Automatic Detection of Schizophrenia by Applying Deep Learning over Spectrogram Images of EEG Signals

Aslan, Zülfikar; Akın, Mehmet

doi:10.18280/ts.370209

Cited by 76 publications

(38 citation statements)

References 28 publications

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“…Several methods for the conversion of 1D signal data into a 2D image have been considered. For example, the STFT, wavelet-based spectrogram method [33], and brain coherence network-based method [34,35] have been proposed. However, to implement an effective 2D CNN image, this study used brain asymmetry, which has been identified as an important biomarker of depression.…”

Section: Discussionmentioning

confidence: 99%

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression

Kang

Kwon

Park

et al. 2020

Sensors

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To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG’s asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG’s asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients.

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Section: Discussionmentioning

confidence: 99%

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression

Kang

Kwon

Park

et al. 2020

Sensors

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“…Finally, classification was performed using linear discriminant analysis (LDA), SVM, KNN, and ANN. In the DL‐based methods, there is no need of experts in the specific feature domain as both the feature extraction and classification are done by DL methods and produce a better result than ML, but it works as a black box [15 ].…”

Section: Introductionmentioning

confidence: 99%

“…This proposed method is novel in the way that it shows time–frequency ( T–F ) based spectrogram image of EEG signal can be used to diagnose ASD. Few studies have used T–F images for neurological disorder classification like epilepsy [10 ], schizophrenia [15 ], epileptic seizures [16 ] and sleep stage classification [17 ], but never used for ASD classification. In this Letter, the proposed method converts EEG signal into T–F based spectrogram images and then those images are used for classification using our previously proposed texture classifier named ternary CENTRIST (tCENTRIST) with SVM [18 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of autism spectrum disorder from EEG using a time–frequency spectrogram image‐based approach

2020

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Autism is a type of neurodevelopment disorder in which individuals often have difficulties in social relationship, communication, expressing and controlling emotions and poor eye contact, among other symptoms. Currently, electroencephalography (EEG) is the most popular tool to investigate the presence of autism biomarkers. Generally, EEG recordings generate huge volume data with dynamic behaviour. In current practice, the massive EEG data are visually analysed by specialist clinicians to identify autism, which is time consuming, costly, subject to human error, and reduces decision‐making reliability. Hence this Letter aims to develop an efficient autism diagnostic system that can automatically identify autism based on time–frequency spectrogram images from EEG signals. Firstly, the raw EEG data is pre‐processed using several techniques, such as re‐referencing, filtering and normalisation. After that, the pre‐processed EEG signals are converted to two‐dimensional images using a short‐time Fourier transform. Then, textural features are extracted, and significant features are selected using principal component analysis, and feed to support vector machine classifier for classification. The proposed system achieved an average of 95.25% accuracy in ten‐fold cross‐validation evaluation. The developed system's simplicity and performance indicates usefulness as a decision support tool for healthcare professionals in autism diagnosis.

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“…The main benefit of the DL based classification approaches is that it does not require any specific feature domain experts for feature extraction from the raw data. The DL methods perform both feature extraction and classification automatically and generates better results than ML based classification processes but it works as a black box to the user [ 28 ]. In the same study [ 21 ], the authors have also used CNN model on power spectrum of electrodes to evaluate the long-term dependencies between ASD and EEG data.…”

Section: Introductionmentioning

confidence: 99%

“…[ 29 ]. Previously, few studies have used time-frequency (T-F) based images for the classification of neurological disorders such as epilepsy [ 10 ], epileptic seizures [ 30 ], clinical brain death diagnosis [ 29 ], schizophrenia [ 28 ] and classification of sleep stage [ 31 ], but never for the classification of ASD. In our recent work [ 32 ], we have used this T-F based spectrogram image of EEG signal for ASD classification using ternary CENTRIST (tCENTRIST) [ 33 ] with SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG

et al. 2021

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Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system.

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Automatic Detection of Schizophrenia by Applying Deep Learning over Spectrogram Images of EEG Signals

Cited by 76 publications

References 28 publications

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression

Diagnosis of autism spectrum disorder from EEG using a time–frequency spectrogram image‐based approach

A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG

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