معرفی نمایه جدید به منظور تشخیص خودکار شدت پیشرفت بیماری آصم با استفاده از سیگنالهای کپنوگرام

محورهای موضوعی : پردازش سیگنالهای پزشکی

محسن کاظمی ¹ (استادیار - دانشکده مهندسی برق، واحد خمینی شهر، دانشگاه آزاد اسلامی، خمینی شهر، اصفهان، ایران)
آیک هوتو ² (دانشیار - رییس بخش اورژانس بیمارستان پولای پننگ، شهر پننگ، مالزی)

تاریخ دریافت : 1394/03/29 تاریخ پذیرش : 1395/02/18 تاریخ انتشار : 1395/04/11

کلید واژه: آصم, مدل اتورگرسیو, کپنوگرام, ضرائب پیش‌بینی خطی, شبکه عصبی .RBF,

چکیده مقاله :

در این مقاله یک نمایه جدید به منظور تشخیص خودکار شدت بیماری آصم با استفاده از پردازش سیگنالهای کپنوگرام ارائه شده است. تحقیقات انجام گرفته در گذشته نشان دهنده ارتباط مهمی بین کپنوگرام و بیماری آصم بوده است .هرچند، اغلب آن تحقیقات از روشهای پردازشی حوزه زمان اسفاده کرده بوده و بر این فرضیه استوار بودند که کپنوگرام یک سیگنال ایستان است. در این تحقیق با استفاده از ضرائب پیش بینی خطی (LPC) و روش مدلینگ اتورگرسیو (AR Modelling-Burg Method) سیگنالهای کپنوگرام مورد پردازش قرار گرفته‌اند. با استفاده از نتایج حاصل از این پردازش، تعداد شش ویژگی استخراج شده اند که با استفاده از روشهای آماری مانند ROC, توانایی‌های آنها برای تمایز بیماران آصمی از افراد سالم و همینطور قابلیت آنها برای تشخیص شدت بیماری آصم اثبات شده است. در ادامه با استفاده از به کار بردن این بردار ویژگی در یک شبکه عصبی GRBF, نمایه اشاره شده که همان خروچی این شبکه است، استخراج شده است. این نمایه یک عدد طبیعی بین 1 تا 10 می‌باشد (1 برای افراد سالم و10 نشان دهنده بیمار با شدت آصم ببسیار بالا) که متوسط تشخیص صحیح 90/15 % و خطای 9/85% را داراست. الگوریتم ارائه شده در این پژوهش بر آن دارد که روشی سریع و مقرون به صرفه برای کمک به متخصصان ارائه دهد، چراکه قادر است شدت بیماری آصم را به صورت سریع و خودکار رصد کند.

چکیده انگلیسی:

In this paper, a new prognostic index to detect the severity of asthma by processing capnogram signals is presented. Previous studies have shown significant correlation between the capnogram and asthmatic patient. However, most of them used conventional time-domain methods and based on assumption that the capnogram is a stationary signal. In this study, by using linear predictive coding (LPC) coefficients and autoregressive (AR) modelling (Burg method), the capnogram signals are processed. Then, a number of six features including α1, and α4 from LPC and power spectral density (PSD) parameters through AR modelling are extracted. After that, by means of receiver operating characteristic (ROC) curve, the effectiveness of the extracted features to differentiate between asthmatic and nonasthmatic conditions is justified. Finally, selected features are used in a Gaussian radial basis function (GRBF) network. The output of this network is an integer prognostic index ranging from 1 to 10 (depends on the severity of asthma) with an average good detection rate of 90.15% and an error rate of 9.85%. In the other word, based on the results, sensitivity and specificity of this algorithm are 93.54% and 98.29%, respectively. This developed algorithm is purposed to provide a fast and low-cost diagnostic system to help healthcare professional involved in respiratory care as it would be possible to monitor severity of asthma automatically and instantaneously.

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