مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

استخراج ویژگی از سیگنال مکانیکی نبض به منظور تشخیص بیماری عروق کرونری

نویسندگان
محمد سجاد سکوت 1
برهان بیگ زاده 2
1 دانشگاه علم و صنعت ایران،تهران،ایران
2 استادیار، آزمایشگاه تحقیقاتی بیومکاترونیک و مهندسی شناختی، مهندسی مکانیک، دانشگاه علم و صنعت ایران
چکیده
تشخیص هوشمند بیماریها با دقت بالا، سرعت زیاد، هزینه پایین و به صورت غیرتهاجمی امروزه به یک ضرورت در روند درمان بیماریها تبدیل شده است. در این راستا، گرفتن سیگنال نبض، امری بسیار آسان و کم هزینه است که با توجه به در دسترس بودن آن و نیز آسان بودن فرآیند مربوطه، می تواند در تشخیص سریع و کم هزینه بیماریهای قلبی بسیار مفید باشد. چنانچه در این زمینه بتوان از روش مناسب پردازش سیگنال و هوشمندسازی آن نیز بهره جست بگونه ای که دقت آن با سایر روشهای هزینه بر و زمان بر برابری کند، می توان گفت که به دستاورد ارزشمندی رسیده ایم که هدف از این مطالعه نیز همین امر است. در ابتدا با استفاده از دستگاه تسک فورس مانیتور، داده های مربوط به سیگنال فشاری نبض در انگشت دست چپ برای دو گروه سالم و مبتلا به بیماری عروق کرونر دریافت شده است. سپس سیگنالها فیلتر شده و موارد نادرست از آن جدا شده است. آنگاه ویژگی های مربوط به حالت سالم و بیمار استخراج شده و در نهایت با انتخاب ویژگی های برتر، داده های افراد سالم و افراد بیمار با دقت بیش از 85 درصد با استفاده از الگوریتم ماشین‌های بردار پشتیبان از هم جدا می شوند.
کلیدواژه‌ها
سیگنال نبض
پالس فشاری خون
تشخیص بیماری
عروق کرونری
استخراج ویژگی

عنوان مقاله English

Mechanical pulse signal analysis in order to feature extraction to use in the diagnosis of CAD

نویسندگان English

mohammad sajjad sokout 1
Borhan Beigzadeh 2
1 Iran University of Science and Technology,tehran,iran
چکیده English

Nowadays, diagnosis of diseases with high precision, high speed, low-cost and non-invasive approaches has become a necessity. In this regard, taking pulse signal is very easy and inexpensive, which due to the availability and feasibility of the process, can be very useful in the rapid diagnosis heart disease. If we can use the appropriate signal processing and intelligent methods in such a way that its accuracy and total cost equal those of other corresponding methods, we can say that we have reached a valuable achievement; in the current study we pursue the same purpose. In the first step, pressure pulse signals of 45 Coronary Arterial Disease (CAD) patients and 45 healthy persons are acquired from the left fingers using Task Force Monitor (TFM). Then the signals are filtered by wavelet transform (db6) and the wrong items are discarded. Then, the features corresponding to the CAD and healthy states are extracted which based on Time Domain Analysis. Finally, by choosing the best features, the data of healthy people and patients (CAD) are classified with Support Vector Machine (SVM) classifier by the accuracy rate of more than 85%.

کلیدواژه‌ها English

Pulse Signal
Blood Pressure Pulse
Disease Diagnosis
Coronary Arteries
Feature Extraction
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مهندسی مکانیک مدرس
دوره 18، شماره 1
فروردین 1397
صفحه 62-68