Automatic feature learning model combining functional connectivity network and graph regularization for depression detection

Yang, Lijun; Wei, Xiaoyong; Liu, Fengrui; Zhu, Xinhua; Zhou, Feng

doi:10.1016/j.bspc.2022.104520

Cited by 17 publications

(7 citation statements)

References 38 publications

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“…When in a cardiorhythmogram, non-steadystate events (Figure 6) occurred, such as the cardiorhythmogram having to be excluded from the analysis by the linear methods [16,18,19]. It is important to know that, especially in cardiorhythmograms, where non-steady-state events in rest state occur, important information is hidden regarding the detection of pathological signs in healthy individuals [4,10,15,19,[22][23][24]. Such non-steady-state events in a rest-state cardiorhythmogram are described in the research work of N. Wessel [27].…”

Section: Discussionmentioning

confidence: 99%

“…Usually, a structural heart disease is manifested when all of the compensatory mechanisms of regulation of the heart at the medullary and the central levels are broken down [7,8]. The person then enters a dangerous state, where the heart regulation migrates in a compensatory sense from the medullary to the predominant central level of regulation [4,9,10]. It is obvious that such a migration of heart regulation should be recognized as early as possible in order to prevent further progression into a structural heart disease or arrhythmia [3,8,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…One of the well-known methods nowadays for assessing the state of the neural regulatory systems of the heart is the heart rate variability (HRV) [6,10,[13][14][15]. Applying this method, when judging the reliability of the transition from the medullary level of regulation to the central one, shows a lot of limitations [8,13,16,17].…”

Section: Introductionmentioning

confidence: 99%

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Pathological Heart Rate Regulation in Apparently Healthy Individuals

Sidorenko

Sidorenko²,

Gapelyuk

et al. 2023

Entropy

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Cardiovascular diseases are the leading cause of morbidity and mortality in adults worldwide. There is one common pathophysiological aspect present in all cardiovascular diseases—dysfunctional heart rhythm regulation. Taking this aspect into consideration for cardiovascular risk predictions opens important research perspectives, allowing for the development of preventive treatment techniques. The aim of this study was to find out whether certain pathologically appearing signs in the heart rate variability (HRV) of an apparently healthy person, even with high HRV, can be defined as biomarkers for a disturbed cardiac regulation and whether this can be treated preventively by a drug-free method. This multi-phase study included 218 healthy subjects of either sex, who consecutively visited the physician at Gesundheit clinic because of arterial hypertension, depression, headache, psycho-emotional stress, extreme weakness, disturbed night sleep, heart palpitations, or chest pain. In study phase A, baseline measurement to identify individuals with cardiovascular risks was done. Therefore, standard HRV, as well as the new cardiorhythmogram (CRG) method, were applied to all subjects. The new CRG analysis used here is based on the recently introduced LF drops and HF counter-regulation. Regarding the mechanisms of why these appear in a steady-state cardiorhythmmogram, they represent non-linear event-based dynamical HRV biomarkers. The next phase of the study, phase B, tested whether the pathologically appearing signs identified via CRG in phase A could be clinically influenced by drug-free treatment. In order to validate the new CRG method, it was supported by non-linear HRV analysis in both phase A and in phase B. Out of 218 subjects, the pathologically appearing signs could be detected in 130 cases (60%), p < 0.01, by the new CRG method, and by the standard HRV analysis in 40 cases (18%), p < 0.05. Thus, the CRG method was able to detect 42% more cases with pathologically appearing cardiac regulation. In addition, the comparative CRG analysis before and after treatment showed that the pathologically appearing signs could be clinically influenced without the use of medication. After treatment, the risk group decreased eight-fold—from 130 people to 16 (p < 0.01). Therefore, progression of the detected pathological signs to structural cardiac pathology or arrhythmia could be prevented in most of the cases. However, in the remaining risk group of 16 apparently healthy subjects, 8 people died due to all-cause mortality. In contrast, no other subject in this study has died so far. The non-linear parameter which is able to quantify the changes in CRGs before versus after treatment is FWRENYI4 (symbolic dynamic feature); it decreased from 2.85 to 2.53 (p < 0.001). In summary, signs of pathological cardiac regulation can be identified by the CRG analysis of apparently healthy subjects in the early stages of development of cardiac pathology. Thus, our method offers a sensitive biomarker for cardiovascular risks. The latter can be influenced by non-drug treatments (acupuncture) to stop the progression into structural cardiac pathologies or arrhythmias in most but not all of the patients. Therefore, this could be a real and easy-to-use supplemental method, contributing to primary prevention in cardiology.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pathological Heart Rate Regulation in Apparently Healthy Individuals

Sidorenko

Sidorenko²,

Gapelyuk

et al. 2023

Entropy

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“…Given that the number of subjects obtaining EEG data collection in reality is relatively small, this is not conducive to learner training [38] due to the high temporal resolution nature of the signals, i.e., the large amount of information that can be provided in a short period of time. We can use effective data enhancement methods to increase the diversity and quantity of training data.…”

Section: Preprocessingmentioning

confidence: 99%

Efficient Feature Learning Model of Motor Imagery EEG Signals with L1-Norm and Weighted Fusion

Kong,

Wu,

Chen

et al. 2024

Biosensors

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Brain–computer interface (BCI) for motor imagery is an advanced technology used in the field of medical rehabilitation. However, due to the poor accuracy of electroencephalogram feature classification, BCI systems often misrecognize user commands. Although many state-of-the-art feature selection methods aim to enhance classification accuracy, they usually overlook the interrelationships between individual features, indirectly impacting the accuracy of feature classification. To overcome this issue, we propose an adaptive feature learning model that employs a Riemannian geometric approach to generate a feature matrix from electroencephalogram signals, serving as the model’s input. By integrating the enhanced adaptive L1 penalty and weighted fusion penalty into the sparse learning model, we select the most informative features from the matrix. Specifically, we measure the importance of features using mutual information and introduce an adaptive weight construction strategy to penalize regression coefficients corresponding to each variable adaptively. Moreover, the weighted fusion penalty balances weight differences among correlated variables, reducing the model’s overreliance on specific variables and enhancing accuracy. The performance of the proposed method was validated on BCI Competition IV datasets IIa and IIb using the support vector machine. Experimental results demonstrate the effectiveness and superiority of the proposed model compared to the existing models.

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“…The literature contains a limited number of studies on the detection of alcohol and cigarette objects in videos and most of them are related to sensors that detect drunk drivers (Pain et al, 2023; Shen et al, 2023) and smoking activities (Bahhar et al, 2023; Sathishkumar et al, 2023). Several applications, such as Image Moderation API Demo (Sightengine, 2021) and Realtime Image & Video Moderation API (TuPuTech, 2021) were used for alcohol, nudity and gun detection (Zhenhua et al, 2022) in images and videos.…”

Section: Related Workmentioning

confidence: 99%

DeepCens: A deep learning‐based system for real‐time image and video censorship

Yuksel,

Tan

2023

Expert Systems

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The popularity of social networks and video‐on‐demand platforms has increased the importance of image and video censorship. These platforms contain various content such as violence, explicit content, drug use and smoking that may be offensive or harmful to certain viewers. As a result, censorship is employed to filter or remove content that is unsuitable for particular audiences such as children and teenagers. However, policies for censoring harmful content in digital environments are either limited or nonexistent. This underscores the need for automated systems that can detect and censor harmful content in real time. To address these challenges, we developed the first of our knowledge systems using deep learning techniques to censor harmful content. We propose two novel, YOLO‐based real‐time censorship algorithms. Our approaches employ a pipeline‐based architecture that parallelizes the operations with subprocesses. In our experiments, the proposed algorithms performed faster and with higher accuracies compared to traditional approaches. Specifically, its content‐based accuracies were 98% for explicit content, 97% for alcohol, 98% for cigarettes and 97% for violence. Our research highlights the importance of developing effective and efficient solutions for censoring harmful content on digital media platforms. Our deep learning‐based system represents a promising approach to this challenge and has the potential to enhance user safety and protect vulnerable groups from harmful and offensive content. Future research will continue to refine and improve such systems to better address the evolving landscape of digital media and the challenges posed by harmful content.

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Automatic feature learning model combining functional connectivity network and graph regularization for depression detection

Cited by 17 publications

References 38 publications

Pathological Heart Rate Regulation in Apparently Healthy Individuals

Pathological Heart Rate Regulation in Apparently Healthy Individuals

Efficient Feature Learning Model of Motor Imagery EEG Signals with L1-Norm and Weighted Fusion

DeepCens: A deep learning‐based system for real‐time image and video censorship

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