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Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis

Khosla Ashima, Khandnor Padmavati, Chand Trilok

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

108--142

EN

Depression is one of the significant contributors to the global burden disease, affecting nearly 264 million people worldwide along with the increasing rate of suicidal deaths. Electroencephalogram (EEG), a non-invasive functional neuroimaging tool has been widely used to study the significant biomarkers for the diagnosis of the disorder. Computational Psychiatry is a novel avenue of research that has shown a tremendous success in the automated diagnosis of depression. The present comprehensive review concentrate on two approaches widely adopted for an EEG based automated diagnosis of depression: Deep Learning (DL) approach and the traditional approach based upon Machine Learning (ML). In this review, we focus on performing the comparative analysis of a variety of signal processing and classification methods adopted in the existing literature for these approaches. We have discussed a variety of EEG based objective biomarkers and the data acquisition systems adopted for the diagnosis of depression. Few EEG studies focusing on multimodal fusion of data have also been explained. Additionally, the research based upon the analysis and prediction of treatment outcome response for depression using EEG signals and machine learning techniques has been briefly discussed to aware the researchers about this emerging field. Finally, the future opportunities and a valuable discussion on major issues related to this field have been summarized that will help the researchers in developing more reliable and computationally intelligent systems in the field of psychiatry.

2

Influence of ECG testing errors on Automatic ECG interpretation

Petrović I., Eljuga K., Puharić Z.

Przegląd Elektrotechniczny

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2016

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R. 92, nr 9

216--219

EN

The ECG testing is very common in health diagnose of a patient. Also, in present it is common to apply as many automated technology solutions as possible. Generally, this should insure better quality of gained diagnostic results. On the other hand, complex solutions invoke human error in procedure, which ultimately can provide inaccurate results. The influence of such errors on example of ECG testing is analyzed and compared to normal ECG test in this research, where six scenarios are deliberately forced to provide fault diagnose.

PL

Do analizy sygnałów EKG stosuje się obecnie wiele metod automatyzacji. W artykule analizuje się możliwe błędy w interpretacji sygnałów EKG.

3

Wavelet based classification of skin lesion images

Surówka G., Merkwirth C., Żabińska-Płazak E., Graca A.

Bio-Algorithms and Med-Systems

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2006

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Vol. 2, no. 4

43--49

EN

Visual examination of the early stages of the melanocytic skin cancer (melanoma) may often lead to a false diagnosis. Only the resection and then histologic examination of the lesion can fully detect malignant transformations of the skin. This is the reason why development of non-invasive methods for dermatological diagnosis, like dermatoscopy, is of key importance. We build a MLP-based binary classifier for discriminating melanoma from dysplastic nevus utilizing textural information contained in the skin lesion images taken in dermatoscopic examinations. Our analysis is based on the multiresolution wavelet-based decomposition of the images. Significant features of both classes are found by means of the Ridge regression models. Discriminating melanoma from dysplastic nevus with this method yields a sensitivity and specificity of 89.5% and 90%, respectively.

PL

Wizualna ocena wczesnych stanów procesu nowotworzenia skóry może prowadzić do błędnej diagnozy. Jedynie resekcja oraz histologiczna ocena może ocenić obecność procesu nowotworzenia. Stąd potrzeba nieinwazyjnej oceny w dermatologii jest potrzebą chwili. Zbudowaliśmy bazujący na MLP binarny klasyfikator dla dyskryminacji melanoma w oparciu o obrazy uzyskane dermatoskopowo. Metoda bazuje na dekompozycji obrazu. Model regresji Ridge'go został zaadaptowany dla klasyfikacji obrazu co dało specyficzność oceny rzędu 89.5% i 90%.