-intensity exercise in the heat. Therefore, nine participants performed a protocol of rest, (sub)maximal cycling and recovery at 30 °C. The pill temperature (T pill) was compared with the rectal temperature (T re) and esophageal temperature (T es). T pillcorresponded well to T reduring the entire trial, but deviated considerably from T esduring the exercise and recovery periods. During maximal exercise, the average ΔT pillT reand ΔT pillT eswere 0.13 ± 0.26 and0.57 ± 0.53 °C, respectively. The response time from the start of exercise, the rate of change during exercise and the peak temperature were similar for T pilland T re.T esresponded 5 min earlier, increased more than twice as fast and its peak value was 0.42 ± 0.46 °C higher than T pill. In conclusion, also during considerable temperature changes at a very high rate, T pillis still a representative of T re. The extent of the deviation in the pattern and peak values between T pilland T es(up to >1 °C) strengthens the assumption that T pillis unsuited to evaluate central blood temperature when body temperatures change rapidly. © 2012 Institute of Physics and Engineering in Medicine.

The measurement of sedentary patterns and behaviors using the activPAL™ Professional physical activity monitor

Abstract: Epidemiological studies have associated the negative effects of sedentary time and sedentary patterns on health indices. However, these studies have used methodologies that do not directly measure the sedentary state. Recent technological developments in the area of motion sensors have incorporated inclinometers, which can measure the inclination of the body directly, without relying on self-report or count thresholds. This paper aims to provide a detailed description of methodologies used to examine a range of relevant variables, including sedentary levels and patterns from an inclinometer-based motion sensor. The activPAL Professional physical activity logger provides an output which can be interpreted and used without the need for further processing and additional variables were derived using a custom designed MATLAB® computer program. The methodologies described have been implemented on a sample of 44 adolescent females, and the results of a range of daily physical activity and sedentary variables are described and presented. The results provide a range of objectively measured and objectively processed variables, including total time spent sitting/lying, standing and stepping, number and duration of daily sedentary bouts and both bed hours and non-bed hours, which may be of interest when making association between physical activity, sedentary behaviors and health indices.

Electrical impedance tomography system based on active electrodes.

Abstract: Electrical impedance tomography (EIT) can image the distribution of ventilated lung tissue, and is thus a promising technology to help monitor patient breathing to help selection of mechanical ventilation parameters. Two key difficulties in EIT instrumentation make such monitoring difficult: (1) EIT data quality depends on good electrode contact and is sensitive to changes in contact quality, and (2) EIT electrodes are difficult and time consuming to place on patients. This paper presents the design and initial tests of an active electrode-based system to address these difficulties. Our active electrode EIT system incorporates an active electrode belt, a central voltage-driven current source, central analog to digital converters and digital to analog converters, a central FPGA-based demodulator and controller. The electrode belt is designed incorporating 32 active electrodes, each of which contains the electronic amplifiers, switches and associated logic. Tests show stable device performance with a convenient ease of use and good imaging ability in volunteer tests.

A comparison of the Actigraph GT1M and GT3X accelerometers under standardized and free-living conditions.

Abstract: Prior to 2008, data collection from Actigraph accelerometers was only possible in the uniaxial mode. In 2009, Actigraph released the GT3X, which allows triaxial data collection. The purpose of this study was to determine if data collected by the GT3X in the uniaxial mode are comparable to its predecessor, the GT1M, under both standardized and free-living conditions. Thirty-four subjects (17 women and 17 men) provided complete data for this study. Subjects wore the accelerometers (one GT1M and one GT3X) on their waistband in line with the right and left anterior axillary lines. Each subject walked on a treadmill at speeds of 2.4, 3.2, 4.0, 4.8, 5.6 and 6.4 km h(-1) for 5 min each, and then continued to wear both accelerometers for all waking hours for three consecutive days. Mean steady-state activity counts min(-1) for both accelerometers were not statistically different for the standardized treadmill walking speeds and for mean minutes/day and activity counts/day for intensity classifications under the free-living condition. Based on comparisons made from both standardized walking speeds and free-living conditions, it is reasonable to compare data derived from either the GT1M or GT3X when collected in the uniaxial mode.

Test-retest reliability and minimal detectable change of repeated sit-to-stand analysis using one body fixed sensor in geriatric patients

Abstract: A majority of geriatric patients experience difficulty in performing sit-to-stand (SiSt) transitions. A detailed assessment of SiSt ability is a prerequisite for successful rehabilitation. Body fixed sensors (BFSs) are increasingly used to assess functional performances. As to date there is no system which analyzes clinically relevant phases of SiSt, the aim of this study was to determine the reliability of an automated approach for quantifying durations and angular velocities of trunk flexion and extension during repeated SiSt transitions using one BFS (DynaPort® Hybrid). Forty multimorbid geriatric patients aged 84.1 ± 6.6 years were included. Each patient participated in two test sessions with a 5 min rest period in between. Intra- and interrater reliability was assessed. Intraclass correlation coefficients (ICCs), absolute and relative standard measurement errors (SEMs, SEMs%) and minimal detectable changes (MDCs(95), MDCs(95)%) were calculated. ICCs were good to excellent for all variables in the total sample (0.80-0.94). The intraobserver group (50%) showed a higher number of excellent ICCs (≥.9) compared to the interobserver subgroup (10%). SEM% was low for all variables (6.9-12.7%). MDC(95)% ranged 19.2-34.4% and more variables ≤30% were found in the intra- (80%) compared to the inter-observer group (60%). Study results demonstrate that the BFS system provides a reliable analysis of SiSt phases in geriatric patients, and is a substantial improvement over the stopwatch approach used in clinical practice today.

Automatic ECG quality scoring methodology: mimicking human annotators

Abstract: An algorithm to determine the quality of electrocardiograms (ECGs) can enable inexperienced nurses and paramedics to record ECGs of sufficient diagnostic quality. Previously, we proposed an algorithm for determining if ECG recordings are of acceptable quality, which was entered in the PhysioNet Challenge 2011. In the present work, we propose an improved two-step algorithm, which first rejects ECGs with macroscopic errors (signal absent, large voltage shifts or saturation) and subsequently quantifies the noise (baseline, powerline or muscular noise) on a continuous scale. The performance of the improved algorithm was evaluated using the PhysioNet Challenge database (1500 ECGs rated by humans for signal quality). We achieved a classification accuracy of 92.3% on the training set and 90.0% on the test set. The improved algorithm is capable of detecting ECGs with macroscopic errors and giving the user a score of the overall quality. This allows the user to assess the degree of noise and decide if it is acceptable depending on the purpose of the recording.

Inertial sensor motion analysis of gait, sit-stand transfers and step-up transfers: differentiating knee patients from healthy controls.

Abstract: Patients undergoing total knee replacement for end stage knee osteoarthritis (OA) become increasingly younger and more demanding. Consequently, outcome assessment tools need to evolve toward objective performance-based measures. We applied a novel approach toward ambulatory biomechanical assessment of physical function using a single inertial sensor located at the pelvis to derive various motion parameters during activities of daily living. We investigated the potential of a clinically feasible battery of tests to define relevant parameters of physical function. We compared preoperative measures of end stage knee OA patients to healthy subjects. Our results show that measures of time yield the highest discriminative capacity to differentiate between groups. Additionally we found disease-dependent and task-specific alterations of movement for inertial sensor-derived motion parameters with good discriminative capacity. The inertial sensor's output quantities seem to capture another clinically relevant dimension of physical function that is supplementary to time. This study demonstrates the potential of inertial sensor-based motion analysis and provides a standardized test feasible for a routine clinical application in the longitudinal follow-up.

Preferred Sensor Sites for Surface EMG Signal Decomposition

Abstract: Technologies for decomposing the electromyographic (EMG) signal into its constituent motor unit action potential trains have become more practical by the advent of a non-invasive methodology using surface EMG (sEMG) sensors placed on the skin above the muscle of interest (De Luca et al 2006 J. Neurophysiol. 96 1646-57 and Nawab et al 2010 Clin. Neurophysiol. 121 1602-15). This advancement has widespread appeal among researchers and clinicians because of the ease of use, reduced risk of infection, and the greater number of motor unit action potential trains obtained compared to needle sensor techniques. In this study we investigated the influence of the sensor site on the number of identified motor unit action potential trains in six lower limb muscles and one upper limb muscle with the intent of locating preferred sensor sites that provided the greatest number of decomposed motor unit action potential trains, or motor unit yield. Sensor sites rendered varying motor unit yields throughout the surface of a muscle. The preferred sites were located between the center and the tendinous areas of the muscle. The motor unit yield was positively correlated with the signal-to-noise ratio of the detected sEMG. The signal-to-noise ratio was inversely related to the thickness of the tissue between the sensor and the muscle fibers. A signal-to-noise ratio of 3 was found to be the minimum required to obtain a reliable motor unit yield.

Comparison of frequency difference reconstruction algorithms for the detection of acute stroke using EIT in a realistic head-shaped tank

Abstract: Imaging of acute stroke might be possible using multi-frequency electrical impedance tomography (MFEIT) but requires absolute or frequency difference imaging. Simple linear frequency difference reconstruction has been shown to be ineffective in imaging with a frequency-dependant background conductivity; this has been overcome with a weighted frequency difference approach with correction for the background but this has only been validated for a cylindrical and hemispherical tank. The feasibility of MFEIT for imaging of acute stroke in a realistic head geometry was examined by imaging a potato perturbation against a saline background and a carrot-saline frequency-dependant background conductivity, in a head-shaped tank with the UCLH Mk2.5 MFEIT system. Reconstruction was performed with time difference (TD), frequency difference (FD), FD adjacent (FDA), weighted FD (WFD) and weighted FDA (WFDA) linear algorithms. The perturbation in reconstructed images corresponded to the true position to 5.4 for all algorithms in saline but only for TD, WFDA and WFD in the carrot-saline background. No reliable imaging was possible with FD and FDA. This indicates that the WFD approach is also effective for a realistic head geometry and supports its use for human imaging in the future.

Linear and nonlinear tremor acceleration characteristics in patients with Parkinson's disease

Abstract: The purpose of the study was to evaluate linear and nonlinear tremor characteristics of the hand in patients with Parkinson's disease (PD) and to compare the results with those of healthy old and young control subjects. Furthermore, the aim was to study correlation between tremor characteristics and clinical signs. A variety of nonlinear (sample entropy, cross-sample entropy, recurrence rate, determinism and correlation dimension) and linear (amplitude, spectral peak frequency and total power, and coherence) hand tremor parameters were computed from acceleration measurements for PD patients (n = 30, 68.3 ± 7.8 years), and old (n = 20, 64.2 ± 7.0 years) and young (n = 20, 18.4 ± 1.1 years) control subjects. Nonlinear tremor parameters such as determinism, sample entropy and cross-sample entropy were significantly different between the PD patients and healthy controls. These parameters correlated with the Unified Parkinson's disease rating scale (UPDRS), tremor and finger tapping scores, but not with the rigidity scores. Linear tremor parameters such as the amplitude and the maximum power (power corresponding to peak frequency) also correlated with the clinical findings. No major difference was detected in the tremor characteristics between old and young control subjects. The study revealed that tremor in PD patients is more deterministic and regular when compared to old or young healthy controls. The nonlinear tremor parameters can differentiate patients with PD from healthy control subjects and these parameters may have potential in the assessment of the severity of PD (UPDRS).

Biomechanical Examination of the ‘Plateau Phenomenon’ in ActiGraph Vertical Activity Counts

Abstract: This paper determines if the leveling off ('plateau/inverted-U' phenomenon) of vertical ActiGraph activity counts during running at higher speeds is attributable to the monitor's signal filtering and acceleration detection characteristics. Ten endurance-trained male participants (mean (SD) age = 28.2 (4.7) years) walked at 3, 5 and 7 km h(-1), and ran at 8, 10, 12, 14, 16, 18 and 20 km h(-1) on a force treadmill while wearing an ActiGraph GT3X monitor at the waist. Triaxial accelerations of the body's center of mass (CoM) and frequency content of these accelerations were computed from the force treadmill data. GT3X vertical activity counts demonstrated the expected 'plateau/inverted-U' phenomenon. In contrast, vertical CoM accelerations increased with increasing speed (1.32 ± 0.26 g at 10 km h(-1) and 1.68 ± 0.24 g at 20 km h(-1)). The dominant frequency in the CoM acceleration signals increased with running speed (14.8 ± 3.2 Hz at 10 km h(-1) and 24.8 ± 3.2 Hz at 20 km h(-1)) and lay beyond the ActiGraph band-pass filter (0.25 to 2.5 Hz) limits. In conclusion, CoM acceleration magnitudes during walking and running lie within the ActiGraph monitor's dynamic acceleration detecting capability. Acceleration signals of higher frequencies that are eliminated by the ActiGraph band-pass filter may be necessary to distinguish among exercise intensity at higher running speeds.

Wrist-worn accelerometers in assessment of energy expenditure during intensive training.

Abstract: We assessed the ability of the Polar activity recorder (AR) to measure energy expenditure (EE) during military training. Twenty-four voluntary male conscripts participated in the study and wore an AR on the non-dominant wrist 24 h a day for 7 d. The AR analyzed and stored the frequency of hand movements (f_hand) into memory at 1 min intervals. The relationship between f_hand and EE was studied over a 7 d period of military training using the doubly labeled water (DLW) technique. In addition, the relationship between f_hand and EE was analyzed during walking and running on a treadmill with an indirect calorimeter (IC), and f_hand was measured during a supervised 45 min field march test where the conscripts carried combat gear. EE was expressed as physical activity level (PAL), total energy expenditure (TEE), and activity-induced energy expenditure adjusted for body mass (AEE/BM). Over the 7 d period, f_hand alone explained 46% of inter-individual variation in PAL(DLW). After inclusion of body height and mass in the model used to predict PAL(DLW) from f_hand, a very high positive correlation and a low standard error of estimate (SEE) were observed between the AR and DLW techniques: for TEE r = 0.86 (p < 0.001), the SEE was 6.3%, and for AEE/BM r = 0.84 (p < 0.001), the SEE was 12.8%. In the treadmill exercise, f_hand correlated highly with PAL(IC) (r = 0.97 ± 0.02). In the 45 min field march test, the AR measured similar f_hand as on the treadmill at the same speed. In conclusion, the wrist-worn AR can be regarded as a reliable and valid method for assessing EE during intensive training.