Long–Short Ensemble Network for Bipolar Manic-Euthymic State Recognition Based on Wrist-Worn Sensors

Côté-Allard, Ulysse; Jakobsen, Petter; Stautland, Andrea; Nordgreen, Tine; Fasmer, Ole Bernt; Oedegaard, Ketil J.; Tørresen, Jim

doi:10.1109/mprv.2022.3155728

Cited by 20 publications

(22 citation statements)

References 17 publications

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“…Our findings indicate that analysing motor activity time series with machine-learning techniques present promising abilities to discriminate between depressed patients and healthy controls, 42 as well as differentiating between manic and asymptomatic bipolar patients. 43 , 44 Self-assessment chatbot (ADHD) : In this study, we developed a chatbot for screening symptoms for ADHD in adults based on the psychometric questionnaire Adult ADHD Self-Report Scale (ASRS). In the study, we compared the responses from the conversational chatbot with responses on the standardized paper-based ASRS.…”

Section: Resultsmentioning

confidence: 99%

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Towards adaptive technology in routine mental health care

et al. 2022

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This paper summarizes the information technology-related research findings after 5 years with the INTROducing Mental health through Adaptive Technology project. The aim was to improve mental healthcare by introducing new technologies for adaptive interventions in mental healthcare through interdisciplinary research and development. We focus on the challenges related to internet-delivered psychological treatments, emphasising artificial intelligence, human-computer interaction, and software engineering. We present the main research findings, the developed artefacts, and lessons learned from the project before outlining directions for future research. The main findings from this project are encapsulated in a reference architecture that is used for establishing an infrastructure for adaptive internet-delivered psychological treatment systems in clinical contexts. The infrastructure is developed by introducing an interdisciplinary design and development process inspired by domain-driven design, user-centred design, and the person based approach for intervention design. The process aligns the software development with the intervention design and illustrates their mutual dependencies. Finally, we present software artefacts produced within the project and discuss how they are related to the proposed reference architecture. Our results indicate that the proposed development process, the reference architecture and the produced software can be practical means of designing adaptive mental health care treatments in correspondence with the patients’ needs and preferences. In summary, we have created the initial version of an information technology infrastructure to support the development and deployment of Internet-delivered mental health interventions with inherent support for data sharing, data analysis, reusability of treatment content, and adaptation of intervention based on user needs and preferences.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Towards adaptive technology in routine mental health care

et al. 2022

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“…Previous research endeavours pursued mood states classification in BD using data from wearable devices. Côté-Allard et al [7] introduced a deep learning-based ensemble method to distinguish manic from euthymic BD patients, which leverage long (20h) and short (5 minutes) time intervals based on actigraphy and EDA. Their classification accuracy on 47 BD patients achieved an accuracy of 91.59% in euthymia/mania recognition.…”

Section: Related Workmentioning

confidence: 99%

Can machine learning with data from wearable devices distinguish disease severity levels and generalise across patients? A pilot study in Mania and Depression

Corponi

Anmella

et al. 2022

Preprint

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Mood disorders are severe and chronic mental conditions exacting high costs from society. The lack of reliable biomarkers to aid clinicians in tailoring pharmacotherapy based on distinguishable patient-specific traits means that the current prescribing paradigm is largely one of trial and error. Previous studies showed that different biological signatures, such as patterns of heart rate variability or electro-dermal reactivity, are associated with clinically meaningful outcomes. Against this backdrop, the advances in machine learning and the spread of wearable devices capable of providing continuous and ecological monitoring of patients may unlock great opportunities in mental healthcare. We herewith present a pilot study on mania and depression where we moved beyond the simple disease state binary classification but pursued the more informative and clinically meaningful task of differentiating between levels of disease severity. While most previous similar endeavours used recording segments extracted from the same subjects for both training and testing, we explicitly carried out model development and evaluation on segments from different groups of patients, in order to have a fair assessment of the model out-of-sample generalisation. This illustrated how individuals heterogeneity and non-disease-related dimensions of variations (e.g. sex, age, physical fitness) may dominate the signal so that in low sample size regimes a model might learn and overfit subject-specific patterns rather than capturing disease-relevant traits generalisable across disorders. Lastly, we developed a viable baseline for pre-processing raw data from wristband recordings and compared three classical and two deep-learning models to identify levels of disease severity.

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“…Psychophysiological data acquisition with wearables has been shown to play an important role in the ongoing medical paradigm shift, i.e., moving from disease treatment to prevention and health management [ 1 ]. Using neuropsychiatry as an example, early research has demonstrated that wearables have the potential to accurately monitor the health condition of patients with major depression [ 2 ], bipolar disorder [ 3 ], and epilepsy [ 4 ]. Often, health monitoring through wearables can even represent an adjunct therapeutic plan that extends established practices.…”

Section: Introductionmentioning

confidence: 99%

“…The set of sensors most commonly found in wearables consist of an accelerometer, a Photoplethysmography (PPG) sensor, and an Electrodermal Activity (EDA) sensor [ 3 , 4 ], which are used to track body movement, Heart-rate (HR) and autonomic arousal, respectively. Multimodal data acquisition has been shown to outperform unimodal approaches and to provide promising results in multiple clinical applications [ 2 , 3 , 4 , 9 ]. In fact, there are even FDA-approved commercially available wearable devices that use the aforementioned combination of sensors (e.g., Empatica Embrace for epileptic seizure monitoring).…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Electrodermal Activity and Photoplethysmography Data Acquisition at the Foot Using a Sock Form Factor

Ferreira

Silva

Alves

et al. 2023

Sensors

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Wearable devices have been shown to play an important role in disease prevention and health management, through the multimodal acquisition of peripheral biosignals. However, many of these wearables are exposed, limiting their long-term acceptability by some user groups. To overcome this, a wearable smart sock integrating a PPG sensor and an EDA sensor with textile electrodes was developed. Using the smart sock, EDA and PPG measurements at the foot/ankle were performed in test populations of 19 and 15 subjects, respectively. Both measurements were validated by simultaneously recording the same signals with a standard device at the hand. For the EDA measurements, Pearson correlations of up to 0.95 were obtained for the SCL component, and a mean consensus of 69% for peaks detected in the two locations was obtained. As for the PPG measurements, after fine-tuning the automatic detection of systolic peaks, the index finger and ankle, accuracies of 99.46% and 87.85% were obtained, respectively. Moreover, an HR estimation error of 17.40±14.80 Beats-Per-Minute (BPM) was obtained. Overall, the results support the feasibility of this wearable form factor for unobtrusive EDA and PPG monitoring.

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Long–Short Ensemble Network for Bipolar Manic-Euthymic State Recognition Based on Wrist-Worn Sensors

Cited by 20 publications

References 17 publications

Towards adaptive technology in routine mental health care

Towards adaptive technology in routine mental health care

Can machine learning with data from wearable devices distinguish disease severity levels and generalise across patients? A pilot study in Mania and Depression

Feasibility of Electrodermal Activity and Photoplethysmography Data Acquisition at the Foot Using a Sock Form Factor

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