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  • 1.
    Aare, Magnus
    et al.
    KTH, School of Technology and Health (STH), Neuronic Engineering (Closed 20130701).
    Kleiven, Svein
    KTH, School of Technology and Health (STH), Neuronic Engineering (Closed 20130701).
    Evaluation of head response to ballistic helmet impacts, using FEM2003Conference paper (Refereed)
  • 2.
    Aare, Magnus
    et al.
    KTH, School of Technology and Health (STH), Neuronic Engineering (Closed 20130701).
    Kleiven, Svein
    KTH, School of Technology and Health (STH), Neuronic Engineering (Closed 20130701).
    Halldin, Peter
    KTH, School of Technology and Health (STH), Neuronic Engineering (Closed 20130701).
    Proposed global injury thresholds for oblique helmet impacts2003Conference paper (Refereed)
  • 3.
    Abbaspour, Sara
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Surface EMG signal processing: Removing ECG interferences and classifying hand movements2017In: Medicinteknikdagarna 2017 MTD 2017, Västerås, Sweden, 2017Conference paper (Refereed)
  • 4.
    Abbaspour, Sara
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Electromyography signal analysis: Electrocardiogram artifact removal and classifying hand movements2018In: World Congress on Medical Physics and Biomedical Engineering IUPESM, 2018Conference paper (Refereed)
  • 5.
    Abedan Kondori, Farid
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Yousefi, Shahrouz
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Li, Haibo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Sonning, Samuel
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Sonning, Sabina
    3D Head Pose Estimation Using the Kinect2011Conference paper (Refereed)
    Abstract [en]

    Head pose estimation plays an essential role for bridging the information gap between humans and computers. Conventional head pose estimation methods are mostly done in images captured by cameras. However accurate and robust pose estimation is often problematic. In this paper we present an algorithm for recovering the six degrees of freedom (DOF) of motion of a head from a sequence of range images taken by the Microsoft Kinectfor Xbox 360. The proposed algorithm utilizes a least-squares minimization of the difference between themeasured rate of change of depth at a point and the rate predicted by the depth rate constraint equation. We segment the human head from its surroundings and background, and then we estimate the head motion. Our system has the capability to recover the six DOF of the head motion of multiple people in one image. Theproposed system is evaluated in our lab and presents superior results.

  • 6.
    Abedan Kondori, Farid
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Yousefi, Shahrouz
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Liu, Li
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Active human gesture capture for diagnosing and treating movement disorders2013Conference paper (Other academic)
    Abstract [en]

    Movement disorders prevent many people fromenjoying their daily lives. As with other diseases, diagnosisand analysis are key issues in treating such disorders.Computer vision-based motion capture systems are helpfultools for accomplishing this task. However Classical motiontracking systems suffer from several limitations. First theyare not cost effective. Second these systems cannot detectminute motions accurately. Finally they are spatially limitedto the lab environment where the system is installed. In thisproject, we propose an innovative solution to solve the abovementionedissues. Mounting the camera on human body, webuild a convenient, low cost motion capture system that canbe used by the patient in daily-life activities. We refer tothis system as active motion capture, which is not confinedto the lab environment. Real-time experiments in our labrevealed the robustness and accuracy of the system.

  • 7. Abrahamsson, S.
    et al.
    Blom, Hans
    KTH, School of Engineering Sciences (SCI), Applied Physics.
    Agostinho, A.
    Jans, Daniel
    KTH, School of Engineering Sciences (SCI), Applied Physics.
    Jost, A.
    Müller, M.
    Nilsson, Linnea
    KTH, School of Engineering Sciences (SCI), Applied Physics.
    Bernhem, K.
    Lambert, T. J.
    Heintzmann, R.
    Brismar, Hjalmar
    KTH, School of Engineering Sciences (SCI), Applied Physics.
    Multifocus structured illumination microscopy for fast volumetric super-resolution imaging2017In: Biomedical Optics Express, ISSN 2156-7085, E-ISSN 2156-7085, Vol. 8, no 9, p. 4135-4140, article id #294866Article in journal (Refereed)
    Abstract [en]

    We here report for the first time the synergistic implementation of structured illumination microscopy (SIM) and multifocus microscopy (MFM). This imaging modality is designed to alleviate the problem of insufficient volumetric acquisition speed in superresolution biological imaging. SIM is a wide-field super-resolution technique that allows imaging with visible light beyond the classical diffraction limit. Employing multifocus diffractive optics we obtain simultaneous wide-field 3D imaging capability in the SIM acquisition sequence, improving volumetric acquisition speed by an order of magnitude. Imaging performance is demonstrated on biological specimens.

  • 8.
    Abtahi, Farhad
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Aspects of Electrical Bioimpedance Spectrum Estimation2014Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Electrical bioimpedance spectroscopy (EBIS) has been used to assess the status or composition of various types of tissue, and examples of EBIS include body composition analysis (BCA) and tissue characterisation for skin cancer detection. EBIS is a non-invasive method that has the potential to provide a large amount of information for diagnosis or monitoring purposes, such as the monitoring of pulmonary oedema, i.e., fluid accumulation in the lungs. However, in many cases, systems based on EBIS have not become generally accepted in clinical practice. Possible reasons behind the low acceptance of EBIS could involve inaccurate models; artefacts, such as those from movements; measurement errors; and estimation errors. Previous thoracic EBIS measurements aimed at pulmonary oedema have shown some uncertainties in their results, making it difficult to produce trustworthy monitoring methods. The current research hypothesis was that these uncertainties mostly originate from estimation errors. In particular, time-varying behaviours of the thorax, e.g., respiratory and cardiac activity, can cause estimation errors, which make it tricky to detect the slowly varying behaviour of this system, i.e., pulmonary oedema.

    The aim of this thesis is to investigate potential sources of estimation error in transthoracic impedance spectroscopy (TIS) for pulmonary oedema detection and to propose methods to prevent or compensate for these errors.   This work is mainly focused on two aspects of impedance spectrum estimation: first, the problems associated with the delay between estimations of spectrum samples in the frequency-sweep technique and second, the influence of undersampling (a result of impedance estimation times) when estimating an EBIS spectrum. The delay between frequency sweeps can produce huge errors when analysing EBIS spectra, but its effect decreases with averaging or low-pass filtering, which is a common and simple method for monitoring the time-invariant behaviour of a system. The results show the importance of the undersampling effect as the main estimation error that can cause uncertainty in TIS measurements.  The best time for dealing with this error is during the design process, when the system can be designed to avoid this error or with the possibility to compensate for the error during analysis. A case study of monitoring pulmonary oedema is used to assess the effect of these two estimation errors. However, the results can be generalised to any case for identifying the slowly varying behaviour of physiological systems that also display higher frequency variations.  Finally, some suggestions for designing an EBIS measurement system and analysis methods to avoid or compensate for these estimation errors are discussed.

  • 9.
    Abtahi, Farhad
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Towards Heart Rate Variability Tools in P-Health: Pervasive, Preventive, Predictive and Personalized2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Heart rate variability (HRV) has received much attention lately. It has been shown that HRV can be used to monitor the autonomic nervous system and to detect autonomic dysfunction, especially vagal dysfunction. Reduced HRV is associated with several diseases and has also been suggested as a predictor of poor outcomes and sudden cardiac death. HRV is, however, not yet widely accepted as a clinical tool and is mostly used for research. Advances in neuroimmunity with an improved understanding of the link between the nervous and immune systems have opened a new potential arena for HRV applications. An example is when systemic inflammation and autoimmune disease are primarily caused by low vagal activity; it can be detected and prognosticated by reduced HRV. This thesis is the result of several technical development steps and exploratory research where HRV is applied as a prognostic diagnostic tool with preventive potential. The main objectives were 1) to develop an affordable tool for the effective analysis of HRV, 2) to study the correlation between HRV and pro-inflammatory markers and the potential degree of activity in the cholinergic anti-inflammatory pathway, and 3) to develop a biofeedback application intended for support of personal capability to increase the vagal activity as reflected in increased HRV. Written as a compilation thesis, the methodology and the results of each study are presented in each appended paper. In the thesis frame/summary chapter, a summary of each of the included papers is presented, grouped by topic and with their connections. The summary of the results shows that the developed tools may accurately register and properly analyse and potentially influence HRV through the designed biofeedback game. HRV can be used as a prognostic tool, not just in traditional healthcare with a focus on illness but also in wellness. By using these tools for the early detection of decreased HRV, prompt intervention may be possible, enabling the prevention of disease. Gamification and serious gaming is a potential platform to motivate people to follow a routine of exercise that might, through biofeedback, improve HRV and thereby health.

  • 10.
    Abtahi, Farhad
    et al.
    Karolinska Institutet.
    Anund, Anna
    Fors, Carina
    Seoane, Fernando
    University of Borås, Faculty of Textiles, Engineering and Business. University of Borås, Faculty of Caring Science, Work Life and Social Welfare. Karolinska Institutet.
    Lindecrantz, Kaj
    University of Borås, Faculty of Textiles, Engineering and Business. Karolinska Institutet.
    Association of Drivers’ sleepiness with heart rate variability. A Pilot Study with Drivers on Real Road2017Conference paper (Other academic)
  • 11. Abtahi, Farhad
    et al.
    Forsman, Mikael
    Diaz-Olivazrez, Jose A.
    KTH, School of Technology and Health (STH).
    Yang, Liyun
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Ergonomics.
    Lu, Ke
    KTH, School of Technology and Health (STH).
    Eklund, Jörgen
    KTH, School of Technology and Health (STH).
    Lindecrantz, Kaj
    KTH, School of Technology and Health (STH).
    Seoane, Fernando
    Teriö, Heikki
    Mediavilla Martinez, Cesar
    Aso, Santiago
    Tiemann, Christian
    Big Data & Wearable Sensors Ensuring Safety and Health @Work2017In: GLOBAL HEALTH 2017, The Sixth International Conference on Global Health Challenges, 2017Conference paper (Refereed)
    Abstract [en]

    —Work-related injuries and disorders constitute a major burden and cost for employers, society in general and workers in particular. We@Work is a project that aims to develop an integrated solution for promoting and supporting a safe and healthy working life by combining wearable technologies, Big Data analytics, ergonomics, and information and communication technologies. The We@Work solution aims to support the worker and employer to ensure a healthy working life through pervasive monitoring for early warnings, prompt detection of capacity-loss and accurate risk assessments at workplace as well as self-management of a healthy working life. A multiservice platform will allow unobtrusive data collection at workplaces. Big Data analytics will provide real-time information useful to prevent work injuries and support healthy working life

  • 12.
    Abtahi, Farhad
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems. Karolinska Institutet, Sweden.
    Hilderman, Marie
    Bruchfeld, Annette
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems. University of Borås, Sweden.
    Janerot-Sjöberg, Birgitta
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Imaging. Karolinska Institutet, Sweden.
    Lindecrantz, Kaj
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems. Karolinska Institutet, Sweden.
    Pro-inflammatory Blood Markers and Heart Rate Variability in Apnoea as a Reflection of Basal Vagal ToneManuscript (preprint) (Other academic)
    Abstract [en]

    Pro-inflammatory cytokines play a crucial role in inflammatory response, which istightly regulated by the nervous system to avoid the damage caused by inflammation. There isevidence for a cholinergic anti-inflammatory pathway that includes afferent and efferent vagalnerves that sense the inflammation and stimulate the anti-inflammatory response. Non-functionalanti-inflammatory response might lead to excessive and chronic inflammation e.g., rheumatoidarthritis (RA), inflammatory bowel disease (IBD), and poor outcome. Heart rate variability(HRV) has been proposed as a potential tool to monitor the level of anti-inflammatory activitythrough the monitoring of vagal activity. In this paper, the association of pro-inflammatorymarkers with HRV indices is evaluated. We used a database called “Heart Biomarker Evaluationin Apnea Treatment (HeartBEAT)” that consists of 6±2 hours of Electrocardiogram (ECG)recordings during nocturnal sleep from 318 patients at baseline and 301of them at 3 monthsfollow-up. HRV indices are calculated from ECG recordings of 5-360 minutes. The results showa statistically significant correlation between heart rate (HR) and pro-inflammatory cytokines,independent of duration of ECG analysis. HRV indices e.g., standard deviation of all RRintervals (SDNN) show an inverse relation to the pro-inflammatory cytokines. Longer ECGrecordings show a higher potential to reflect the level of anti-inflammatory response. In light oftheories for the cholinergic anti-inflammatory pathway, a combination of HR and HRV as areflection of basal vagal activity might be a potential prognostic tool for interventional guidance.

  • 13.
    Abtahi, Farhad
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Ji, Guangchao
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Lu, Ke
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Rodby, Kristian
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    A knitted garment using intarsia technique for Heart Rate Variability biofeedback: Evaluation of initial prototype2015In: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, IEEE , 2015, Vol. 2015, p. 3121-3124Conference paper (Refereed)
    Abstract [en]

    Heart rate variability (HRV) biofeedback is a method based on paced breathing at specific rate called resonance frequency by giving online feedbacks from user respiration and its effect on HRV. Since the HRV is also influence by different factors like stress and emotions, stress related to an unfamiliar measurement device, cables and skin electrodes may cover the underling effect of such kind of intervention. Wearable systems are usually considered as intuitive solutions which are more familiar to the end-user and can help to improve usability and hence reducing the stress. In this work, a prototype of a knitted garment using intarsia technique is developed and evaluated. Results show the satisfactory level of quality for Electrocardiogram and thoracic electrical bioimpedance i.e. for respiration monitoring as a part of HRV biofeedback system. Using intarsia technique and conductive yarn for making the connection instead of cables will reduce the complexity of fabrication in textile production and hence reduce the final costs in a final commercial product. Further development of garment and Android application is ongoing and usability and efficiency of final prototype will be evaluated in detail.

  • 14.
    Abtahi, Farhad
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Ji, Guangchao
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Lu, Ke
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Rödby, Kristian
    Högskolan i Borås, Akademin för textil, teknik och ekonomi.
    Björlin, Anders
    Kiwok AB.
    Östlund, Anders
    Kiwok AB.
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems. Högskolan i Borås, Akademin för vård, arbetsliv och välfärd.
    Lindecrantz, Kaj
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Textile-Electronic Integration in Wearable Measurement Garments for Pervasive Healthcare Monitoring2015Conference paper (Other academic)
  • 15.
    Abtahi, Farhad
    et al.
    KTH-School of Technology and Health.
    Ji, Guangchao
    KTH-School of Technology and Health.
    Lu, Ke
    KTH-School of Technology and Health.
    Rödby, Kristian
    University of Borås, Faculty of Textiles, Engineering and Business.
    Björlin, Anders
    Kiwok AB.
    Östlund, Anders
    Kiwok AB.
    Seoane, Fernando
    University of Borås, Faculty of Caring Science, Work Life and Social Welfare. KTH-School of Technology and Health.
    Lindecrantz, Kaj
    KTH-School of Technology and Health.
    Textile-Electronic Integration in Wearable Measurement Garments for Pervasive Healthcare Monitoring2015Conference paper (Other academic)
  • 16.
    Abtahi, Farhad
    et al.
    KTH-School of Technology and Health.
    Ji, Guangchao
    KTH-School of Technology and Health.
    Lu, Ke
    KTH-School of Technology and Health.
    Rödby, Kristian
    University of Borås, Faculty of Textiles, Engineering and Business.
    Seoane, Fernando
    University of Borås, Faculty of Caring Science, Work Life and Social Welfare. KTH-School of Technology and Health.
    A knitted garment using intarsia technique for Heart Rate Variability biofeedback: Evaluation of initial prototype2015In: Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, p. 3121-3124Conference paper (Refereed)
  • 17.
    Abtahi, Farhad
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Lindecrantz, Kaj
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Aslamy, Benjamin
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Boujabir, Imaneh
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    An Affordable ECG and Respiration Monitoring System Based on Raspberry PI and ADAS1000: First Step towards Homecare Applications2015In: 16th Nordic-Baltic Conference on Biomedical Engineering: 16. NBC & 10. MTD 2014 joint conferences. October 14-16, 2014, Gothenburg, Sweden, Springer, 2015, p. 5-8Conference paper (Refereed)
    Abstract [en]

    Homecare is a potential solution for problems associated with an aging population. This may involve several physiological measurements, and hence a flexible but affordable measurement device is needed. In this work, we have designed an ADAS1000-based four-lead electrocardiogram (ECG) and respiration monitoring system. It has been implemented using Raspberry PI as a platform for homecare applications. ADuM chips based on iCoupler technology have been used to achieve electrical isolation as required by IEC 60601 and IEC 60950 for patient safety. The result proved the potential of Raspberry PI for the design of a compact, affordable, and medically safe measurement device. Further work involves developing a more flexible software for collecting measurements from different devices (measuring, e.g., blood pressure, weight, impedance spectroscopy, blood glucose) through Bluetooth or user input and integrating them into a cloud-based homecare system.

  • 18.
    Abtahi, Farhad
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Lu, Ke
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Dizon, M
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Johansson, M
    KTH-School of Technology and Health.
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems. Högskolan i Borås.
    Lindecrantz, Kaj
    KTH, School of Technology and Health (STH), Medical Engineering, Computer and Electronic Engineering. Högskolan i Borås, Akademin för vård, arbetsliv och välfärd.
    Evaluating Atrial Fibrillation Detection Algorithm based on Heart Rate Variability analysis2015In: Medicinteknikdagarna, Uppsala: Svensk förening för medicinsk teknik och fysik , 2015Conference paper (Refereed)
  • 19.
    Abtahi, Farhad
    et al.
    KTH-School of Technology and Health.
    Lu, Ke
    KTH-School of Technology and Health.
    Dizon, M
    KTH-School of Technology and Health.
    Johansson, M
    KTH-School of Technology and Health.
    Seoane, Fernando
    University of Borås, Faculty of Caring Science, Work Life and Social Welfare. KTH-School of Technology and Health.
    Lindecrantz, Kaj
    University of Borås, Faculty of Caring Science, Work Life and Social Welfare. KTH-School of Technology and Health.
    Evaluating Atrial Fibrillation Detection Algorithm based on Heart Rate Variability analysis2015In: Medicinteknikdagarna, Uppsala: Svensk förening för medicinsk teknik och fysik , 2015Conference paper (Refereed)
  • 20.
    Abtahi, Farhad
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Lu, Ke
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Dizon, M
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Johansson, M
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems. Högskolan i Borås, Akademin för vård, arbetsliv och välfärd.
    Lindecrantz, Kaj
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Evaluation of Atrial Fibrillation Detection by using Heart Rate Variability analysis2015Conference paper (Other academic)
  • 21.
    Abtahi, Farhad
    et al.
    KTH-School of Technology and Health.
    Lu, Ke
    KTH-School of Technology and Health.
    Dizon, M
    KTH-School of Technology and Health.
    Johansson, M
    KTH-School of Technology and Health.
    Seoane, Fernando
    University of Borås, Faculty of Caring Science, Work Life and Social Welfare. KTH-School of Technology and Health.
    Lindecrantz, Kaj
    University of Borås, Faculty of Caring Science, Work Life and Social Welfare. KTH-School of Technology and Health.
    Evaluation of Atrial Fibrillation Detection by using Heart Rate Variability analysis2015Conference paper (Other academic)
  • 22.
    Abtahi, Farhad
    et al.
    KTH-School of Technology and Health.
    Lu, Ke
    KTH-School of Technology and Health.
    Guangchao, Li
    KTH-School of Technology and Health.
    Rödby, Kristian
    University of Borås, Faculty of Textiles, Engineering and Business.
    Seoane, Fernando
    University of Borås, Faculty of Caring Science, Work Life and Social Welfare. KTH-School of Technology and Health.
    A Knitted Garment using Intarsia Technique for Heart Rate Variability Biofeedback: Evaluation of Initial Prototype.2015Conference paper (Other academic)
  • 23.
    Abtahi, Farhad
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Lu, Ke
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Guangchao, Li
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Rödby, Kristian
    Högskolan i Borås, Akademin för textil, teknik och ekonomi.
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems. Högskolan i Borås.
    A Knitted Garment using Intarsia Technique for Heart Rate Variability Biofeedback: Evaluation of Initial Prototype.2015Conference paper (Other academic)
  • 24.
    Abtahi, Farhad
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Lindecrantz, Kaj
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Electrical bioimpedance spectroscopy in time-variant systems: Is undersampling always a problem?2014In: Journal of Electrical Bioimpedance, ISSN 1891-5469, Vol. 5, no 1, p. 28-33Article in journal (Refereed)
    Abstract [en]

    During the last decades, Electrical Bioimpedance Spectroscopy (EBIS) has been applied mainly by using the frequency-sweep technique, across a range of many different applications. Traditionally, the tissue under study is considered to be time-invariant and dynamic changes of tissue activity are ignored by treating the changes as a noise source. A new trend in EBIS is simultaneous electrical stimulation with several frequencies, through the application of a multi-sine, rectangular or other waveform. This method can provide measurements fast enough to sample dynamic changes of different tissues, such as cardiac muscle. This high sampling rate comes at a price of reduction in SNR and the increase in complexity of devices. Although the frequency-sweep technique is often inadequate for monitoring the dynamic changes in a variant system, it can be used successfully in applications focused on the time-invariant or slowly-variant part of a system. However, in order to successfully use frequency-sweep EBIS for monitoring time-variant systems, it is paramount to consider the effects of aliasing and especially the folding of higher frequencies, on the desired frequency e.g. DC level. This paper discusses sub-Nyquist sampling of thoracic EBIS measurements and its application in the case of monitoring pulmonary oedema. It is concluded that by considering aliasing, and with proper implementation of smoothing filters, as well as by using random sampling, frequency-sweep EBIS can be used for assessing time-invariant or slowly-variant properties of time-variant biological systems, even in the presence of aliasing. In general, undersampling is not always a problem, but does always require proper consideration.

  • 25.
    Abtahi, Farhad
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Lindecrantz, Kaj
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Löfgren, Nils
    Elimination of ECG Artefacts in Foetal EEG Using Ensemble Average Subtraction and Wavelet Denoising Methods: A Simulation2014In: XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013, Springer, 2014, p. 551-554Conference paper (Refereed)
    Abstract [en]

    Biological signals recorded from surface electrodes contain interference from other signals which are not desired and should be considered as noise. Heart activity is especially present in EEG and EMG recordings as a noise. In this work, two ECG elimination methods are implemented; ensemble average subtraction (EAS) and wavelet denoising methods. Comparison of these methods has been done by use of simulated signals achieved by adding ECG to neonates EEG. The result shows successful elimination of ECG artifacts by using both methods. In general EAS method which remove estimate of all ECG components from signal is more trustable but it is also harder for implementation due to sensitivity to noise. It is also concluded that EAS behaves like a high-pass filter while wavelet denoising method acts as low-pass filter and hence the choice of one method depends on application.

  • 26.
    Abtahi, Farhad
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Snäll, Jonatan
    KTH, School of Technology and Health (STH).
    Aslamy, Benjamin
    KTH, School of Technology and Health (STH).
    Abtahi, Shirin
    KTH, School of Technology and Health (STH).
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems. University of Boras, Sweden.
    Lindecrantz, Kaj
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems. Karolinska Institute, Sweden.