نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی پزشکی، گروه بیوالکتریک، دانشکده مهندسی پزشکی، دانشگاه صنعتی امیرکبیر، تهران

2 استاد، گروه بیوالکتریک، دانشکده مهندسی پزشکی، دانشگاه صنعتی امیرکبیر، تهران

3 دانشجوی کارشناسی ارشد مهندسی پزشکی گروه بیوالکتریک، دانشکده مهندسی پزشکی ، دانشگاه صنعتی امیرکبیر، تهران

10.22041/ijbme.2013.13090

چکیده

اپیزودهای افت فشار خون حاد یکی از اختلالات همودینامیکی رایج در طیف گسترد های از بیماران است. متاسفانه نرخ تلفات در بین بیماران مبتلا به این اختلال بسیار بالا می باشد. عوامل مختلفی در وقوع این اختلال فیزیولوژیک موثر هستند که هر کدام داری منشا متفاوت می باشند. پیش آگهی اپیزودهای افت فشار خون حاد کمک شایانی به درمان مناسب و کاهش تلفات این بیماران خواهد نمود. با پی شآگهی این اختلال فیزیولوژیکی، پزشکان قادر خواهند بود علت وقوع این اختلال را با استفاده از بررس یهای بالینی مختلف دریافته و درمان مناسبی بر اساس عامل وقوع آن، انتخاب کنند. در این پژوهش به منظور پیش آگهی اپیزودهای افت فشار خون حاد در بازه یک ساعت آینده، دو نوع ویژگی آماری از پارامترهای همودینامیکی و ویژگی های آشوبناک از سری های زمانی فیزیولوژیکی موجود در بازه دو ساعتی منتهی به به ابتدای بازه پیش بینی، استخراج گردید. سپس ویژگی های برگزیده با استفاده از الگوریتم ژنتیک، توسط ماشین بردار پشتیبان طبقه بندی شدند. دقت پیش آگهی برای ویژگ یهای آماری پارامترهای فیزیولوژیکی 5/87 درصد و برای ویژگی های آشوبی 85 درصد حاصل گردید. در ادامه به منظور استفاده از جنبههای مختلف اطلاعات موجود در دو دسته ویژگی و بهبود دقت پیش آگهی، فرآیند انتخاب ویژگی به صورت همزمان برای هر دو دسته ویژگی استخراج شده، اعمال گردید و بهترین ترکیب از میان هر دو دسته ویژگی انتخاب شد. دقت پیش آگهی برای دسته ویژگی تلفیقی بهینه، 95 درصد حاصل شد که در مقایسه با نتایج مطالعات پیشین بر روی مجموعه داده مشابه، بهبود قابل توجهی حاصل گردید.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Prognosis of Acute Hypotension Episodes Using Physiological and Chaotic Features

نویسندگان [English]

  • Amin Janghorbani 1
  • Mohammad Hasan Moradi 2
  • Abdollah Arasteh 3

1 Department of Biomedical Engineering, AmirKabir University of Technology

2 Department of Biomedical Engineering, AmirKabir University of Technology

3 Department of Electrical Engineering, Sharif University of Technology

چکیده [English]

Acute hypotension episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. Prognosis of acute hypotension episodes can help clinicians to diagnose the cause of this physiological disorder and select proper treatment based on this diagnosis. In this study two groups of features, physiological and chaotic features, were extracted from the physiological time series to be applied for prediction of AHEs in the future 1 hour time interval. The best set of the features from the extracted features were selected using Genetic Algorithm (GA) and were classified by SVM. The prediction accuracy for physiological features was 87.5% and for chaotic features was 85%. In order to improve prediction accuracy, physiological and chaotic features were employed simultaneously in feature selection and the best combination of these features was selected by GA and classified by SVM. The best prognosis accuracy, which was achieved in this study by classification of the selected features, was 95% that was better than other previously studies on the same database.

کلیدواژه‌ها [English]

  • Acute Hypotension Episodes
  • Prognosis
  • Physiological Features
  • Chaotic Features
  • feature selection
  • genetic algorithm

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