High Accuracy Respiration and Heart Rate Detection Based on Artificial Neural Network Regression

Tsai, Yu-Chiao; Lai, Shih-Hsuan; Ho, Ching-Ju; Wu, Fang; Henrickson, Lindor; Wei, Chia-Chien; Chen, Irwin; Wu, Vincent; Chen, Jyehong

doi:10.1109/embc44109.2020.9175161

Cited by 13 publications

(25 citation statements)

References 7 publications

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“…Continuous vital signs monitoring can prevent life-threatening scenarios by providing early warnings before a decline of the patient's status [104]. In this sub-category of papers on value estimation we include papers dealing with the estimation of vital signs, such as HR (n = 21) [17], [22], [25], [28], [29], [52], [55]- [57], [62], [64], [74], [76], [82], [87], [91], [93], [96]- [98], BR [58], [66], [88], [89], [99], [102] (n = 6), both HR and BR [31], [34], [40], [53], [54], [92], [103] (n = 7), BP [65], [67], [75], [83], [85], [100], [101] (n = 7), oxygen saturation (SpO2) [84] (n = 1), and spyrometric indices [61] (n = 1) in a given windows of observation.…”

Section: A First Cluster Tasksmentioning

confidence: 99%

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Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Nocera,

Senigagliesi,

Raimondi

et al. 2024

Preprint

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In the field of physiological signals monitoring and its applications, non-contact technology is often proposed as a possible alternative to traditional contact devices. The ability to extract information about a patient’s health status in an unobtrusive way, without stressing the subject and without the need of qualified personnel, fuels research in this growing field. Among the various methodologies, RADAR-based non-contact technology is gaining great interest. This scoping review aims to summarize the main research lines concerning RADAR-based physiological sensing and machine learning applications reporting recent trends, issues and gaps with the scientific literature, best methodological practices, employed standards to be followed, challenges, and future directions. After a systematic search and screening, one hundred and ninety two papers were collected following the guidelines of PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). The included records covered two macro-areas being regression of physiological signals or physiological features (n = 68 papers) and the other a cluster of papers regarding the processing of RADAR-based physiological signals and features applied to four fields of interest, being RADAR-based diagnosis (n = 73), RADAR-based human behaviour monitoring (n = 21), RADAR-based biometrics authentication (n = 18) and RADAR-based affective computing (n = 9). Papers collected under the diagnosis category were further divided, on the basis of their aims: in breath pattern classification (n = 39), infection detection (n = 10), sleep stage classification (n = 9), heart disease detection (n = 8) and quality detection (n = 7). Papers collected under the human behaviour monitoring were further divided based on their aims: fatigue detection (n = 8), human detection (n = 7), human localisation (n = 4), human orientation (n = 2), and activities classification (n = 3).

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Section: A First Cluster Tasksmentioning

confidence: 99%

“…For the hybrid architecture the deep learning model used was an encoder-decoder type in combination with extracted features from K-nearest neighbour. The general [17], [22], [25], [28], [29], [31], [34], [40], [52]- [58], [61], [62], [64]- [67], [74]- [76], [82]- [89], [91]- [93], [96]- [103] Signal reconstruction (n = 18)…”

Section: A First Cluster Tasksmentioning

confidence: 99%

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Nocera,

Senigagliesi,

Raimondi

et al. 2024

Preprint

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“…Radar systems can monitor RR by using frequency-modulated continuous-wave (FMCW) to observe Doppler shifts associated with breathing movements, and have been able to obtain accuracy of 99% (Tsai et al, 2020). FMCW radar works by transmitting frequency modulated pulses, i.e., pulses with periodic shifts in frequency, and the received echos undergo respective frequency shifts due to the motion of the body, known as the Doppler effect.…”

Section: Respiratory Ratementioning

confidence: 99%

A Review on Wearable and Contactless Sensing for COVID-19 With Policy Challenges

et al. 2021

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The COVID-19 pandemic has affected more than 100 million people worldwide, with around 500,000 cases reported daily. This has led to the overwhelming of healthcare systems even in developed countries such as the US, UK, etc. Remote monitoring of COVID-19 patients with non-serious symptoms can help reduce the burden on healthcare facilities and make them available for high risk groups and the seriously affected. The pandemic has accelerated the demand for the remote patient monitoring (RPM) technologies, and the market is expected to reach 2.14 billion in 2027 from the value of 786.4 million in 2019. In RPM programs, there are two types of sensors that can be used: wearable and contactless. The former, which is currently more widely used, is not only more obtrusive and uncomfortable, but can also lead to cross-infection through patient contact. These two types of technologies are discussed and compared for each vital sign. In the respiratory system, the vital signs are the respiratory rate (RR) and oxygen saturation (SpO2), while for the latter, they are the heart rate/rhythm and the blood pressure (BP). Then, the discussion is broadened to policy level changes needed to expedite the use of such technologies for remote patient monitoring (RPM) in the world. Around 80% of countries' RPM programs are either informal or in a pilot phase, and thus lack policies and an established regulatory framework to implement their programs. The various policies needed to initiate, deliver, and reimburse RPM programs during emergency situations and outbreaks are discussed. Finally, technologies such as contactless systems, robotics, and Internet-of-things (IoT) that will revolutionize healthcare in the future by reducing the interaction between physicians and patients and cross-infection are discussed.

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“…Simultaneously, an increasing number of researchers are focusing on HR signal estimation using DL methods. Various approaches to measuring HR using artificial neural networks (ANN) have been proposed [ 11 ], and the majority of the methods contribute to reducing the noise in order to obtain an accurate HR number. A considerable number of researchers focus on the automatic pathological classifications of ECGs [ 12 , 13 ], and there are still a few researchers working on improving the performance of conventional methods.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Estimation from Incomplete Electrocardiography Signals

Song

Chen

Zhang

2023

Sensors

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As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60–120 bpm in the database without significant arrhythmias and a corresponding range of 30–150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.

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High Accuracy Respiration and Heart Rate Detection Based on Artificial Neural Network Regression

Cited by 13 publications

References 7 publications

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

A Review on Wearable and Contactless Sensing for COVID-19 With Policy Challenges

Heart Rate Estimation from Incomplete Electrocardiography Signals

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