بهبود عملکرد تشخیص بیماری قلبی با ترکیب تبدیل کوچلیگرام و شبکه خودرمزنگار متغیر
محورهای موضوعی : یادگیری عمیق
محبوبه بحرینی
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رامین براتی
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عباس کمالی
3
1 - دانشکده مهندسی برق، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
2 - دانشکده مهندسی برق، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
3 - دانشکده مهندسی برق، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
کلید واژه: شبکه خود رمزنگار متغیر, ماشین بردار پشتیبان و K-نزدیک‌ترین همسایه, تبدیل کوچلیگرام, تشخیص بیماری قلبی,
چکیده مقاله :
تشخیص اولیه صداهای غیرطبیعی قلب تا حد زیادی می‌تواند از مرگ ناگهانی ناشی از بیماری‌های قلبی جلوگیری کند. یک روش کم هزینه و غیرهجومی برای تشخیص صداهای غیرطبیعی قلب، بکارگیری سیگنال PCG می‌باشد. دراین مقاله، بعد از قسمت‌بندی سیگنال صدای قلب، نمایش دو بعدی سیگنال توسط تبدیل کوچلیگرام حاصل می‌شود، سپس به کمک یادگیری عمیق و شبکه خودرمزنگار متغیر، ۴ ویژگی نهایی از هر سیگنال استخراج می‌شود. در نهایت از ماشین بردار پشتیبان و k-نزدیک‌ترین همسایه با اعتبارسنجی k-fold برای طبقه‌بندی سیگنال صدای قلب به یکی از دسته‌های از پیش تعیین‌شده‌ی کلاس صدای نرمال و غیرنرمال استفاده می‌شود. در این پژوهش از پایگاه داده‌ی فیزیونت که دارای 3482 صدای قلب از یک مجموعه استاندارد جهت آموزش و ارزیابی روش پیشنهادی استفاده می‌شود. بهترین نتایج بدست آمده روش پیشنهادی جهت طبقه‌بندی دو کلاسه صدای قلب به ترتیب به دقت، حساسیت و ویژه‌بودن ۹۹.۵۵، ۹۸.۷۵ و ۹۹.۷۰ می‌باشد که توانایی بالاتر روش پیشنهادی در مقایسه با سایر روش‌های موجود را اثبات می‌کند. از این سیستم تشخیص صداهای غیرنرمال می‌توان به صورت بسیار مفید در مراکزدرمانی بهداشت روستایی و بیمارستان‌های کوچک به منظور کمک به پزشکان بدون تخصص برای تشخیص مشکلات قلبی استفاده کرد.
Early detection of abnormal heart sounds can largely prevent sudden death caused by heart diseases. A low-cost and non-invasive method for detecting abnormal heart sounds is the use of PCG signal. In this article, after segmenting the heart sound signal, the two-dimensional representation of the signal is obtained by cochleogram transformation, then with the help of deep learning and variable autoencoder network, 4 final features are extracted from each signal. Finally, support vector machine and k-nearest neighbor with k-fold validation are used to classify the heart sound signal into one of the predetermined categories of normal and abnormal sound class. In this research, the Physionet database, which has 3482 heart sounds from a standard collection, is used to train and evaluate the proposed method. The best results of the proposed method for classifying the two classes of heart sounds are 99.55, 98.75 and 99.70 in terms of accuracy, sensitivity and specificity, which is the higher ability of the proposed method compared to other methods. This abnormal sound detection system can be used very usefully in rural health centers and small hospitals to help doctors without expertise to diagnose heart problems.
بانک فیلتر گاماتون اطلاعات فرکانس را از طریق تبدیل حلزون گوش استخراج می کند.
فضای پنهان از رمزگذارهای خودکار ویژگی های موثر و فشرده را برای طبقه بندی فراهم می کند.
رمزگذارهای خودکار در یادگیری بدون نظارت با فشرده سازی کارآمد ویژگی ها برتری می یابند.
رمزگذارهای خودکار که دربرابر نویز مقاوم هستند ،دقت را با استفاده از ویژگی‌های پنهان فشرده بهبود می‌بخشند.
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