| 1. |
- Afifi, S., et al.
(författare)
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A Novel Medical Device for Early Detection of Melanoma
- 2019
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Ingår i: Studies in Health Technology and Informatics. - : NLM (Medline). - 0926-9630 .- 1879-8365. ; 261, s. 122-127
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Tidskriftsartikel (refereegranskat)abstract
- Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in decision-making and detecting melanoma at an early stage. We aim to develop a novel handheld medical scanning device dedicated to early detection of melanoma at the primary healthcare with low cost and high performance. However, developing this particular device is very challenging due to the complicated computations required by the embedded diagnosis system. In this paper, we propose a hardware-friendly design for implementing an embedded system by exploiting the recent hardware advances in reconfigurable computing. The developed embedded system achieved optimized implementation results for the hardware resource utilization, power consumption, detection speed and processing time with high classification accuracy rate using real data for melanoma detection. Consequently, the proposed embedded diagnosis system meets the critical embedded systems constraints, which is capable for integration towards a cost- and energy-efficient medical device for early detection of melanoma.
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| 2. |
- Ask, Per, et al.
(författare)
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NovaMedTech - A regional program for supporting new medical technologies in personalized health care
- 2012
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Ingår i: Studies in Health Technology and Informatics. - 9781614990680 ; 177, s. 71-5
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Konferensbidrag (refereegranskat)abstract
- NovaMedTech is an initiative funded from EU structural funds for supporting new medical technologies for personalized health care. It aims at bringing these technologies into clinical use and to the health care market. The program has participants from health care, industry and academia in East middle Sweden. The first three year period of the program was successful in terms of product concepts tried clinically, and number of products brought to a commercialization phase. Further, the program has led to a large number of scientific publications. Among projects supported, we can mention: Intelligent sensor networks; A digital pen to collect medical information about health status from patients; A web-based intelligent stethoscope; Methodologies to measure local blood flow and nutrition using optical techniques; Blood flow assessment from ankle pressure measurements; Technologies for pressure ulcer prevention; An IR thermometer for improved accuracy; A technique that identifies individuals prone to commit suicide among depressed patients; Detection of infectious disease using an electronic nose; Identification of the lactate threshold from breath; Obesity measurements using special software and MR camera; and An optical probe guided tumor resection. During the present three years period emphasis will be on entrepreneurial activities supporting the commercialization and bringing products to the market.
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| 3. |
- Gharehbaghi, Arash, et al.
(författare)
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A Decision Support System for Cardiac Disease Diagnosis Based on Machine Learning Methods
- 2017
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Ingår i: Studies in Health Technology and Informatics. - : IOS Press. - 0926-9630 .- 1879-8365. - 9781614997528 ; 235, s. 43-47
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a decision support system for screening pediatric cardiac disease in primary healthcare centres relying on the heart sound time series analysis. The proposed system employs our processing method which is based on the hidden Markov model for extracting appropriate information from the time series. The binary output resulting from the method is discriminative for the two classes of time series existing in our databank, corresponding to the children with heart disease and the healthy ones. A total 90 children referrals to a university hospital, constituting of 55 healthy and 35 children with congenital heart disease, were enrolled into the study after obtaining the informed consent. Accuracy and sensitivity of the method was estimated to be 86.4% and 85.6%, respectively, showing a superior performance than what a paediatric cardiologist could achieve performing auscultation. The method can be easily implemented using mobile and web technology to develop an easy-To-use tool for paediatric cardiac disease diagnosis. © 2017 European Federation for Medical Informatics (EFMI) and IOS Press.
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| 4. |
- GholamHosseini, Hamid, et al.
(författare)
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Obesity Risk Assessment Model Using Wearable Technology with Personalized Activity, Calorie Expenditure and Health Profile
- 2019
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Ingår i: Studies in Health Technology and Informatics. - : NLM (Medline). - 0926-9630 .- 1879-8365. ; 261, s. 91-96
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Tidskriftsartikel (refereegranskat)abstract
- There is a worldwide increase in the rate of obesity and its related long-term conditions, emphasizing an immediate need to address this modern-age global epidemic of healthy living. Moreover, healthcare spending on long-term or chronic care conditions such as obesity is increasing to the point that requires effective interventions and advancements to reduce the burden of the healthcare. This research focuses on the early risk assessment of overweight/obesity using wearable technology. We establish an individualised health profile that identifies the level of activity and current health status of an individual using real-time activity and vital signs. We developed an algorithm to assess the risk of obesity using the individual's current activity and calorie expenditure. The algorithm was deployed on a smartphone application to collect wearable device data, and user reported data. Based on the collected data, the proposed application assesses the risk of obesity/overweight, measures the current activity level and recommends an optimized calorie plan.
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| 5. |
- Hagblad, Jimmie, et al.
(författare)
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Long term monitoring of blood flow at multiple depths - observations of changes.
- 2012
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Ingår i: Studies in Health Technology and Informatics. - 0926-9630 .- 1879-8365. ; 177, s. 107-112
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Tidskriftsartikel (refereegranskat)abstract
- Detecting reduced circulation, which is a major factor in the development of pressure ulcers, can be done using optical methods. PPG and LDF can be combined and used to evaluate blood flow at different depths. In this study the use of a probe combining PPG and LDF to monitor multiple tissue depths is evaluated. The effects on blood flow and temperature without additional provocation was examined. Measurements were performed during 60 min and the use of an active probe was compared with the use of a semi-active probe turned off a major part of the time. Changes in temperature and blood flow using these probe configurations (active and semi-active probe) are compared; four different 5 min segments during a 60 min measurement. A general increase in both temperature and blood flow is found but this increase could not be concluded to occur due to the light sources of the probe.
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| 6. |
- Rastegar, S., et al.
(författare)
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Estimating Systolic Blood Pressure Using Convolutional Neural Networks
- 2019
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Ingår i: Studies in Health Technology and Informatics. - : NLM (Medline). - 0926-9630 .- 1879-8365. ; 261, s. 143-149
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Tidskriftsartikel (refereegranskat)abstract
- Continuous blood pressure (BP) monitoring can produce a significant amount of digital data, which increases the chance of early diagnosis and improve the rate of survival for people diagnosed with hypertension and Cardiovascular diseases (CVDs). However, mining and processing this vast amount of data are challenging. This research is aimed to address this challenge by proposing a deep learning technique, convolutional neural network (CNN), to estimate the systolic blood pressure (SBP) using electrocardiogram (ECG) and photoplethysmography (PPG) signals. Two different methods are investigated and compared in this research. In the first method, continuous wavelet transform (CWT) and CNN have been employed to estimate the SBP. For the second method, we used random sampling within the stochastic gradient descent (SGD) optimization of CNN and the raw ECG and PPG signals for training the network. The Medical Information Mart for Intensive Care (MIMIC III) database is used for both methods, which split to two parts, 70% for training our network and the remaining used for testing the performance of the network. Both methods are capable of learning how to extract relevant features from the signals. Therefore, there is no need for engineered feature extraction compared to previous works. Our experimental results show high accuracy for both CNN-based methods which make them promising and reliable architectures for SBP estimation.
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| 7. |
- Ödman, Torbjörn, et al.
(författare)
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A study of different fabrics to increase radar cross section of humans
- 2015
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Ingår i: Stud. Health Technol. Informatics. - : IOS Press. - 9781614995159 ; 211, s. 201-206
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Konferensbidrag (refereegranskat)abstract
- This purpose of the study was to increase the visibility on radar for unprotected pedestrians with the aid of conducting fabric. The experiment comprised measurements of four types of fabric to determine the radio frequency properties, such as radar cross section (RCS) for the vehicle radar frequency 77 GHz and transmission (shielding) in the frequency range 3-18 GHz. Two different thicknesses of polypyrrole (PPy) nonvowen fabric were tested and one thickness for 30 % and 40 % stainless steel fabrics respectively. A jacket with the thinner nonvowen material and one with 40 % steel were tested and compared to an unmodified jacket in the RCS measurement. The measurement showed an increase in RCS of 4 dB for the jacket with the 40 % steel lining compared to the unmodified jacket. The transmission measurement was aimed at determining the fabric with the highest transmission of an incoming radio wave. The 30 % steel fabric and the two thicknesses of the nonvowen fabrics were tested. One practical application is for example the use of radar reflective material in search and rescue (SAR) clothes. The study showed that the 30 % steel fabric was the best candidate for further RCS measurements. © 2015 The authors and IOS Press. All rights reserved.
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| 8. |
- Ödman, Torbjörn, et al.
(författare)
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Reflection/Transmission Study of Two Fabrics with Microwave Properties
- 2014
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Ingår i: Studies in Health Technology and Informatics. - 0926-9630. ; 200, s. 95-100, s. 95-100
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Tidskriftsartikel (refereegranskat)abstract
- In this study, the transmission and reflection of two conductive fabrics are investigated in the frequency range from 2 to 18 GHz. One of the fabrics is a non-woven polypyrrole and the other consists of a polyethylene warp with steel threads in the weft. Reflection and transmission measurements are performed in order to characterize the electromagnetic properties of the materials. Reflection measurements are performed for two polarizations at normal, 0°, and 60° incident angles. Transmission measurements are also done for two polarization directions at normal incidence. The results show that the fabric with the steel filler reflects most of the incident radiation and has very low transmission with some polarization dependence. The polypyrrole non-woven fabric, on the other hand, has reflection and transmission properties that show that it is absorbing the incident radiation. Wearable on-body sensors that in addition are comfortable to wear can be integrated in the textile of clothes. These sensors can eg., be used to monitor health or analyze gait. The fabrics have the potential to be used in health applications when designing on-body sensors, e.g for movement analysis.
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