Journal of Medical Signals & Sensors最新文献

In this article, a smart visual acuity measurement (VAM) system is designed and implemented. Hardware of the proposed VAM system consists of two parts: a wireless remote controller, and a high-resolution LCD controlled through a Raspberry-Pi mini-computer. In the remote controller, a 3.5" graphical LCD with a touch screen is used as a human-machine interface. When a point is pressed on the touch screen, the unique identifier (ID) code of that point as well as its page number is transmitted to the Raspberry-Pi. In the Raspberry-Pi, data are received and processed by a smart application coded in visual studio software. Then, the commanded tasks are executed by the Raspberry-Pi's operating system. Numerous charts, characters, and pictures are stored in the proposed VAM system to provide various VAM options while the size of the optotypes is adjusted automatically based on the distance of the patient from the LCD. The performance of the proposed VAM system is examined practically under the supervision of an expert optometrist where the results indicate that visual acuity, astigmatism, and color blindness of patients can be examined precisely through the proposed VAM system in an easier and more comfortable manner.

Barrett's mucosa is one of the most important diseases in upper gastrointestinal system that caused by gastro-esophagus reflux. If left untreated, the disease will cause distal esophagus and gastric cardia adenocarcinoma. The malignancy risk is very high in short segment Barrett's mucosa. Therefore, lesion area segmentation can improve specialist decision for treatment. In this paper, we proposed a combined fuzzy method with active models for Barrett's mucosa segmentation. In this study, we applied three methods for special area segmentation and determination. For whole disease area segmentation, we applied the hybrid fuzzy based level set method (LSM). Morphological algorithms were used for gastroesophageal junction determination, and we discriminated Barrett's mucosa from break by applying Chan-Vase method. Fuzzy c-mean and LSMs fail to segment this type of medical image due to weak boundaries. In contrast, the full automatic hybrid method with correlation approach that has used in this paper segmented the metaplasia area in the endoscopy image with desirable accuracy. The presented approach omits the manually desired cluster selection step that needed the operator manipulation. Obtained results convinced us that this approach is suitable for esophagus metaplasia segmentation.

Measurement of the stroke volume (SV) and its changes over time can be very helpful for diagnosis of dysfunctions in the blood circulatory system and monitoring their treatments. Impedance cardiography (ICG) is a simple method of measuring the SV based on changes in the instantaneous mean impedance of the thorax. This method has received much attention in the last two decades because it is noninvasive, easy to be used, and applicable for continuous monitoring of SV as well as other hemodynamic parameters. The aim of this study was to develop a low-cost portable ICG system with high accuracy for monitoring SV. The proposed wireless system uses a tetrapolar configuration to measure the impedance of the thorax at 50 kHz. The system consists of carefully designed precise voltage-controlled current source, biopotential recorder, and demodulator. The measured impedance was analyzed on a computer to determine SV. After evaluating the system's electronic performance, its accuracy was assessed by comparing its measurements with the values obtained from Doppler echocardiography (DE) on 5 participants. The implemented ICG system can noninvasively provide a continuous measure of SV. The signal to noise ratio of the system was measured above 50 dB. The experiments revealed that a strong correlation (r = 0.89) exists between the measurements by the developed system and DE (P < 0.05). ICG as the sixth vital sign can be measured simply and reliably by the developed system, but more detailed validation studies should be conducted to evaluate the system performance. There is a good promise to upgrade the system to a commercial version domestically for clinical use in the future.

This study investigates the detection of the drowsiness state (DS) for future application such as in the reduction of the road traffic accidents. The electroencephalography, electrooculography, driving quality, and Karolinska sleepiness scale data of 7 males during approximately 20 h of sleep deprivation were recorded. To reduce the eye blink artifact, an automatic mechanism based on the independent component analysis method and Higuchi's fractal dimension has been applied. After recordings, for selecting the best subset of features, a new combined method, called class separability feature selection-sequential feature selection, has been developed. This method reduces the time of calculations from 6807 to 2096 s (by 69.21%) while the classification accuracy remains relatively unchanged. For diagnosis of the DS and classification of the state, a new approach based on a self-organized map network is used. First, using the data obtained from two classes of awareness state (AS) and DS, the network achieved an accuracy of 76.51 ± 3.43%. Using data from three classes of AS, AS/DS (passing from awareness to drowsiness), and DS to the network, an accuracy of 62.70 ± 3.65% was achieved. It is suggested that the DS during driving is detectable with an unsupervised network.

Acoustic analysis of sounds produced during speech provides significant information about the physiology of larynx and vocal tract. The analysis of voice power spectrum is a fundamental sensitive method of acoustic assessment that provides valuable information about the voice source and characteristics of vocal tract resonance cavities. The changes in long-term average spectrum (LTAS) spectral tilt and harmony to noise ratio (HNR) were analyzed to assess the voice quality before and after functional rhinoplasty in patients with internal nasal valve collapse. Before and 3 months after functional rhinoplasty, 12 participants were evaluated and HNR and LTAS spectral tilt in /a/ and /i/ vowels were estimated. It was seen that an increase in HNR and a decrease in LTAS spectral tilt existed after surgery. Mean LTAS spectral tilt in vowel /a/ decreased from 2.37 ± 1.04 to 2.28 ± 1.17 (P = 0.388), and it was decreased from 4.16 ± 1.65 to 2.73 ± 0.69 in vowel /i/ (P = 0.008). Mean HNR in the vowel /a/ increased from 20.71 ± 3.93 to 25.06 ± 2.67 (P = 0.002), and it was increased from 21.28 ± 4.11 to 25.26 ± 3.94 in vowel /i/ (P = 0.002). Modification of the vocal tract caused the vocal cords to close sufficiently, and this showed that although rhinoplasty did not affect the larynx directly, it changes the structure of the vocal tract and consequently the resonance of voice production. The aim of this study was to investigate the changes in voice parameters after functional rhinoplasty in patients with internal nasal valve collapse by computerized analysis of acoustic characteristics.