Signal and Data Processing

fa تشخیص بیماری ADHD به‌وسیله پردازش غیرخطی پاسخ برانگیخته شنوایی ساقه مغز (ABR) ADHD recognition by processing nonlinear features of ABR based on a new innovative method for extracting Geometry features from phase space trajectory مقالات گروه علائم حیاتی ( مرتبط با مهندسی پزشکی) Paper پژوهشي Research <div style="text-align: justify;"><a name="_Hlk208903145">تشخیص </a>ADHD در سال‌های نخست تولد، برای طراحی برنامه درمانی بهتر برای بیماران ضروری است؛ بر اساس آخرین پژوهش‌ها، تشخیص این اختلال از روی ABR<a href="#_ftn1" name="_ftnref1" title="">[1]</a> روشی مؤثر است. باتوجه به اینکه در اصل ABR غیرخطی است، پردازش غیرخطی می‌تواند برای طبقه‌بندی گروه‌های نرمال و <a href="#_ftn2" name="_ftnref2" title="">[2]</a>ADHD از یکدیگر مؤثرتر باشد. در این پژوهش، سیگنال ABR دو گروه از کودکان شامل 37 کودک عادی و 31 کودک دارای اختلال ADHD ثبت شده‌است. در ابتدا سیگنال زمانی دوبعدی ABR با روش جای‌گذاری<a href="#_ftn3" name="_ftnref3" title="">[3]</a> به سیگنال سه‌بعدی تبدیل شد تا از روی آن فضای فاز سیگنال ترسیم شود. در فضای فاز به بررسی صحت این فرض که ممکن است سیگنال‌های نرمال و ADHD در زمان‌های متفاوت در نوع حضور و حرکت بین محوطه‌های مختلف فضای فاز تفاوت داشته باشند پرداخته شد؛ به این منظور ابتدا فضای فاز به المان‌های حجم<a href="#_ftn4" name="_ftnref4" title="">[4]</a> مساوی بسته به طول هر سیگنال تقسیم‌بندی شد و سپس با تعریف و استخراج ویژگی‌های هندسی جدید در فضای فاز، روش جدیدی برای طبقه‌بندی دو گروه ارائه شده‌است. این رویکرد شامل بررسی اشغال فضای وکسل‌ها به‌وسیله نقاط ترژکتوری داده‌ها در فضای فاز است؛ درنهایت کارایی این روش و ویژگی‌ها ارزیابی شد و بهترین نتیجه در ویژگی مینیمم محلی استخراج‌شده با روش جدید و با استفاده از طبقه‌بندی‌کننده‌های KNN و SVM مشاهده شد. بهترین دقت حدود 98.53 است که افزایش قابل‌توجهی در دقت را در مقایسه با رویکردهای پردازش خطی نشان می‌دهد. <div>  <hr align="left" size="1" width="33%" > <div id="ftn1" style="text-align: left;"><a href="#_ftnref1" name="_ftn1" title="">[1]</a> Auditory Brainstem Response</div> <div id="ftn2" style="text-align: left;"><a href="#_ftnref2" name="_ftn2" title="">[2]</a> Attention Deficit Hyperactivity Disorder</div> <div id="ftn3" style="text-align: left;"><a href="#_ftnref3" name="_ftn3" title="">[3]</a> Embedding</div> <div id="ftn4" style="text-align: left;"><a href="#_ftnref4" name="_ftn4" title="">[4]</a> Voxels</div> </div> </div> <div style="text-align: justify;">ADHD recognition in first years after birth is essential to consider a better treatment plan for patients and consequently helps children who suffer from this disorder to promote their communication abilities. According to the latest researches, ADHD recognition through ABR is a useful method for this purpose instead of surveys or other oral methods. Considering the basis of ABR which is nonlinear, so nonlinear processing methods can be more effective for classification normal and ADHD groups from each other than other linear classifications. In this paper ABR signal of two groups of children including 37 normal and 31 with ADHD disorder have been recorded in a rehabilitation center. To that end, in this study firstly by using embedding method, the two-dimensional ABR signal converted to three-dimensional signals to be ready for drawing and calculating the phase space arguments. In the phase space the accuracy of this hypothesis was checked that if ADHD and normal signals in different time would show different behavior in occupying and movement through the voxels in the phase space, for this purpose at first the phase space data should be produced, but it is important to have access to the raw data of phase space because if the methods that only print the phase space were been used the access of raw data would be impossible, Actually using these method for drawing phase space is in conflict with this study’s objectives, because it is not important to just draw the phase space of normal or ADHD signals but it is necessary to divide the phase space of individual signals to equal voxels and then determine that any sample of trajectory points is located in which of these voxels over the time. So, it is crucial to access the raw data of each axis of phase space of any signal to use them for diving that volume to voxels and then trace the attendance of trajectory in these voxels and make a new mapped signal that introduce the voxel number of each point of trajectory against time.  Therefore the source data of any axis of any signal has been produced by embedding method that it is discussed before, it means that we replace any x(t) by {x(t), x(t+lag), x(t+2 lag)} and they are three axis of new three dimensional space, so we can use these axis for diving this space to different voxels. The phase space should be divided to equal voxels depending on the length of each signal, then a novel method of classification has been developed by extracting new geometry features in the phase space. This approach includes dividing three-dimensional phase space to equal voxels and checking space voxel’s occupation by trajectory points in these voxels. As trajectory points has the same time sequence as time series of original signals so trajectories can be used in mentioned new method to check time occupation of trajectories in phase space. Then by changing each sample value by its voxel number of that sample, a mapping method was developed, this new mapped signal is the basis of later analysis and feature extractions in this study. Briefly, this mapped signal has been formed by taking some steps including producing the phase space signal from ABR, dividing the phase space to voxels and predicating voxel numbers to each sample. Then to find some distinctions between ADHD and normal group mapped signal, four groups of features have been developed in this study including temporal features, extremum features, histogram features, spatial occupational, Lyapunov features. Temporal features are some common features including min, max, mean, median, variance, skewness, kurtosis. But further in accuracy analysis of project it has been cleared that time or sample limited min and max (called extremum features) are more effective and can make a better distinction result. Histogram features focus is on the most repeated voxel numbers which means which voxels has been most occupied by trajectory during its journey through the phased space. Spatial occupational features deal with the span or extension of total trajectory path by introducing some features like the biggest or smallest voxel number which has been occupied by at least one point of trajectory, as it is can be interpreted from these two latter features, the subtraction of the biggest and smallest occupied voxel number can represent a vision of extension of trajectory. Another feature in this category is the total voxels quantity which are occupied by at least one of the points of the trajectory. The Lyapunov related feature also calculate the total number of non-zero voxels in a specific duration of time than can represent the chaotic grade of the signal, beside the difference of two adjacent time duration Lyapunov feature and show the gradient of chaotic level of the signal that shows the Lyapunov gradient. Finally, efficiency of this method and features have been evaluated and best result observed in local minimum feature extracted by the new method and using KNN and SVM classifiers, the best accuracy is about 98.53 that shows a significant increase in accuracy in comparison with linear processing approaches. In the other words, this distinction gained from the minimum feature shows that there are some places among the lower voxel numbers in trajectories which Normal group of signals desire to occupy more than ADHD group. According to anatomical data which is extractable from ABR signal in different points of time is reachable in the final mapped data also, because the time sequence of first samples is saved in the final data. It means that we can check the distinction areas in phase space and adopt them with time or their anatomical generating source. From the physiologic point of view, these distinction areas in the phase space are correspond to the activity of the primary auditory neurons in the cochlear nerve and lower levels of the brainstem. </div> اختلال بیش‌فعالی و نقصان توجه, پاسخ ساقه مغز به تحریک شنوایی, فضای فاز, ترژکتوری Attention-Deficit Hyperactivity Disorder, Auditory Brainstem Response, Phase Space, Trajectory 38 19 http://jsdp.rcisp.ac.ir/browse.php?a_code=A-10-2645-2&slc_lang=fa&sid=1 Ali Akbar Alidoust Ghadikolayi علی اکبر علی دوست قادیکلایی eng.alidoost@gmail.com 100319475328460014049 100319475328460014049 No PhD Candidate in Biomedical Engineering, Azad University, South Branch, Tehran, Iran دانشجوی دکتری مهندسی پزشکی دانشکده فناوری‌های نوین، واحد تهران جنوب، دانشگاه آزاد، تهران، ایران Ali Motie Nasrabadi علی مطیع نصرآبادی nasrabadi@shahed.ac.ir 100319475328460014050 100319475328460014050 Yes Professor, Department of Biomedical Engineering, Shahed University, Tehran, Iran استاد گروه مهندسی پزشکی، دانشکده فنی و مهندسی دانشگاه شاهد، تهران، ایران Saeed Malayeri سعید ملایری malayeris@gmail.com 100319475328460014051 100319475328460014051 No Part-time Lecturer, Iran University of Medical Sciences, Tehran, Iran مدرس پاره وقت، دانشگاه علوم پزشکی و خدمات بهداشتی درمانی ایران، دانشکده علوم توانبخشی، تهران، ایران