A Contrastive Predictive Coding-Based Classification Framework for Healthcare Sensor Data

Ren, Chaoxu; Sun, Le; Peng, Dandan

doi:10.1155/2022/5649253

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…EEG. In all the reviewed papers, we find 31.7% studies [20,21,[28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43] used EEG. Among these, only one of these studies used intracranial EEG (invasive), and the others used noninvasive EEG.…”

Section: Data Types Of Medical Time Seriesmentioning

confidence: 99%

“…Consistent with the distribution of data types, 25.5% of the reviewed studies performed experiments on cardiovascular disease-related detection/diagnosis. The specific applications mainly include cardiac abnormalities detection [46,47,53,54], cardiac arrhythmia detection or clustering [18,36,37,[48][49][50][51][52]55], and heart sound classification [57]. Nearly all of the studies in this scope are based on ECG data, except one work [57] used PCG signals that record heart sounds and murmurs [73].…”

Section: Medical Applicationsmentioning

confidence: 99%

“…Sleep status. A large portion (20%) of research is related to sleep states [21,28,[32][33][34][35][37][38][39][40]65], such as sleep stage scoring and sleep disorder classification (e.g., insomnia detection). Sleep stage can be categorized into five stages in accordance with the patterns of specific physiological signals (e.g., EEG, EMG, EOG): wake, non-rapid eye movement stage 1, nonrapid eye movement stage 2, non-rapid eye movement stage 3, and rapid eye movement stage [74].…”

Section: Medical Applicationsmentioning

confidence: 99%

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Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review

Liu

Alavi

Li³

et al. 2023

Sensors

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Medical time series are sequential data collected over time that measures health-related signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive care unit (ICU) readings. Analyzing medical time series and identifying the latent patterns and trends that lead to uncovering highly valuable insights for enhancing diagnosis, treatment, risk assessment, and disease progression. However, data mining in medical time series is heavily limited by the sample annotation which is time-consuming and labor-intensive, and expert-depending. To mitigate this challenge, the emerging self-supervised contrastive learning, which has shown great success since 2020, is a promising solution. Contrastive learning aims to learn representative embeddings by contrasting positive and negative samples without the requirement for explicit labels. Here, we conducted a systematic review of how contrastive learning alleviates the label scarcity in medical time series based on PRISMA standards. We searched the studies in five scientific databases (IEEE, ACM, Scopus, Google Scholar, and PubMed) and retrieved 1908 papers based on the inclusion criteria. After applying excluding criteria, and screening at title, abstract, and full text levels, we carefully reviewed 43 papers in this area. Specifically, this paper outlines the pipeline of contrastive learning, including pre-training, fine-tuning, and testing. We provide a comprehensive summary of the various augmentations applied to medical time series data, the architectures of pre-training encoders, the types of fine-tuning classifiers and clusters, and the popular contrastive loss functions. Moreover, we present an overview of the different data types used in medical time series, highlight the medical applications of interest, and provide a comprehensive table of 51 public datasets that have been utilized in this field. In addition, this paper will provide a discussion on the promising future scopes such as providing guidance for effective augmentation design, developing a unified framework for analyzing hierarchical time series, and investigating methods for processing multimodal data. Despite being in its early stages, self-supervised contrastive learning has shown great potential in overcoming the need for expert-created annotations in the research of medical time series.

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Section: Data Types Of Medical Time Seriesmentioning

confidence: 99%

Section: Medical Applicationsmentioning

confidence: 99%

Section: Medical Applicationsmentioning

confidence: 99%

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Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review

Liu

Alavi

Li³

et al. 2023

Sensors

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“…Ren et al . [121] applied a modified version of contrastive predictive coding, while Jiang et al . [122] chose SimCLR.…”

Section: ) Sleep Stagingmentioning

confidence: 99%

Applications of Self-Supervised Learning to Biomedical Signals: A Survey

Pup,

Atzori

2023

IEEE Access

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Over the last decade, deep learning applications in biomedical research have exploded, demonstrating their ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models for biomedical applications requires large amounts of data annotated by experts, whose collection is often time-and cost-prohibitive. Self-Supervised Learning (SSL) has emerged as a prominent solution for such problem, as it allows to learn powerful representations from vast unlabeled data by producing supervisory signals directly from the data.

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“…Contrastive learning is predominant in works dealing with sleep staging. Ren et al [139] applied contrastive predictive coding with a modified version of the InfoNCE loss function to better fit EEG data, while Jiang et al [130] chose SimCLR. Yang et al [133] proposed ContraWR, a novel approach which aims at solving the problem of negative sampling by using the average representation over the dataset (called the world representation) as the only contrastive information.…”

Section: B Self-supervised Learning On Eegmentioning

confidence: 99%

Applications of Self-Supervised Learning to Biomedical Signals: where are we now

Pup¹,

Atzori²

2023

Preprint

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<p>Over the last decade, deep learning applications in biomedical research have exploded, demonstrating the ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models requires large amounts of data annotated by experts, whose collection is often time- and cost- prohibitive in the biomedical domain. Self-Supervised Learning (SSL) has emerged as a prominent solution for these problems, as it allows to learn powerful data representations in an unsupervised manner. Despite most applications in biomedical research targeted images, the high amount of recent works targeting biosignals can make it difficult for researchers to have a complete picture of the current situation. The aim of this paper is to outline and clarify the state of the art in the domain. The article briefly summarizes the nature and acquisition modality of biomedical signals, introduces the SSL method, and provides a complete but synthetic overview of the main works applying SSL for the analysis of biomedical signals. The analysis of the scientific literature highlights the importance of SSL, confirming its potential to improve the integration of deep learning into clinical tasks. </p>

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A Contrastive Predictive Coding-Based Classification Framework for Healthcare Sensor Data

Cited by 4 publications

References 24 publications

Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review

Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review

Applications of Self-Supervised Learning to Biomedical Signals: A Survey

Applications of Self-Supervised Learning to Biomedical Signals: where are we now

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