دانلود مقاله مدل یادگیری ماشین برای نظارت بر افسردگی
خلاصه
معرفی
مواد
نتایج و بحث
نتیجه
در دسترس بودن داده ها
تضاد علاقه
قدردانی ها
منابع
Abstract
Introduction
Materials
Results and Discussion
Conclusion
Data Availability
Conflicts of Interest
Acknowledgments
References
چکیده
افسردگی اختلالی است که در صورت عدم درمان می تواند کیفیت زندگی را مختل کند. نوار مغزی در تشخیص افراد افسرده از افراد کنترل کننده افسردگی نویدبخشی را نشان داده است. بر محدودیت های روش های سنتی مبتنی بر پرسشنامه غلبه می کند. در این مطالعه، یک روش مبتنی بر یادگیری ماشین برای تشخیص افسردگی در میان بزرگسالان جوان با استفاده از داده های EEG ثبت شده توسط هدست بی سیم پیشنهاد شده است. به همین دلیل، داده های EEG با استفاده از هدست Emotiv Epoc+ ثبت شده است. در مجموع 32 بزرگسال جوان شرکت کردند و ابزار غربالگری PHQ9 برای شناسایی شرکت کنندگان افسرده استفاده شد. ویژگی‌هایی مانند چولگی، کشیدگی، واریانس، پارامترهای Hjorth، آنتروپی شانون و آنتروپی انرژی ورود از داده‌های 1 تا 5 ثانیه که در فرکانس‌های باند مختلف پخش می‌شوند برای طبقه‌بندی‌کننده‌های KNN و SVM با هسته‌های مختلف اعمال شدند. در فرکانس باند AB (8 تا 30 هرتز)، دقت 0.15 ± 98.43 با استخراج پارامترهای Hjorth، آنتروپی شانون و آنتروپی انرژی ورود از نمونه‌های 5 ثانیه با CV 5 برابر با استفاده از یک طبقه‌بندی کننده KNN به دست آمد. و با همان ویژگی ها و دقت کلی طبقه بندی کننده = 0.11 ± 98.10، NPV = 0.977، دقت = 0.984، حساسیت = 0.984، ویژگی = 0.976، و امتیاز F1 = 0.984 پس از تقسیم داده ها برای آموزش 0io/03 و rating داده ها به دست آمد. با رزومه 5 برابری از یافته‌ها، می‌توان نتیجه گرفت که داده‌های EEG از هدست Emotiv می‌تواند برای تشخیص افسردگی با روش پیشنهادی استفاده شود.
Abstract
Depression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods. In this study, a machine learning-based method for detecting depression among young adults using EEG data recorded by the wireless headset is proposed. For this reason, EEG data has been recorded using an Emotiv Epoc+ headset. A total of 32 young adults participated and the PHQ9 screening tool was used to identify depressed participants. Features such as skewness, kurtosis, variance, Hjorth parameters, Shannon entropy, and Log energy entropy from 1 to 5 sec data filtered at different band frequencies were applied to KNN and SVM classifiers with different kernels. At AB band (8–30 Hz) frequency, 98.43 ± 0.15% accuracy was achieved by extracting Hjorth parameters, Shannon entropy, and Log energy entropy from 5 sec samples with a 5-fold CV using a KNN classifier. And with the same features and classifier overall accuracy = 98.10 ± 0.11, NPV = 0.977, precision = 0.984, sensitivity = 0.984, specificity = 0.976, and F1 score = 0.984 was achieved after splitting the data to 70/30 ratio for training and testing with 5-fold CV. From the findings, it can be concluded that EEG data from an Emotiv headset can be used to detect depression with the proposed method.
Introduction
Depressive disorder is a highly prevalent mental illness. Sadness, loss of interest or enjoyment, feelings of guilt or low self-worth, interrupted sleep or food, fatigue, and difficulty concentrating are some characteristics of depression. It may affect a person’s capacity to operate in daily life or at work or school. According to the World Health Organization (WHO) back in 2015, almost 4.4% of the world’s population was suffering from depression [1]. Because of the COVID-19 pandemic, many people suffered from depression due to job loss, study hampering, losing close relatives, staying indoors, etc. A study showed 19.3% increase in depression symptoms among people after COVID-19 in the United States [2]. A study has shown the changes in obsession, depression, and quality of life in schizophrenia patients before and after COVID-19 [3]. When depression is severe it can lead to suicide. Every year around 800 thousand people die because of suicide [1]. In 2017, 13.2% of young adults (aged 18–25) in the U.S. suffered from depression which was 5.1% less in the year 2009 [4]. Of the deaths of young people, around 9.1% are due to suicide [5]. In most suicide cases, people had psychiatric disorders where depression is the most common disorder among others [6]. According to a recent study, insecure attachment styles are linked to greater problems such as depression, social anxiety, and suicidal thoughts [7]. So, depression is a major issue that should be diagnosed and treated at an early age to prevent suicide and for the betterment of the quality of life.
Conclusion
In this work, we have recorded EEG data of young adults (19 depressed and 13 Control) evaluated by the PHQ9 screening tool and proposed a machine learning approach to learn about the EEG properties for depression detection.
We conducted multiple experiments with the reported machine learning (SVM and KNN) classifiers with our recorded data. The first experiment we conducted was on segmentation to find the better sample length suitable for ML. From our experiments, we have identified that 5-second segments are suitable for our work. Then, we have identified a suitable frequency range from various experiments that improve performance using features that are related to depression detection. We have found out that Hjorth parameters along with Shannon entropy and long energy entropy provide better results among other reported features and the beta band (12–30 Hz) gives the highest accuracy of 97.21 ± 0.21% with 25 iterations and 5-fold CV using weighted KNN compared to the other sub-bands. By combining the sub-bands, we have also investigated some other frequency ranges. We have found out that by taking the range from alpha to beta 8–30 Hz (AB), we can improve ML performance and achieve 98.43 ± 0.15% accuracy with 25 iterations and a 5-fold CV with fine KNN classifier. Using AB (8–30 Hz), we can see a significant improvement of 1.22% accuracy and slandered deviation. To further investigate the reliability, we divided the dataset 70/30 for training and testing with 5-fold CV and 10 iterations. In this experiment, we have found out that the ML performance is better by choosing the AB (8–30 Hz) band with fine KNN classifier with an accuracy of 98.10 ± 0.11%, precision of 0.984 ± 0.003, NPV of 0.977 ± 0.002, sensitivity of 0.984 ± 0.002, specificity of 0.976 ± 0.005, and F1 score of 0.984 ± 0.001. Then, we analyzed the ML performance in different regions of the brain and concluded that using the whole brain for depression detection will give the highest accuracy. The proposed method can detect depression among young adults with minimum requirements compared to other related works.