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Yue H, Chen Z, Guo W, Sun L, Dai Y, Wang Y, Ma W, Fan X, Wen W, Lei W. Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice. Sleep Med Rev 2024; 74:101897. [PMID: 38306788 DOI: 10.1016/j.smrv.2024.101897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/30/2023] [Accepted: 01/04/2024] [Indexed: 02/04/2024]
Abstract
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.
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Affiliation(s)
- Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zhuqi Chen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenbin Guo
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Lin Sun
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Yidan Dai
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Yiming Wang
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenjun Ma
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, People's Republic of China
| | - Weiping Wen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China; Department of Otolaryngology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
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Zhang Z, Lin BS, Peng CW, Lin BS. Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2096-2105. [PMID: 38848223 DOI: 10.1109/tnsre.2024.3394738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorders.
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Kim Y, Lee M, Yoon J, Kim Y, Min H, Cho H, Park J, Shin T. Predicting Future Incidences of Cardiac Arrhythmias Using Discrete Heartbeats from Normal Sinus Rhythm ECG Signals via Deep Learning Methods. Diagnostics (Basel) 2023; 13:2849. [PMID: 37685387 PMCID: PMC10487044 DOI: 10.3390/diagnostics13172849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/29/2023] [Accepted: 09/01/2023] [Indexed: 09/10/2023] Open
Abstract
This study aims to compare the effectiveness of using discrete heartbeats versus an entire 12-lead electrocardiogram (ECG) as the input for predicting future occurrences of arrhythmia and atrial fibrillation using deep learning models. Experiments were conducted using two types of inputs: a combination of discrete heartbeats extracted from 12-lead ECG and an entire 12-lead ECG signal of 10 s. This study utilized 326,904 ECG signals from 134,447 patients and categorized them into three groups: true-normal sinus rhythm (T-NSR), atrial fibrillation-normal sinus rhythm (AF-NSR), and clinically important arrhythmia-normal sinus rhythm (CIA-NSR). The T-NSR group comprised patients with at least three normal rhythms in a year and no atrial fibrillation or arrhythmias history. Clinically important arrhythmia included atrial fibrillation, atrial flutter, atrial premature contraction, atrial tachycardia, ventricular premature contraction, ventricular tachycardia, right and left bundle branch block, and atrioventricular block over the second degree. The AF-NSR group included normal sinus rhythm paired with atrial fibrillation or atrial flutter within 14 days, and the CIA-NSR group comprised normal sinus rhythm paired with CIA occurring within 14 days. Three deep learning models, ResNet-18, LSTM, and Transformer-based models, were utilized to distinguish T-NSR from AF-NSR and T-NSR from CIA-NSR. The experiments demonstrated the potential of using discrete heartbeats in predicting future arrhythmia and atrial fibrillation incidences extracted from 12-lead electrocardiogram (ECG) signals alone, without any additional patient information. The analysis reveals that these discrete heartbeats contain subtle patterns that deep learning models can identify. Focusing on discrete heartbeats may lead to more timely and accurate diagnoses of these conditions, improving patient outcomes and enabling automated diagnosis using ECG signals as a biomarker.
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Affiliation(s)
- Yehyun Kim
- Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea; (Y.K.); (M.L.); (J.Y.)
| | - Myeonggyu Lee
- Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea; (Y.K.); (M.L.); (J.Y.)
| | - Jaeung Yoon
- Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea; (Y.K.); (M.L.); (J.Y.)
| | - Yeji Kim
- Department of Cardiology, Ewha Womans University Mokdong Hospital, Seoul 07985, Republic of Korea;
| | - Hyunseok Min
- Tomocube Inc., Daejeon 34141, Republic of Korea; (H.M.); (H.C.)
| | - Hyungjoo Cho
- Tomocube Inc., Daejeon 34141, Republic of Korea; (H.M.); (H.C.)
| | - Junbeom Park
- Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea; (Y.K.); (M.L.); (J.Y.)
- Department of Cardiology, Ewha Womans University Mokdong Hospital, Seoul 07985, Republic of Korea;
| | - Taeyoung Shin
- Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea; (Y.K.); (M.L.); (J.Y.)
- Department of Urology, Ewha Womans University Mokdong Hospital, Seoul 07985, Republic of Korea
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A Long Short-Term Memory Network Using Resting-State Electroencephalogram to Predict Outcomes Following Moderate Traumatic Brain Injury. COMPUTERS 2023. [DOI: 10.3390/computers12020045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Although traumatic brain injury (TBI) is a global public health issue, not all injuries necessitate additional hospitalisation. Thinking, memory, attention, personality, and movement can all be negatively impacted by TBI. However, only a small proportion of nonsevere TBIs necessitate prolonged observation. Clinicians would benefit from an electroencephalography (EEG)-based computational intelligence model for outcome prediction by having access to an evidence-based analysis that would allow them to securely discharge patients who are at minimal risk of TBI-related mortality. Despite the increasing popularity of EEG-based deep learning research to create predictive models with breakthrough performance, particularly in epilepsy prediction, its use in clinical decision making for the diagnosis and prognosis of TBI has not been as widely exploited. Therefore, utilising 60s segments of unprocessed resting-state EEG data as input, we suggest a long short-term memory (LSTM) network that can distinguish between improved and unimproved outcomes in moderate TBI patients. Complex feature extraction and selection are avoided in this architecture. The experimental results show that, with a classification accuracy of 87.50 ± 0.05%, the proposed prognostic model outperforms three related works. The results suggest that the proposed methodology is an efficient and reliable strategy to assist clinicians in creating an automated tool for predicting treatment outcomes from EEG signals.
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