نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی پزشکی، گروه مهندسی پزشکی، دانشکده‌ی مهندسی برق، دانشگاه علم و صنعت ایران، تهران

2 استادیار، گروه مهندسی پزشکی، دانشکده‌ی مهندسی برق، دانشگاه علم و صنعت ایران، تهران

10.22041/ijbme.2018.89342.1364

چکیده

ثبت خارج‌سلولی فعالیت تک‌نورون‌های مغزی به عنوان روشی پرطرفدار در تحقیقات حوزه‌ی علوم اعصاب و مهندسی توان‌بخشی عصبی شناخته می‌شود. این ثبت‌ها شامل فعالیت تمام نورون‌های اطراف الکترود می‌شود که برای استفاده‌ی بهتر از آن‌ها باید با روش‌های طبقه‌بندی اسپایک به فعالیت تک‌نورون‌ها رسید. بر اساس ویژگی‌های ساختاری نورون، مانند درخت دندریتی آن و فاصله و جهت ثابتی که نسبت به الکترود ثبت دارد، می‌توان نتیجه گرفت که شکل اسپایک تولیدی آن منحصر به فرد و ثابت است. با این حال انجام طبقه‌بندی پتانسیل عمل‌ها در مقادیر نسبت سیگنال به نویز پایین، همواره با چالش‌هایی همراه است. طبقه‌بندی اسپایک‌های نورونی معمولا شامل سه بخش آشکارسازی، استخراج ویژگی و طبقه‌بندی می‌شود. در این مقاله، روشی بر مبنای بهینه‌سازی ضرایب ویولت پیوسته در مرحله‌ی استخراج ویژگی­ها ارائه شده که در مقادیر نسبت سیگنال به نویز پایین نیز کارایی خوبی دارد. در روش پیشنهادی، بعد از محاسبه‌ی ضرایب ویولت پارامتری‌شده، با استفاده از معیارهای فاصله‌ی اقلیدسی و سطح زیر منحنی مشخصه‌ی عمل‌گر گیرنده در طبقه‌بندی دو گروه، بهترین پارامترها برای افزایش تفکیک‌پذیری ویژگی‌ها انتخاب می‌شوند، به طوری که ابتدا مقیاس مناسب با معیار فاصله‌ی اقلیدسی پیدا شده و در نهایت انتقال زمانی با معیار دوم انتخاب می‌شود. در این پژوهش برای خوشه‌بندی از الگوریتم ساده و در عین حال کارامد k-means استفاده شده است. برای بررسی و ارزیابی روش پیشنهادی از سه مجموعه داده‌ی شبیه‌سازی‌شده استفاده گردید که در 9 حالت مختلف نسبت سیگنال به نویز و با مدل‌سازی نویز زمینه از نویز حقیقی ثبت‌شده تهیه شده بودند. نتایج به دست آمده از مرتب‌سازی داده‌های شبیه‌سازی‌شده نشان داد که بهینه‌سازی پارامترهای تبدیل ویولت پیوسته با روش پیشنهادی می‌تواند در ارتقای کارایی طبقه‌بندی اسپایک‌ها نسبت به روش آنالیز اجزای اصلی، موثر واقع شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Optimization of Continuous Wavelet Coefficients for Neural Spike Sorting

نویسندگان [English]

  • Amir Soleymankhani 1
  • Vahid Shalchyan 2

1 MS.c Student, Electrical Engineering Department, Iran University of Science and Technology, Tehran, Iran

2 Assistant Professor, Electrical Engineering Department, Iran University of Science and Technology, Tehran, Iran

چکیده [English]

The extracellular recording from the brain's single neurons is known as a popular method in neuroscience and neuro-rehabilitation engineering. These recordings include the activity of all neurons around the electrode, for better use of which, spike sorting methods should be utilized to obtain the activity of single neurons. Based on the structural properties of the neuron, such as its dendritic tree, and the distance and direction of it relative to the electrode, it can be claimed that the form of its spike waveform is unique and constant. However, spike sorting under low signal-to-noise ratio (SNR) conditions is always accompanied with challenges. A spike sorting algorithm usually consists of three sections including the spike detection, feature extraction, and classification. In this paper, a method based on optimization of continuous wavelet coefficients is presented which is effective in low SNR values. In the proposed method, after the calculation of the parameterized wavelet coefficients, using the Euclidean distance and the area under the receiver operator characteristic curve, the best parameters were chosen to increase the separation of the features, so that a suitable scale was first found with the Euclidean distance criterion and then the translation parameter was obtained with the second criterion. In this research k-means algorithm was used for the clustering as a simple but efficient method. For evaluation, three simulated data sets were made in 9 different SNRs with a modeled background noise. The obtained results from simulated data showed that the optimization of parameters in continuous wavelet transform using the proposed algorithm could effectively improve the spike sorting performance compared to principal component analysis method.

کلیدواژه‌ها [English]

  • Spike Sorting
  • Action potential
  • Continuous Wavelet Transform
  • Principal Component Analysis
  • Optimization

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