PPG Signal Reconstruction Using Deep Convolutional Generative Adversarial Network

Wang, Yuning; Azimi, Iman; Kazemi, Kianoosh; Rahmani, Amir M.; Liljeberg, Pasi

doi:10.1109/embc48229.2022.9871678

Cited by 11 publications

(7 citation statements)

References 16 publications

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“…In ubiquitous monitoring settings, signal collection with PPG-based wearable devices can be affected by the presence of noise. To overcome this issue, we implement the methods to extract HR and HRV features from raw collected PPG signals [19]- [21]. We first conduct a signal quality assessment to classify PPG signals as clean or noisy, then reconstruct short-term noisy segments via a generative adversarial network.…”

Section: Methodsmentioning

confidence: 99%

Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings

Yang,

Azimi,

Jafarlou

et al. 2023

2023 IEEE 19th International Conference on Body Sensor Networks (BSN)

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The adverse effects of loneliness on both physical and mental well-being are profound. Although previous research has utilized mobile sensing techniques to detect mental health issues, few studies have utilized state-of-the-art wearable devices to forecast loneliness and estimate the physiological manifestations of loneliness and its predictive nature. The primary objective of this study is to examine the feasibility of forecasting loneliness by employing wearable devices, such as smart rings and watches, to monitor early physiological indicators of loneliness. Furthermore, smartphones are employed to capture initial behavioral signs of loneliness. To accomplish this, we employed personalized machine learning techniques, leveraging a comprehensive dataset comprising physiological and behavioral information obtained during our study involving the monitoring of college students. Through the development of personalized models, we achieved a notable accuracy of 0.82 and an F-1 score of 0.82 in forecasting loneliness levels seven days in advance. Additionally, the application of Shapley values facilitated model explainability. The wealth of data provided by this study, coupled with the forecasting methodology employed, possesses the potential to augment interventions and facilitate the early identification of loneliness within populations at risk.

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Section: Methodsmentioning

confidence: 99%

Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings

Yang,

Azimi,

Jafarlou

et al. 2023

2023 IEEE 19th International Conference on Body Sensor Networks (BSN)

Self Cite

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“…When subjected to slight noise interference, the corrupted part can be reconstructed by leveraging the information contained in the preceding clean parts. To accomplish this, we employ a PPG reconstruction approach 68 based on a deep convolutional generative adversarial network to reconstruct noisy parts for durations of up to 15 seconds.…”

Section: Hr and Hrv Data Extractionmentioning

confidence: 99%

Preterm Birth Risk Stratification through Longitudinal Heart Rate and HRV Monitoring in Daily Life

Feli,

Azimi,

Sarhaddi

et al. 2024

Preprint

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Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk. In this paper, we conduct a longitudinal study to assess the risk of PTB by examining maternal ANS activity through heart rate (HR) and heart rate variability (HRV). To achieve this, we collect long-term raw photoplethysmogram (PPG) signals from 58 pregnant women (including seven preterm cases) from gestational weeks 12-15 to three months post-delivery using smartwatches in daily life settings. We employ a PPG processing pipeline to accurately extract HR and HRV, and an autoencoder machine learning model with SHAP analysis to generate explainable abnormality scores indicative of PTB risk. Our results reveal distinctive patterns in PTB abnormality scores during the second pregnancy trimester, indicating the potential for early PTB risk estimation. Moreover, we find that HR, average of interbeat intervals (AVNN), SD1SD2 ratio, and standard deviation of interbeat intervals (SDNN) emerge as significant PTB indicators.

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“…The SQA classifies PPG signals as "clean" or "noisy" by extracting five features from the signal, including interquartile range, standard deviation of the power spectral density, range of energy of heart cycles, average Euclidean distances, and average correlation between a template and heart cycles [52]. After we performed SQA, short-term "noisy" segments (less than 15 seconds) were reconstructed using a trained generative adversarial network (GAN) model [53]. The GAN model was trained to reconstruct noisy PPG using the information both in the distorted part and its proceeding clean signals.…”

Section: Plos Onementioning

confidence: 99%

Objective monitoring of loneliness levels using smart devices: A multi-device approach for mental health applications

Jafarlou,

Azimi,

Lai

et al. 2024

PLoS ONE

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Loneliness is linked to wide ranging physical and mental health problems, including increased rates of mortality. Understanding how loneliness manifests is important for targeted public health treatment and intervention. With advances in mobile sending and wearable technologies, it is possible to collect data on human phenomena in a continuous and uninterrupted way. In doing so, such approaches can be used to monitor physiological and behavioral aspects relevant to an individual’s loneliness. In this study, we proposed a method for continuous detection of loneliness using fully objective data from smart devices and passive mobile sensing. We also investigated whether physiological and behavioral features differed in their importance in predicting loneliness across individuals. Finally, we examined how informative data from each device is for loneliness detection tasks. We assessed subjective feelings of loneliness while monitoring behavioral and physiological patterns in 30 college students over a 2-month period. We used smartphones to monitor behavioral patterns (e.g., location changes, type of notifications, in-coming and out-going calls/text messages) and smart watches and rings to monitor physiology and sleep patterns (e.g., heart-rate, heart-rate variability, sleep duration). Participants reported their loneliness feeling multiple times a day through a questionnaire app on their phone. Using the data collected from their devices, we trained a random forest machine learning based model to detect loneliness levels. We found support for loneliness prediction using a multi-device and fully-objective approach. Furthermore, behavioral data collected by smartphones generally were the most important features across all participants. The study provides promising results for using objective data to monitor mental health indicators, which could provide a continuous and uninterrupted source of information in mental healthcare applications.

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PPG Signal Reconstruction Using Deep Convolutional Generative Adversarial Network

Cited by 11 publications

References 16 publications

Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings

Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings

Preterm Birth Risk Stratification through Longitudinal Heart Rate and HRV Monitoring in Daily Life

Objective monitoring of loneliness levels using smart devices: A multi-device approach for mental health applications

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