Deep neural network technique for automated detection of ADHD and CD using ECG signal

Loh, Hui Wen; Ooi, Chui Ping; Oh, Shu Lih; Barua, Prabal Datta; Tan, Yi Ren; Molinari, Filippo; March, Sonja; Acharya, U. Rajendra; Fung, Daniel Shuen Sheng

doi:10.1016/j.cmpb.2023.107775

Cited by 17 publications

(3 citation statements)

References 39 publications

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“…Another crucial reason for the high accuracy could be the fact that heart rates are closely related to the presence of disorders, as discussed in References [34,35]. Also, we have compared our results with previous studies that have utilized various physiological signals to identify sleep disorders automatically, which are summarized in Table 10.…”

Section: Discussionmentioning

confidence: 93%

Sleep disorder identification using wavelet scattering on ECG signals

Sharma,

Lodhi,

Yadav

et al. 2023

Int J Imaging Syst Tech

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Sleep affects the functioning of all biological processes in the human body. Sleep disorders disrupt the well‐being of an individual and can potentially lead to other health complications. It affects a large part of the population, and their timely and efficient detection is crucial for improving the quality of health. Sleep disorders are generally analyzed using polysomnogram (PSG) signals, which must be captured in sophisticated laboratory settings. However, electrocardiogram (ECG) signals provide information about the electrical activity of the heart, which is strongly linked with disturbances in sleep and can be used as an indicator of disordered sleep. Along with that, the recording of ECG signals is much easier than other signals and can be done remotely without causing any significant disturbance to the subject. This brings practicality to the systems that are already being employed for sleep disorder detection. In this paper, we present a method for identifying types of sleep disorders using ECG signals. The objective of this paper is to investigate the use of ECG signals for the automated identification of insomnia, narcolepsy, periodic leg movement (PLM), rapid eye movement (REM) behavior disorder (RBD), and nocturnal frontal lobe epilepsy (NFLE) against healthy subjects. We aim to develop a machine learning‐based algorithm that automatically classifies ECG signals into these sleep disorder categories. The cyclic alternating pattern (CAP) sleep database was used in this study, which contains PSG recordings from individuals with and without sleep disorders, including insomnia, narcolepsy, PLM, RBD, and NFLE. A wavelet scattering network has been used to extract features from the ECG signals. Various classifiers were tested, and the ensemble bag of trees classifier provided the optimum performance. An overall accuracy of 98% was obtained for the identification of sleep disorders, along with a classification accuracy of 99.37%, 99.45%, 99.23%, 99.4%, and 99.65% for insomnia, narcolepsy, NFLE, PLM, and RBD, respectively, for binary classification against healthy subject data. Our results show that wavelet scattering network and ensemble of bagged tree (EbagT) classification can accurately identify various sleep disorders. They provided evidence that ECG signals can be used to identify and diagnose sleep disorders, which could lead to the development of accurate detection of these sleep disorders. Also, our study demonstrated the effectiveness of the proposed algorithm in identifying multiple sleep disorders, which could lead to more efficient and cost‐effective diagnosis and treatment of sleep disorders.

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Section: Discussionmentioning

confidence: 93%

Sleep disorder identification using wavelet scattering on ECG signals

Sharma,

Lodhi,

Yadav

et al. 2023

Int J Imaging Syst Tech

Self Cite

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“…Its main characteristics include persistent inattention, hyperactivity and impulsive behaviors, which often have a significant impact on an individual's ability to learn, socialize, and work (Tang et al, 2020 ). The diagnosis of ADHD is complex and varied and often requires a combination of medical, psychological and behavioral evaluations (Loh et al, 2023 ). Currently, the exact cause of ADHD is not fully understood, and it is widely believed that a combination of genetics, environmental factors, and variations in brain development play a role (Tan et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD

Yu,

Fang

2024

Front. Neurosci.

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Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly affects children and adults worldwide, characterized by persistent inattention, hyperactivity, and impulsivity. Current research in this field faces challenges, particularly in accurate diagnosis and effective treatment strategies. The analysis of motor information, enriched by artificial intelligence methodologies, plays a vital role in deepening our understanding and improving the management of ADHD. The integration of AI techniques, such as machine learning and data analysis, into the study of ADHD-related motor behaviors, allows for a more nuanced understanding of the disorder. This approach facilitates the identification of patterns and anomalies in motor activity that are often characteristic of ADHD, thereby contributing to more precise diagnostics and tailored treatment strategies. Our approach focuses on utilizing AI techniques to deeply analyze patients' motor information and cognitive processes, aiming to improve ADHD diagnosis and treatment strategies. On the ADHD dataset, the model significantly improved accuracy to 98.21% and recall to 93.86%, especially excelling in EEG data processing with accuracy and recall rates of 96.62 and 95.21%, respectively, demonstrating precise capturing of ADHD characteristic behaviors and physiological responses. These results not only reveal the great potential of our model in improving ADHD diagnostic accuracy and developing personalized treatment plans, but also open up new research perspectives for understanding the complex neurological logic of ADHD. In addition, our study not only suggests innovative perspectives and approaches for ADHD treatment, but also provides a solid foundation for future research exploring similar complex neurological disorders, providing valuable data and insights. This is scientifically important for improving treatment outcomes and patients' quality of life, and points the way for future-oriented medical research and clinical practice.

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“…Over the last years in ECG analysis, the contributions of DL have immensely increased [14,15,16]. Some of these contributions have addressed the ECG denoising problem, but the most successful of them have focused on classification applications, particularly detecting and classifying arrhythmias [5,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

Exploring Cardiac Rhythms and Improving ECG Beat Classification Through Latent Spaces

Vadillo-Valderrama,

Chaquet-Ulldemolins,

Goya-Esteban

et al. 2024

IEEE Access

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In recent years, a wide variety of Machine Learning (ML) algorithms, including Deep Learning (DL) methods, have been proposed for electrocardiogram (ECG) beat classification. However, accurately discerning ECG beat types faces challenges due to noise interference and inherent imbalances among different classes. Moreover, understanding mathematical models enclosed by black-box learning systems is an open issue today. Our study employed a manifold learning algorithm capable of mapping highdimensional data into a latent space to conduct a comprehensive analysis within a neural network learning framework. This approach involved the following studies: (1) Examining the intermediate high-dimensional latent space in simple architectures by studying its projection into a visualizable latent space; (2) Exploring the influence of class imbalance on the configuration of the latent space; (3) Evaluating and analyzing the compensatory effects of employing diverse DL architectures, such as modified autoencoders and Generative Adversarial Networks (GANs), specifically in generating data augmentation through synthetic beats. The experimental results demonstrated the effectiveness of our methodology in mitigating noise and addressing inter-class imbalance, notably enhancing the diagnostic Area Under the ROC Curve (AUC) in ECG signal analysis. Implementation of GAN data augmentation techniques resulted in a 2% improvement, elevating the AUC from 0, 9332 to 0, 9520 in the biclass dataset. Similarly, the AUC values increased by 2%, from 0, 9020 to 0, 9223, for the multiclass dataset. These findings highlight the impact of appropriate data augmentation techniques on AUC improvement. Furthermore, visualizing latent spaces during beat classifier design significantly contributes to the development of solid and principled multiclass beat-discriminating systems.

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Deep neural network technique for automated detection of ADHD and CD using ECG signal

Cited by 17 publications

References 39 publications

Sleep disorder identification using wavelet scattering on ECG signals

Sleep disorder identification using wavelet scattering on ECG signals

Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD

Exploring Cardiac Rhythms and Improving ECG Beat Classification Through Latent Spaces

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