Journal of Medical Signals & Sensors最新文献

{"title":"Implementation and Validation of a Novel Noninvasive blood Pressure Measurement Algorithm Based on Oscillometric Method with Obtaining International Hypertension Standards.","authors":"Sahar Karimian,&nbsp;Mohammadreza Yazdchi,&nbsp;Reza Hajian","doi":"10.4103/jmss.jmss_123_21","DOIUrl":"https://doi.org/10.4103/jmss.jmss_123_21","url":null,"abstract":"<p><strong>Background: </strong>One of the most prevalent methods in noninvasive blood pressure (BP) measurement with cuff is oscillometric, which has two different types of deflation, including linear and step deflation. With this approach, in addition to designing a novel algorithm by the step deflation method, a sample of its module was constructed and validated during clinical tests in different hospitals.</p><p><strong>Method: </strong>In this study, by controlling the valve, the pressure would be deflated through optimized steps. By real-time processing on the obtained signal from the pressure sensor, pulses in each step would be extracted. After that, in offline mode, mean arterial pressure is estimated based on curve fitting.</p><p><strong>Result: </strong>A BP simulator, various modules, and an auditory method were used to validate the algorithm and its results. During clinical tests, 80 people (men and women), 11 dialysis patients, and 69 non-dialysis (healthy or with other diseases) in the age range of 17-85 years participated.</p><p><strong>Conclusion: </strong>The obtained results compared with the BP simulator are in the standard range according to the international medical standards of the British Hypertension Society (BHS) and the US Association for the Advancement of Medical Instrumentation (AAMI), which are the global standard of comparison in this field.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 2","pages":"114-121"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b2/7a/JMSS-12-114.PMC9215828.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weight Pruning-UNet: Weight Pruning UNet with Depth-wise Separable Convolutions for Semantic Segmentation of Kidney Tumors.","authors":"Patike Kiran Rao,&nbsp;Subarna Chatterjee,&nbsp;Sreedhar Sharma","doi":"10.4103/jmss.jmss_108_21","DOIUrl":"https://doi.org/10.4103/jmss.jmss_108_21","url":null,"abstract":"<p><strong>Background: </strong>Accurate semantic segmentation of kidney tumors in computed tomography (CT) images is difficult because tumors feature varied forms and occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumor segmentation.</p><p><strong>Methods: </strong>We present weight pruning (WP)-UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating-point computational complexity.</p><p><strong>Results: </strong>We trained and evaluated the model with CT images from 210 patients. The findings implied the dominance of our method on the training Dice score (0.98) for the kidney tumor region. The proposed model only uses 1,297,441 parameters and 7.2e floating-point operations, three times lower than those for other network models.</p><p><strong>Conclusions: </strong>The results confirm that the proposed architecture is smaller than that of UNet, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumor imaging.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 2","pages":"108-113"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c3/ae/JMSS-12-108.PMC9215835.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Design of an Audit Test for ⁶⁰Co Brachytherapy Treatment Planning System.","authors":"Mohammad Javad Tahmasebi Birgani,&nbsp;Hadis Khorshidsavar,&nbsp;Ali Bagheri,&nbsp;Amir Danyaei,&nbsp;Neda Abdalvand","doi":"10.4103/jmss.JMSS_47_19","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_47_19","url":null,"abstract":"<p><strong>Background: </strong>Auditing the treatment planning system (TPS) software for a radiotherapy unit is of paramount importance in any radiation therapy department. A Plexiglas phantom was proposed to measure the ionization of ⁶⁰Co high dose rate (HDR) source and compare dose points in the planning system for auditing and verifying TPS.</p><p><strong>Methods: </strong>Auditing was performed using a Plexiglas phantom in an end-to-end test, and relative dose points were detected by a farmer-type ionization chamber and compared with the relative dose of similar points in TPS. The audit results were determined as pass optimal level (<3.3%), pass action level (between 3.3% and 5%), and out of tolerance (>5%).</p><p><strong>Results: </strong>The comparison of the collected data revealed that 80% of the measured values were ≤5% in the pass level, and 20% of the points were out of tolerance (between 5% and 6.99%).</p><p><strong>Conclusion: </strong>This study documented the appropriateness of the dosimetry audit test and this phantom design for the HDR brachytherapy TPS.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 2","pages":"133-137"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/0b/2b/JMSS-12-133.PMC9215833.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Classification and K-means Methods to Predict Breast Cancer Recurrence in Gene Expression Data.","authors":"Mohammadreza Sehhati,&nbsp;Mohammad Amin Tabatabaiefar,&nbsp;Ali Haji Gholami,&nbsp;Mohammad Sattari","doi":"10.4103/jmss.jmss_117_21","DOIUrl":"https://doi.org/10.4103/jmss.jmss_117_21","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer is a type of cancer that starts in the breast tissue and affects about 10% of women at different stages of their lives. In this study, we applied a new method to predict recurrence in biological networks made from gene expression data.</p><p><strong>Method: </strong>The method includes the steps such as data collection, clustering, determining differentiating genes, and classification. The eight techniques consist of random forest, support vector machine and neural network, randomforest + k-means, hidden markov model, joint mutual information, neural network + k-means and suportvector machine + k-menas were implemented on 12172 genes and 200 samples.</p><p><strong>Results: </strong>Thirty genes were considered as differentiating genes which used for the classification. The results showed that random forest + k-means get better performance than other techniques. The two techniques including neural network + k-means and random forest + k-means performed better than other techniques in identifying high risk cases.</p><p><strong>Conclusion: </strong>Thirty of 12,172 genes are considered for classification that the use of clustering has improved the classification techniques performance.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 2","pages":"122-126"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/cd/f3/JMSS-12-122.PMC9215834.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accidental Pathological Findings in Asymptomatic Maxillary Sinuses in Patients Referred for Head and Neck Cone-Beam Computed Tomography: A Cross-sectional Study Analysis.","authors":"Ajay Chandran,&nbsp;Manisha B Patil,&nbsp;S Nachiappan,&nbsp;Pratyaksha Singh Panwar,&nbsp;Anil Kumar Nagarajappa,&nbsp;Deepak R Kolte,&nbsp;J Suresh Babu,&nbsp;C Swarnalatha,&nbsp;Abhishek Singh Nayyar","doi":"10.4103/jmss.jmss_96_21","DOIUrl":"https://doi.org/10.4103/jmss.jmss_96_21","url":null,"abstract":"<p><strong>Background: </strong>The aim of the present study was to detect the prevalence of accidental pathological findings in asymptomatic maxillary sinuses in patients referred for head and neck cone-beam computed tomography (CBCT) examination for varied reasons.</p><p><strong>Methods: </strong>The present cross-sectional study included a detailed analysis of CBCT scans of 150 patients aged between 18 and 70 years reporting for varied dental complaints for detecting accidental pathological findings in maxillary sinuses while the patients did not have any complaint pertaining to sinuses.</p><p><strong>Results: </strong>The findings of the present study revealed 58% patients to have pathological findings in maxillary sinuses while they were asymptomatic for sinuses. Furthermore, the prevalence of mucosal thickening was found in 29.3% of the patients while 36.7% patients presented with polypoidal mucosal thickening.</p><p><strong>Conclusion: </strong>Higher prevalence of pathologies in asymptomatic maxillary sinuses found in the present study emphasized significance of a thorough examination of routine dental patients by dento-maxillofacial radiologists with necessary investigations to be advised in the form of higher imaging modalities like CBCT, if necessary.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 2","pages":"138-144"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/00/38/JMSS-12-138.PMC9215838.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction a CO2 Incubator for Cell Culture with Capability of Transmitting Microwave Radiation.","authors":"Mansour Tayebi-Khorami,&nbsp;Nahid Chegeni,&nbsp;Maryam Tahmasebi Birgani,&nbsp;Amir Danyaei,&nbsp;Reza Fardid,&nbsp;Jaber Zafari","doi":"10.4103/jmss.jmss_113_21","DOIUrl":"https://doi.org/10.4103/jmss.jmss_113_21","url":null,"abstract":"<p><strong>Background: </strong>The objective of this study was to design and construct a CO2 incubator with nonmetallic walls and to investigate the viability of the cells and microwave irradiance inside this incubator.</p><p><strong>Methods: </strong>Because the walls of conventional incubators are made of metal, this causes scattering, reflection, and absorption of electromagnetic waves. We decided to build a nonmetallic wall incubator to examine cells under microwave radiation. Incubator walls were made using polyvinyl chloride and Plexiglas and then temperature, CO2 pressure, and humidity sensors were placed in it. Atmel® ATmega1284, a low-power CMOS 8-bit microcontroller, collects and analyzes the sensor information, and if the values are less or more than the specified limits, the command to cut off or connect the electric current to the heater or CO2 solenoid valve is sent. Using a fan inside the incubator chamber, temperature and CO2 are uniforms. The temperature of the points where the cell culture plates are placed was measured, and the temperature difference was compared. Ovarian cancer cells (A2780) were cultured in the hand-made and commercial incubators at different times, and cell viability was compared by the MTT method. Microwave radiation in the incubator was also investigated using a spectrum analyzer. The survival of cells after microwave irradiation in the incubator was measured and compared with control cells.</p><p><strong>Results: </strong>The data showed that there was no significant difference in temperature of different points in hand-made incubator and also there was no significant difference between the viability of cells cultured in the hand-made and commercial incubators. The survival of irradiated cells in the incubator was reduced compared to control cells, but this reduction was not significant.</p><p><strong>Conclusion: </strong>This incubator has the ability to maintain cells and study the effects of electromagnetic radiations on the desired cells, which becomes possible by using this device.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"12 2","pages":"127-132"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d2/c3/JMSS-12-127.PMC9215836.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Development of a 3-Axis Accelerometer Biofeedback System for Real-Time Correction of Neck Posture for Long-Time Computer Users.","authors":"Hossein Shirvani,&nbsp;Mahmood Salesi,&nbsp;Mohammad Samadi,&nbsp;Alireza Shamsoddini","doi":"10.4103/jmss.JMSS_56_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_56_20","url":null,"abstract":"<p><strong>Background: </strong>Due to long-term use of computers and not maintaining the correct position and angle of the body while working with it, various skeletal and muscular problems and pain in the neck area occurs. This study aims to use a biofeedbck system to alert the computer users of an inappropriate angle of their necks, and as a result help them to establish a correct neck position.</p><p><strong>Method: </strong>The user's neck angle is measured using a three dimensional accelerometer and the signal is processed, digitalized, and sent to the computer. User friendly software is designed to process the received data and warn the users when their neck angle is inappropriate.</p><p><strong>Results: </strong>The results show that the application of the biofeedback system reduces the users' total time with inappropriate neck angle to <50%.</p><p><strong>Conclusion: </strong>results demonstrated that training with the biofeedback system has been sufficient to make the habit of maintaining the neck in the correct angle.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 4","pages":"269-273"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/92/79/JMSS-11-269.PMC8588882.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39656379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and Classification of COVID-19 by Lungs Computed Tomography Scan Image Processing using Intelligence Algorithm.","authors":"Naser Safdarian,&nbsp;Nader Jafarnia Dabanloo","doi":"10.4103/jmss.JMSS_55_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_55_20","url":null,"abstract":"<p><p>The latest World Health Organization statistics show that the number of people living with COVID-19 disease is now more than 42 million worldwide. Some diagnosis methods include detecting and observing clinical symptoms associated with the disease (fever, dry cough, shortness of breath, sore throat, and muscle fatigue). Some other methods, such as computed tomography (CT)-scan imaging from the lungs, are the more accurate diagnostic methods. In this study, we examine the types of abnormal COVID-19 can cause in the lungs of infected subjects and detect and classify this disease. In this paper, we used data from the lung's CT-scan images from the 79 participants. To do this, in this article, for processing CT-scan images of the lungs to diagnose and classification of the COVID-19 disease in men and women of different ages, for rapid diagnosis and high accuracy of this disease by the automatic classification algorithm is used. The final results showed that the proposed method could base on different categories (gender, age categories, and type of damage caused by COVID-19) with high detection and classification accuracy. The algorithm presented in this article has accurately identified the data of healthy subjects and patients with coronavirus.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 4","pages":"274-284"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1a/f6/JMSS-11-274.PMC8588881.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39656380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain Tumor Segmentation Using Graph Coloring Approach in Magnetic Resonance Images.","authors":"Rouholla Bagheri,&nbsp;Jalal Haghighat Monfared,&nbsp;Mohammad Reza Montazeriyoun","doi":"10.4103/jmss.JMSS_43_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_43_20","url":null,"abstract":"<p><p>It is important to have an accurate and reliable brain tumor segmentation for cancer diagnosis and treatment planning. There are few unsupervised approaches for brain tumor segmentation. In this paper, a new unsupervised approach based on graph coloring for brain tumor segmentation is introduced. In this study, a graph coloring approach is used for brain tumor segmentation. For this aim, each pixel of brain image assumed as a node of graph and difference between brightness of a couple of pixels considered as edge. This method was applied on T1-enhanced magnetic resonance images of low-grade and high-grade patients. Since a rigid graph was needed for graph coloring, edges must be divided into existing or nonexisting edge using a threshold. The value of this threshold has affected the accuracy of image segmentation, so the choice of the optimal threshold was important. The optimal value for this threshold was 0.42 of maximum value of difference of brightness between pixels that caused the 83.62% of correlation accuracy. The results showed that graph coloring approach can be a reliable unsupervised approach for brain tumor segmentation. This approach, as an unsupervised approach, shows better accuracy in comparison with neural networks and neuro-fuzzy networks. However, as a limitation, the accuracy of this approach is dependent on the threshold of edges.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 4","pages":"285-290"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/86/ec/JMSS-11-285.PMC8588878.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39656381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum: Evaluation of asymmetry in right and left eyes of normal individuals using extracted features from optical coherence tomography and fundus images.","authors":"","doi":"10.4103/2228-7477.328740","DOIUrl":"https://doi.org/10.4103/2228-7477.328740","url":null,"abstract":"<p><p>[This corrects the article on p. 12 in vol. 11, PMID: 34026586.].</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 4","pages":"291"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/85/90/JMSS-11-291.PMC8588880.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39656382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}