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- Abtahi, Farhad, 1981-, et al.
(författare)
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An Affordable ECG and Respiration Monitoring System Based on Raspberry PI and ADAS1000 : First Step towards Homecare Applications
- 2015
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Ingår i: 16th Nordic-Baltic Conference on Biomedical Engineering. - Cham : Springer. - 9783319129662 ; , s. 5-8, s. 5-8
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Konferensbidrag (refereegranskat)abstract
- 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.
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- Abtahi, Farhad, 1981-, et al.
(författare)
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Development and preliminary evaluation of an Android based heart rate variability biofeedback system
- 2014
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Ingår i: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. - : IEEE. - 9781424479290 ; 2014, s. 3382-5
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Konferensbidrag (refereegranskat)abstract
- The reduced Heart Rate Variability (HRV) is believed to be associated with several diseases such as congestive heart failure, diabetes and chronic kidney diseases (CKD). In these cases, HRV biofeedback may be a potential intervention method to increase HRV which in turn is beneficial to these patients. In this work, a real-time Android biofeedback application based on a Bluetooth enabled ECG and thoracic electrical bioimpedance (respiration) measurement device has been developed. The system performance and usability have been evaluated in a brief study with eight healthy volunteers. The result demonstrates real-time performance of system and positive effects of biofeedback training session by increased HRV and reduced heart rate. Further development of the application and training protocol is ongoing to investigate duration of training session to find an optimum length and interval of biofeedback sessions to use in potential interventions.
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- Benouar, Sara, et al.
(författare)
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First Steps Toward Automated Classification of Impedance Cardiography dZ/dt Complex Subtypes
- 2021
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Ingår i: 8th European Medical and Biological Engineering Conference. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030646097 - 9783030646103 ; , s. 563-573
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Konferensbidrag (refereegranskat)abstract
- The detection of the characteristic points of the complex of the impedance cardiography (ICG) is a crucial step for the calculation of hemodynamical parameters such as left ventricular ejection time, stroke volume and cardiac output. Extracting the characteristic points from the dZ/dt ICG signal is usually affected by the variability of the ICG complex and assembling average is the method of choice to smooth out such variability. To avoid the use of assembling average that might filter out information relevant for the hemodynamic assessment requires extracting the characteristics points from the different subtypes of the ICG complex. Thus, as a first step to automatize the extraction parameters, the aim of this work is to detect automatically the kind of dZ/dt complex present in the ICG signal. To do so artificial neural networks have been designed with two different configurations for pattern matching (PRANN) and tested to identify the 6 different ICG complex subtypes. One of the configurations implements a 6-classes classifier and the other implemented the divide and conquer approach classifying in two stages. The data sets used in the training, validation and testing process of the PRANNs includes a matrix of 1 s windows of the ICG complexes from the 60 s long recordings of dZ/dt signal for each of the 4 healthy male volunteers. A total of 240 s. As a result, the divide and conquer approach improve the overall classification obtained with the one stage approach on +26% reaching and average classification ration of 82%.
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