Multiscale Analysis of Intensive Longitudinal Biomedical Signals and Its Clinical Applications

Nakamura, Tôru; Kiyono, Ken; Wendt, Herwig; Abry, Patrice; Yamamoto, Yoshiharu

doi:10.1109/jproc.2015.2491979

Cited by 59 publications

(52 citation statements)

References 141 publications

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“…However, there are still some limitations: Firstly, recording long-term audio/video data of the subjects is time-consuming and expensive; secondly, most of the previous studies relied on human hand-crafted features, which may need specific domain knowledge. Motivated by the success of using the SPA data (recorded by the low cost and low energyconsumption wearable sensors) to model the human behaviour in daily life [14,16,17], and the successful application of the BoAW approach [23,25], we introduce, and investigate the efficacy of a novel BoBW approach for depression detection via the SPA data.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, an objective evaluation of depressive mood is vital in terms of the diagnosis and treatment of depressive disorders [14]. Within the fast development of advanced wearable and/or biomedical sensing technologies, it is feasible to collect, and analyse long-term, continuous biomedical signals, which may benefit the area of automatically tracking the mental health status of subjects [16]. In particular, there are tremendous needs in real practice to use the state-of-the-art signal processing and machine learning techniques for analysing the physiological signals, which will lead to the development of portable, less/non-invasive devices for screening, monitoring, and managing a subject's mental health.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, there are tremendous needs in real practice to use the state-of-the-art signal processing and machine learning techniques for analysing the physiological signals, which will lead to the development of portable, less/non-invasive devices for screening, monitoring, and managing a subject's mental health. In this light, we propose a novel bag-of-behaviour-words (BoBW) approach for detection of MDD from spontaneous physical activity (SPA) data [14,16] recorded via a watch-type device in the subject's daily life.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Detection of Major Depressive Disorder via a Bag-of-Behaviour-Words Approach

Qian

Kuromiya

Ren

et al. 2019

Proceedings of the Third International Symposium on Image Computing and Digital Medicine

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In recent years, machine learning has been increasingly applied to the area of mental health diagnosis, treatment, support, research, and clinical administration. In particular, using less-invasive wearables combined with the artificial intelligence to monitor, or diagnose the mental diseases has tremendous needs in real practice. To this end, we propose a novel approach for automatic detection of major depressive disorder. Firstly, spontaneous activity physical data are recorded by a watch-type device equipped with an activity monitor. Subsequently, a bag-of-behaviour-words approach is applied to extract higher representations from the raw sensor data in an unsupervised scenario. Finally, a support vector machine is selected as the classifier to make the predictions on screening the major depressive disorder. There are 69 healthy control subjects, and 14 major depressive disorder patients involved in this study. The experimental results demonstrate the effectiveness of the proposed method in a rigorous subject-independent test, which achieves an unweighted average recall at 59.3 % (an accuracy of 66.0 %). This unweighted average recall significantly ( < .05, onetailed -test) outperforms human hand-crafted features with an unweighted average recall at 53.6 % (an accuracy of 61.7 %).

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Detection of Major Depressive Disorder via a Bag-of-Behaviour-Words Approach

Qian

Kuromiya

Ren

et al. 2019

Proceedings of the Third International Symposium on Image Computing and Digital Medicine

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“…From a practical perspective, abnormal aerobic responses to exercise may precede the clinical detection of non-communicable diseases4. Therefore, wearable technologies that continuously evaluate the aerobic response during non-supervised activities of daily living ( ADL ) have the potential to identify not only changes in physical fitness, but also disease states before the manifestation of clinical symptoms56.…”

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confidence: 99%

Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living

Beltrame

Amelard

Wong

et al. 2017

Sci Rep

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Currently, oxygen uptake () is the most precise means of investigating aerobic fitness and level of physical activity; however, can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict during activities of daily living (ADL). The main objective of this study was to develop and verify the methods required to predict and investigate the dynamics during ADL. The variables derived from the wearable sensors were used to create a predictor based on a random forest method. The temporal dynamics were assessed by the mean normalized gain amplitude (MNG) obtained from frequency domain analysis. The MNG provides a means to assess aerobic fitness. The predicted during ADL was strongly correlated (r = 0.87, P < 0.001) with the measured and the prediction bias was 0.2 ml·min−1·kg−1. The MNG calculated based on predicted was strongly correlated (r = 0.71, P < 0.001) with MNG calculated based on measured data. This new technology provides an important advance in ambulatory and continuous assessment of aerobic fitness with potential for future applications such as the early detection of deterioration of physical health.

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“…In a single day, a continuous glucose monitor can passively collect over 200 times the amount of data one would normally collect using standard blood glucose measurement practices (e.g., manual measurement). Such wearable health monitors have led to the rapid accumulation of large amounts of rich longitudinal health data in the form of time series [25]. Still, in terms of utility, these data have yet to reach their full potential.…”

Section: Introductionmentioning

confidence: 99%

Contextual Motifs

Fox

Ang

Jaiswal

et al. 2017

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at the time the data were collected). We hypothesize that these additional contextual data could increase the utility of motifs. us, we propose an extension to motifs, contextual motifs, that incorporates context. Recognizing that, o entimes, context may be unobserved or unavailable, we focus on methods to jointly infer motifs and context. Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs. In particular, we discovered contextual motifs in continuous glucose monitor (CGM) data collected from patients with type 1 diabetes. Compared to their contextless counterparts, these contextual motifs led to be er predictions of hypo-and hyperglycemic events. Our results suggest that even when inferred, context is useful in both a long-and short-term prediction horizon when processing and interpreting physiological waveform data.

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Multiscale Analysis of Intensive Longitudinal Biomedical Signals and Its Clinical Applications

Abstract: Recent advances in wearable and/or biomedical sensing technologies have made it possible to record continuous biomedical signals over long periods of time. This paper reviews multiscale approaches for their analysis.

Cited by 59 publications

References 141 publications

Automatic Detection of Major Depressive Disorder via a Bag-of-Behaviour-Words Approach

Automatic Detection of Major Depressive Disorder via a Bag-of-Behaviour-Words Approach

Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living

Contextual Motifs

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