Background: The overarching goal of blood glucose forecasting is to assist individuals with type 1 diabetes (T1D) in avoiding hyper- or hypoglycemic conditions. While deep learning approaches have shown promising results for blood glucose forecasting in adults with T1D, it is not known if these results generalize to children. Possible reasons are physical activity (PA), which is often unplanned in children, as well as age and development of a child, which both have an effect on the blood glucose level. Materials and Methods: In this study, we collected time series measurements of glucose levels, carbohydrate intake, insulin-dosing and physical activity from children with T1D for one week in an ethics approved prospective observational study, which included daily physical activities. We investigate the performance of state-of-the-art deep learning methods for adult data—(dilated) recurrent neural networks and a transformer—on our dataset for short-term (30  min) and long-term (2  h) prediction. We propose to integrate static patient characteristics, such as age, gender, BMI, and percentage of basal insulin, to account for the heterogeneity of our study group. Results: Integrating static patient characteristics (SPC) proves beneficial, especially for short-term prediction. LSTMs and GRUs with SPC perform best for a prediction horizon of 30  min (RMSE of 1.66  mmol/l), a vanilla RNN with SPC performs best across different prediction horizons, while the performance significantly decays for long-term prediction. For prediction during the night, the best method improves to an RMSE of 1.50  mmol/l. Overall, the results for our baselines and RNN models indicate that blood glucose forecasting for children conducting regular physical activity is more challenging than for previously studied adult data. Conclusion: We find that integrating static data improves the performance of deep-learning architectures for blood glucose forecasting of children with T1D and achieves promising results for short-term prediction. Despite these improvements, additional clinical studies are warranted to extend forecasting to longer-term prediction horizons.

Alexander Marx, Francesco Di Stefano, Heike Leutheuser, Kieran Chin-Cheong, Marc Pfister, Marie-Anne Burckhardt, Sara Bachmann^†, Julia E. Vogt^†
† denotes shared last authorship

Frontiers in Pediatrics

14.12.2023

LinkDOI

Objective: To evaluate the association of self-reported physical function with subjective and objective measures as well as temporospatial gait features in lumbar spinal stenosis (LSS). Design: Cross-sectional pilot study. Setting: Outpatient multispecialty clinic. Participants: Participants with LSS and matched controls without LSS (n=10 per group; N=20). Interventions: Not applicable. Main outcome measures: Self-reported physical function (36-Item Short Form Health Survey [SF-36] physical functioning domain), Oswestry Disability Index, Swiss Spinal Stenosis Questionnaire, the Neurogenic Claudication Outcome Score, and inertia measurement unit (IMU)-derived temporospatial gait features. Results: Higher self-reported physical function scores (SF-36 physical functioning) correlated with lower disability ratings, neurogenic claudication, and symptom severity ratings in patients with LSS (P<.05). Compared with controls without LSS, patients with LSS have lower scores on physical capacity measures (median total distance traveled on 6-minute walk test: controls 505 m vs LSS 316 m; median total distance traveled on self-paced walking test: controls 718 m vs LSS 174 m). Observed differences in IMU-derived gait features, physical capacity measures, disability ratings, and neurogenic claudication scores between populations with and without LSS were statistically significant. Conclusions: Further evaluation of the association of IMU-derived temporospatial gait with self-reported physical function, pain related-disability, neurogenic claudication, and spinal stenosis symptom severity score in LSS would help clarify their role in tracking LSS outcomes.

Charles A Odonkor, Salam Taraben, Christy Tomkins-Lane, Wei Zhang, Amir Muaremi, H. Leutheuser, Ruopeng Sun, Matthew Smuck

Archives of Rehabilitation Research and Clinical Translation

01.09.2021

DOI

Background Functional ambulation limitations are features of lumbar spinal stenosis (LSS) and knee osteoarthritis (OA). With numerous validated walking assessment protocols and a vast number of spatiotemporal gait parameters available from sensor-based assessment, there is a critical need for selection of appropriate test protocols and variables for research and clinical applications. Research question In patients with knee OA and LSS, what are the best sensor-derived gait parameters and the most suitable clinical walking test to discriminate between these patient populations and controls? Methods We collected foot-mounted inertial measurement unit (IMU) data during three walking tests (fast-paced walk test-FPWT, 6-min walk test– 6MWT, self-paced walk test – SPWT) for subjects with LSS, knee OA and matched controls (N = 10 for each group). Spatiotemporal gait characteristics were extracted and pairwise compared (Omega partial squared – w_p^2) between patients and controls. Results We found that normal paced walking tests (6MWT, SPWT) are better suited for distinguishing gait characteristics between patients and controls. Among the sensor-based gait parameters, stance and double support phase timing were identified as the best gait characteristics for the OA population discrimination, whereas foot flat ratio, gait speed, stride length and cadence were identified as the best gait characteristics for the LSS population discrimination. Significance These findings provide guidance on the selection of sensor-derived gait parameters and clinical walking tests to detect alterations in mobility for people with LSS and knee OA.

C. Odonkor, A. Kuwabara, C. Tomkins-Lane, W. Zhang, A. Muaremi, H. Leutheuser, R. Sun, M. Smuck

Gait&Posture

01.07.2020

DOI

Wearable health sensors are about to change our health system. While several technological improvements have been presented to enhance performance and energy-efficiency, battery runtime is still a critical concern for practical use of wearable biomedical sensor systems. The runtime limitation is directly related to the battery size, which is another concern regarding practicality and customer acceptance. We introduced ULPSEK-Ultra-Low-Power Sensor Evaluation Kit-for evaluation of biomedical sensors and monitoring applications (http://ulpsek.com). ULPSEK includes a multiparameter sensor measuring and processing electrocardiogram, respiration, motion, body temperature, and photoplethysmography. Instead of a battery, ULPSEK is powered using an efficient body heat harvester. The harvester produced 171 W on average, which was sufficient to power the sensor below 25 C ambient temperature. We present design issues regarding the power supply and the power distribution network of the ULPSEK sensor platform. Due to the security aspect of self-powered health sensors, we suggest a hybrid solution consisting of a battery charged by a harvester.

A. Tobola, H. Leutheuser, M. Pollak, P. Spies, C. Hofmann, C. Weigand, B.M. Eskofier, G. Fischer

IEEE J Biomed Health Inform.

01.01.2018

DOI

The second most common cause of diving fatalities is cardiovascular diseases. Monitoring the cardiovascular system in actual underwater conditions is necessary to gain insights into cardiac activity during immersion and to trigger preventive measures. We developed a wearable, current-based electrocardiogram (ECG) device in the eco-system of the FitnessSHIRT platform. It can be used for normal/dry ECG measuring purposes but is specifically designed to allow underwater signal acquisition without having to use insulated electrodes. Our design is based on a transimpedance amplifier circuit including active current feedback. We integrated additional cascaded filter components to counter noise characteristics specific to the immersed condition of such a system. The results of the evaluation show that our design is able to deliver high-quality ECG signals underwater with no interferences or loss of signal quality. To further evaluate the applicability of the system, we performed an applied study with it using 12 healthy subjects to examine whether differences in the heart rate variability exist between sitting and supine positions of the human body immersed in water and outside of it. We saw significant differences, for example, in the RMSSD and SDSD between sitting outside the water (36 ms) and sitting immersed in water (76 ms) and the pNN50 outside the water (6.4%) and immersed in water (18.2%). The power spectral density for the sitting positions in the TP and HF increased significantly during water immersion while the LF/HF decreased significantly. No significant changes were found for the supine position.

S. Gradl, T. Cibis, J. Lauber, R. Richer, R. Rybalko, N. Pfeiffer, H. Leutheuser, M. Wirth, V. Tscharner, B. M. Eskofier

Appl Sci.

08.12.2017

DOI

Objective: Respiratory inductance plethysmography (RIP) provides an unobtrusive method for measuring breathing characteristics. Accurately adjusted RIP provides reliable measurements of ventilation during rest and exercise if data are acquired via two elastic measuring bands surrounding the rib cage (RC) and abdomen (AB). Disadvantageously, the most accurate reported adjusted model for RIP in literature-least squares regression-requires simultaneous RIP and flowmeter (FM) data acquisition. An adjustment method without simultaneous measurement (reference-free) of RIP and FM would foster usability enormously. Methods: In this paper, we develop generalizable, functional, and reference-free algorithms for RIP adjustment incorporating anthropometric data. Further, performance of only one-degree of freedom (RC or AB) instead of two (RC and AB) is investigated. We evaluate the algorithms with data from 193 healthy subjects who performed an incremental running test using three different datasets: training, reliability, and validation dataset. The regression equation is improved with machine learning techniques such as sequential forward feature selection and 10-fold cross validation. Results: Using the validation dataset, the best reference-free adjustment model is the combination of both bands with 84.69% breaths within 20% limits of equivalence compared to 43.63% breaths using the best comparable algorithm from literature. Using only one band, we obtain better results using the RC band alone. Conclusion: Reference-free adjustment for RIP reveals tidal volume differences of up to 0.25 l when comparing to the best possible adjustment currently present which needs the simultaneous measurement of RIP and FM. Significance: This demonstrates that RIP has the potential for usage in wide applications in ambulatory settings.

H. Leutheuser, C. Heyde, K. Roecker, A. Gollhofer, B. M Eskofier

IEEE Trans Biomed Eng.

01.12.2017

DOI

Sleep plays a fundamental role in the life of every human. The prevalence of sleep disorders has increased significantly, now affecting up to 50% of the general population. Sleep is usually analyzed by extracting a hypnogram containing sleep stages. The gold standard method polysomnography (PSG) requires subjects to stay overnight in a sleep laboratory and to wear a series of obtrusive devices. This work presents an easy to use method to perform somnography at home using unobtrusive motion sensors. Ten healthy male subjects were recorded during two consecutive nights. Sensors from the Shimmer platform were placed in the bed to record accelerometer data, while reference hypnograms were collected using a SOMNOwatch system. A series of filters were used to extract a motion feature in 30 second epochs from the accelerometer signals. The feature was used together with the ground truth information to train a Naive Bayes classifiers that distinguished wakefulness, REM and non-REM sleep. Additionally the algorithm was implemented on an Android mobile phone. Averaged over all subjects, the classifier had a mean accuracy of 79.0 % (SD 9.2%) for the three classes. The mobile phone implementation was able to run in realtime during all experiments. In future this will lead to a method for simple and unobtrusive somnography using mobile phones.

S. Gradl, H. Leutheuser, P. Kugler, T. Biermann, S. Kreil, J. Kornhuber, M. Bergner, B. M. Eskofier

In Proc: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

03.07.2017

DOI

Respiratory motion analysis based on range imaging (RI) has emerged as a popular means of generating respiration surrogates to guide motion management strategies in computer-assisted interventions. However, existing approaches employ heuristics, require substantial manual interaction, or yield highly redundant information. In this paper, we propose a framework that uses preprocedurally obtained 4-D shape priors from patient-specific breathing patterns to drive intraprocedural RI-based real-time respiratory motion analysis. As the first contribution, we present a shape motion model enabling an unsupervised decomposition of respiration induced high-dimensional body surface displacement fields into a low-dimensional representation encoding thoracic and abdominal breathing. Second, we propose a method designed for GPU architectures to quickly and robustly align our models to high-coverage multiview RI body surface data. With our fully automatic method, we obtain respiration surrogates yielding a Pearson correlation coefficient (PCC) of 0.98 with conventional surrogates based on manually selected regions on RI body surface data. Compared to impedance pneumography as a respiration signal that measures the change of lung volume, we obtain a PCC of 0.96. Using off-the-shelf hardware, our framework enables high temporal resolution respiration analysis at 50 Hz.

J. Wasza, P. Fischer, H. Leutheuser, T. Oefner, C. Bert, A. Maier, J. Hornegger

IEEE Trans Biomed Eng.

01.03.2016

DOI

Epilepsy is a disease of the central nervous system. Nearly 70% of people with epilepsy respond to a proper treatment, but for a successful therapy of epilepsy, physicians need to know if and when seizures occur. The gold standard diagnosis tool video-electroencephalography (vEEG) requires patients to stay at hospital for several days. A wearable sensor system, e.g. a wristband, serving as diagnostic tool or event monitor, would allow unobtrusive ambulatory long-term monitoring while reducing costs. Previous studies showed that seizures with motor symptoms such as generalized tonic-clonic seizures can be detected by measuring the electrodermal activity (EDA) and motion measuring acceleration (ACC). In this study, EDA and ACC from 8 patients were analyzed. In extension to previous studies, different types of seizures, including seizures without motor activity, were taken into account. A hierarchical classification approach was implemented in order to detect different types of epileptic seizures using data from wearable sensors. Using a k-nearest neighbor (kNN) classifier an overall sensitivity of 89.1% and an overall specificity of 93.1% were achieved, for seizures without motor activity the sensitivity was 97.1% and the specificity was 92.9%. The presented method is a first step towards a reliable ambulatory monitoring system for epileptic seizures with and without motor activity.

B. E. Heldberg, T. Kautz, H. Leutheuser, R. Hopfeng\"artner, B. Kasper, B. M. Eskofier

In Proc: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

25.08.2015

DOI

Medical diagnosis is the first level for recognition and treatment of diseases. To realize fast diagnosis, we propose a concept of a basic framework for the underwater monitoring of a diver’s ECG signal, including an alert system that warns the diver of predefined medical emergency situations. The framework contains QRS detection, heart rate calculation and an alert system. After performing a predefined study protocol, the algorithm’s accuracy was evaluated with 10 subjects in a dry environment and with 5 subjects in an underwater environment. The results showed that, in 3 out of 5 dives as well as in dry environment, data transmission remained stable. In these cases, the subjects were able to trigger the alert system. The evaluated data showed a clear ECG signal with a QRS detection accuracy of 90%. Thus, the proposed framework has the potential to detect and to warn of health risks. Further developments of this sample concept can imply an extension for monitoring different biomedical parameters.

T. Cibis, B. Groh, H. Leutheuser, B. M. Eskofier

In Proc: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

25.08.2015

DOI

Purpose Exercise and physical activity is a driving force for mental health. Major challenges in the treatment of psychological diseases are accurate activity profiles and the adherence to exercise intervention programs. We present the development and validation of CHRONACT, a wearable realtime activity tracker based on inertial sensor data to support mental health. Methods CHRONACT comprised a Human Activity Recognition (HAR) algorithm that determined activity levels based on their Metabolic Equivalent of Task (MET) with sensors on ankle and wrist. Special emphasis was put on wearability, real-time data analysis and runtime to be able to use the system as augmented feedback device. For the development, data of 47 healthy subjects performing clinical intervention program activities were collected to train different classification models. The most suitable model according to the accuracy and processing power tradeoff was selected for an embedded implementation on CHRONACT. Results A validation trial (six subjects, 6 h of data) showed the accuracy of the system with a classification rate of 85.6%. The main source of error was identified in acyclic activities that contained activity bouts of neighboring classes. The runtime of the system was more than 7 days and continuous result logging was available for 39 h. Conclusions In future applications, the CHRONACT system can be used to create accurate and unobtrusive patient activity profiles. Furthermore, the system is ready to assess the effects of individual augmented feedback for exercise adherence.

U. Jensen, H. Leutheuser, S. Hofmann, B. Schuepferling, G. Suttner, K. Seiler, J. Kornhuber, B. M Eskofier

Biomed Eng Lett.

18.07.2015

DOI

Athletes and their coaches aim for enhancing the sports performance. Collecting data from athletes, transforming them into useful information related to their sports performance (e.g., their type of gait), and transmitting the information to the coaches supports the enhancement. The types of gait standing, walking, and running were often examined. Lack of research remains for the two types of running, jogging and sprinting. In this work, standing, walking, jogging, and sprinting were classified with a single inertial-magnetic measurement unit that was placed at a novel position at the trunk. A comparison was made between classification systems using different combinations of accelerometer, gyroscope, and magnetometer data as well as different classifiers (Naïve Bayes, k-Nearest Neighbors, Support Vector Machine, Adaptive Boosting). After collecting data from 15 male subjects, the data were preprocessed, features were extracted and selected, and the data were classified. All classification systems were successful. With a mean true positive rate of 95.68% ±1.80%, the classification system using accelerometer and gyroscope data as well as the Naïve Bayes classifier performed best. The classification system can be used for applications in sport and sports performance analysis in particular.

K. Full, H. Leutheuser, J. Schlessman, R. Armitage, B. M. Eskofier

In Proc: IEEE-EMBS 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

09.06.2015

DOI

Early detection of arrhythmic beats in the electrocardiogram (ECG) signal could improve the identification of patients at risk from sudden death, for example due to coronary heart disease. We present a mobile, hierarchical classification system (three stages in total) using complete databases with the aim to provide instantaneous analysis in case of symptoms and–if necessary–the recommendation to visit an emergency department. In this work, we give more details about the training process of the second stage classifier. The Linear Regression classifier achieved the smallest false negative rate of 14.06% with an accuracy of 66.19% after feature selection. It has to be investigated whether the hierarchical classification system has–in its entirety–better performance orientating on the false negative rate or the accuracy for the second stage classifier. The complete hierarchical classification system has the potential to provide automated, accurate ECG arrhythmia detection that can easily be integrated in daily life.

H. Leutheuser, T. Gottschalk, L. Anneken, M. Struck, A. Heuberger, M. Arnold, S. Achenbach, B. M. Eskofier

In Proc: Conference on Mobile and Information Technologies in Medicine (MobileMed)

20.11.2014

Analysis of electroencephalography (EEG) recorded during movement is often aggravated or even completely hindered by electromyogenic artifacts. This is caused by the overlapping frequencies of brain and myogenic activity and the higher amplitude of the myogenic signals. One commonly employed computational technique to reduce these types of artifacts is Independent Component Analysis (ICA). ICA estimates statistically independent components (ICs) that, when linearly combined, closely match the input (sensor) data. Removing the ICs that represent artifact sources and re-mixing the sources returns the input data with reduced noise activity. ICs of real-world data are usually not perfectly separated, actual sources, but a mixture of these sources. Adding additional input signals, predominantly generated by a single IC that is already part of the original sensor data, should increase that IC's separability. We conducted this study to evaluate this concept for ICA-based electromyogenic artifact reduction in EEG using EMG signals as additional inputs. To acquire the appropriate data we worked with nine human volunteers. The EEG and EMG were recorded while the study volunteers performed seven exercises designed to produce a wide range of representative myogenic artifacts. To evaluate the effect of the EMG signals we estimated the sources of each dataset once with and once without the EMG data. The ICs were automatically classified as either `myogenic' or `non-myogenic'. We removed the former before back projection. Afterwards we calculated an objective measure to quantify the artifact reduction and assess the effect of including EMG signals. Our study showed that the ICA-based reduction of electromyogenic artifacts can be improved by including the EMG data of artifact-inducing muscles. This approach could prove beneficial for locomotor disorder research, brain-computer interfaces, neurofeedback, and most other areas where brain activity during movement has to be analyzed.

F. Gabsteiger, H. Leutheuser, P. Reis, M. Lochmann, B. M. Eskofier

In Proc: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

26.08.2014

DOI

Respiratory inductive plethysmography (RIP) has been introduced as an alternative for measuring ventilation by means of body surface displacement (diameter changes in rib cage and abdomen). Using a posteriori calibration, it has been shown that RIP may provide accurate measurements for ventilatory tidal volume under exercise conditions. Methods for a priori calibration would facilitate the application of RIP. Currently, to the best knowledge of the authors, none of the existing ambulant procedures for RIP calibration can be used a priori for valid subsequent measurements of ventilatory volume under exercise conditions. The purpose of this study is to develop and validate a priori calibration algorithms for ambulant application of RIP data recorded in running exercise. We calculated Volume Motion Coefficients (VMCs) using seven different models on resting data and compared the root mean squared error (RMSE) of each model applied on running data. Least squares approximation (LSQ) without offset of a two-degree-of-freedom model achieved the lowest RMSE value. In this work, we showed that a priori calibration of RIP exercise data is possible using VMCs calculated from 5 min resting phase where RIP and flowmeter measurements were performed simultaneously. The results demonstrate that RIP has the potential for usage in ambulant applications.

H. Leutheuser, C. Heyde, A. Gollhofer, B. M Eskofier

In Proc: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

26.08.2014

DOI

The electrocardiogram (ECG) is a key diagnostic tool in heart disease and may serve to detect ischemia, arrhythmias, and other conditions. Automatic, low cost monitoring of the ECG signal could be used to provide instantaneous analysis in case of symptoms and may trigger the presentation to the emergency department. Currently, since mobile devices (smartphones, tablets) are an integral part of daily life, they could form an ideal basis for automatic and low cost monitoring solution of the ECG signal. In this work, we aim for a realtime classification system for arrhythmia detection that is able to run on Android-based mobile devices. Our analysis is based on 70% of the MIT-BIH Arrhythmia and on 70% of the MIT-BIH Supraventricular Arrhythmia databases. The remaining 30% are reserved for the final evaluation. We detected the R-peaks with a QRS detection algorithm and based on the detected R-peaks, we calculated 16 features (statistical, heartbeat, and template-based). With these features and four different feature subsets we trained 8 classifiers using the Embedded Classification Software Toolbox (ECST) and compared the computational costs for each classification decision and the memory demand for each classifier. We conclude that the C4.5 classifier is best for our two-class classification problem (distinction of normal and abnormal heartbeats) with an accuracy of 91.6%. This classifier still needs a detailed feature selection evaluation. Our next steps are implementing the C4.5 classifier for Android-based mobile devices and evaluating the final system using the remaining 30% of the two used databases.

H. Leutheuser, S. Gradl, P. Kugler, L. Anneken, M. Arnold, S. Achenbach, B. M. Eskofier

In Proc: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

26.08.2014

DOI

Insufficient physical activity is the 4th leading risk factor for mortality. The physical activity of a person is reflected in the walking behavior. Different methods for the calculation of the accurate step number exists and most of them are evaluated using different walking speeds measured on a treadmill or using a small sample size of overground walking. In this paper, we introduce the BaSA (Basic Step Activities) dataset consisting of four different step activities (walking, jogging, ascending, and descending stairs) that were performed under natural conditions. We further compare two step segmentation algorithms (a simple peak detection algorithm vs. subsequence Dynamic Time Warping (sDTW)). We calculated a multivariate Analysis of Variance (ANOVA) with repeated measures followed by multiple dependent t-tests with Bonferroni correction to test for significant differences in the two algorithms. sDTW performed equally good compared to the peak detection algorithm, but was not considerably better. In further analysis, continuous, real walking signals with transitions from one step activity to the other step activity should be considered to investigate the adaptability of these two step segmentation algorithms.

H. Leutheuser, S. Doelfel, D. Schuldhaus, S. Reinfelder, B. M. Eskofier

In Proc: IEEE-EMBS 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

16.06.2014

DOI

Traditionally, electroencephalography (EEG) recorded during movement has been considered too noise prone to allow for sophisticated analysis. Superimposed electromyogenic activity interferes and masks the EEG signal. Presently, computational techniques such as Independent Component Analysis allow reduction of these artifacts. However, to date, it is relied on the user to select the artifact-contaminated components to reject. To automate this process and to reduce user dependent factors, we trained a support vector machine (SVM) to assist the user in choosing the independent components (ICs) most influenced by electromyogenic artifacts. We designed and conducted a study with specific neck and body movement exercises and collected data from five human participants (35 datasets total). After preprocessing, we decomposed the data by applying the Adaptive Mixture of Independent Component Analysis (AMICA) algorithm. An expert labeled the ICs found in the EEG recordings after decomposition as either ‘myogenic activity’ or ‘non-myogenic activity’. Afterwards, the classifier was evaluated on the dataset of one participant, whose data were not used in the training phase, and obtained 93% sensitivity and 96% specificity. Our study was designed to cover a diverse selection of exercises that stimulate the musculature that most interferes in EEG recordings during movement. This selection should produce similar artifact patterns as seen in most exercises or movements. Although unfamiliar exercises could result in worse classification performance, the results are expected to be equivalent to ours. Our study showed that this tool can help EEG analysis by reliably and efficiently choosing electromyogenic artifact contaminated components after AMICA decomposition, ultimately increasing the speed of data processing.

F. Gabsteiger, H. Leutheuser, P. Reis, M. Lochmann, B. M. Eskofier

In Proc: 15th International Conference on Biomedical Engineering (ICBME)

15.06.2014

DOI

Introduction: The aim of this study was to provide a rationale for future validations of a priori calibrated respiratory inductance plethysmography (RIP) when used under exercise conditions. Therefore, the validity of a posteriori-adjusted gain factors and accuracy in resultant breath-by-breath RIP data recorded under resting and running conditions were examined. Methods: Healthy subjects, 98 men and 88 women (mean ± SD: height = 175.6 ± 8.9 cm, weight = 68.9 ± 11.1 kg, age = 27.1 ± 8.3 yr), underwent a standardized test protocol, including a period of standing still, an incremental running test on treadmill, and multiple periods of recovery. Least square regression was used to calculate gain factors, respectively, for complete individual data sets as well as several data subsets. In comparison with flowmeter data, the validity of RIP in breathing rate (fR) and inspiratory tidal volume (VTIN) were examined using coefficients of determination (R). Accuracy was estimated from equivalence statistics. Results: Calculated gains between different data subsets showed no equivalence. After gain adjustment for the complete individual data set, fR and VTIN between methods were highly correlated (R = 0.96 ± 0.04 and 0.91 ± 0.05, respectively) in all subjects. Under conditions of standing still, treadmill running, and recovery, 86%, 98%, and 94% (fR) and 78%, 97%, and 88% (VTIN), respectively, of all breaths were accurately measured within ± 20% limits of equivalence. Conclusion: In case of the best possible gain adjustment, RIP confidentially estimates tidal volume accurately within ± 20% under exercise conditions. Our results can be used as a rationale for future validations of a priori calibration procedures.

C. Heyde, H. Leutheuser, B. M. Eskofier, K. Roecker, A. Gollhofer

Med Sci Sports Exerc.

01.03.2014

DOI

Insufficient physical activity is the 4th leading risk factor for mortality. Methods for assessing the individual daily life activity (DLA) are of major interest in order to monitor the current health status and to provide feedback about the individual quality of life. The conventional assessment of DLAs with self-reports induces problems like reliability, validity, and sensitivity. The assessment of DLAs with small and light-weight wearable sensors (e.g. inertial measurement units) provides a reliable and objective method. State-of-the-art human physical activity classification systems differ in e.g. the number and kind of sensors, the performed activities, and the sampling rate. Hence, it is difficult to compare newly proposed classification algorithms to existing approaches in literature and no commonly used dataset exists. We generated a publicly available benchmark dataset for the classification of DLAs. Inertial data were recorded with four sensor nodes, each consisting of a triaxial accelerometer and a triaxial gyroscope, placed on wrist, hip, chest, and ankle. Further, we developed a novel, hierarchical, multi-sensor based classification system for the distinction of a large set of DLAs. Our hierarchical classification system reached an overall mean classification rate of 89.6% and was diligently compared to existing state-of-the-art algorithms using our benchmark dataset. For future research, the dataset can be used in the evaluation process of new classification algorithms and could speed up the process of getting the best performing and most appropriate DLA classification system.

H. Leutheuser, D. Schuldhaus, B. M. Eskofier

PLOS ONE

09.10.2013

DOI

Electromyogenic or muscle artifacts constitute a major problem in studies involving electroencephalography (EEG) measurements. This is because the rather low signal activity of the brain is overlaid by comparably high signal activity of muscles, especially neck muscles. Hence, recording an artifact-free EEG signal during movement or physical exercise is not, to the best knowledge of the authors, feasible at the moment. Nevertheless, EEG measurements are used in a variety of different fields like diagnosing epilepsy and other brain related diseases or in biofeedback for athletes. Muscle artifacts can be recorded using electromyography (EMG). Various computational methods for the reduction of muscle artifacts in EEG data exist like the ICA algorithm InfoMax and the AMICA algorithm. However, there exists no objective measure to compare different algorithms concerning their performance on EEG data. We defined a test protocol with specific neck and body movements and measured EEG and EMG simultaneously to compare the InfoMax algorithm and the AMICA algorithm. A novel objective measure enabled to compare both algorithms according to their performance. Results showed that the AMICA algorithm outperformed the InfoMax algorithm. In further research, we will continue using the established objective measure to test the performance of other algorithms for the reduction of artifacts.

H. Leutheuser, F. Gabsteiger, F. Hebenstreit, P. Reis, M. Lochmann, B. M. Eskofier

In Proc: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

03.07.2013

DOI

The normal oscillation of the heart rate is called Heart Rate Variability (HRV). HRV parameters change under different conditions like rest, physical exercise, mental stress, and body posture changes. However, results how HRV parameters adapt to physical exercise have been inconsistent. This study investigated how different HRV parameters changed during one hour of running. We used datasets of 295 athletes where each dataset had a total length of about 65 minutes. Data was divided in segments of five minutes and three HRV parameters and one kinematic parameter were calculated for each segment. We applied two different analysis of variance (ANOVA) models to analyze the differences in the means of each segment for every parameter. The two ANOVA models were univariate ANOVA with repeated measures and multivariate ANOVA with repeated measures. The obligatory post-hoc procedure consisted of multiple dependent t tests with Bonferroni correction. We investigated the last three segments of the parameters in more detail and detected a delay of one minute between varying running speed and measured heart rate. Hence, the circulatory system of our population needed one minute to adapt to a change in running speed. The method we provided can be used to further investigate more HRV parameters.

H. Leutheuser, B. M. Eskofier

Int J Comp Sci Sport

01.01.2013