Using Accelerometric and Gyroscopic Data to Improve Blood Pressure Prediction from Pulse Transit Time Using Recurrent Neural Network

Ghosh, Shrimanti; Banerjee, Ankur; Ray, Nilanjan; Wood, Peter W.; Boulanger, Pierre; Padwal, Raj

doi:10.1109/icassp.2018.8461959

Cited by 8 publications

(10 citation statements)

References 15 publications

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“…Cardiovascular-disease classification experiments were carried out on PPG dataset and CNBP dataset. To evaluate the classification performance of the multi-scale feature-extraction model, three different classification methods including single neural network model 34 , parallel neural network model 35 , and LSTM model 36 were introduced for comparison. Comparative studies show that the multi-scale feature-extraction model outperforms the other classification methods in terms of identification accuracy, stability, and sensitivity, and the multi-scale feature-extraction model consumes less time for training.…”

Section: Discussionmentioning

confidence: 99%

“…On the premise of retaining the structure of the pre-processing layer and the reasoning layer, as well as the network parameters, the network layer is changed for comparison experiments. Zhan 34 36 et al extracted PWC features based on LSTM to predict systolic and diastolic blood pressure. The training process of different network models is shown in Figure 7.…”

Section: Setting Of Model Parametersmentioning

confidence: 99%

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Few-shot pulse wave contour classification based on multi-scale feature extraction

Liu

Mao

et al. 2021

Sci Rep

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The annotation procedure of pulse wave contour (PWC) is expensive and time-consuming, thereby hindering the formation of large-scale datasets to match the requirements of deep learning. To obtain better results under the condition of few-shot PWC, a small-parameter unit structure and a multi-scale feature-extraction model are proposed. In the small-parameter unit structure, information of adjacent cells is transmitted through state variables. Simultaneously, a forgetting gate is used to update the information and retain long-term dependence of PWC in the form of unit series. The multi-scale feature-extraction model is an integrated model containing three parts. Convolution neural networks are used to extract spatial features of single-period PWC and rhythm features of multi-period PWC. Recursive neural networks are used to retain the long-term dependence features of PWC. Finally, an inference layer is used for classification through extracted features. Classification experiments of cardiovascular diseases are performed on photoplethysmography dataset and continuous non-invasive blood pressure dataset. Results show that the classification accuracy of the multi-scale feature-extraction model on the two datasets respectively can reach 80% and 96%, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Setting Of Model Parametersmentioning

confidence: 99%

Few-shot pulse wave contour classification based on multi-scale feature extraction

Liu

Mao

et al. 2021

Sci Rep

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“…This can be accomplished by using advanced LSTM and recurrent neural networks (RNN). An example of this approach can be found in Shrimanti et al [99] where ECG, peak blood oxygenation signal (PPG), and accelerometer measurements were combined using a LSTM and RNN to compute in real-time motion compensated blood pressure. Such technology could open the door to real-time patient-specific anomaly detection that goes far beyond simple ECG measurements, for example correlating cardiac and respiratory events with patient activities.…”

Section: Future Workmentioning

confidence: 99%

A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)

Boulanger

2020

Sensors

Self Cite

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Cardiovascular diseases (CVDs) are the number one cause of death globally. An estimated 17.9 million people die from CVDs each year, representing 31% of all global deaths. Most cardiac patients require early detection and treatment. Therefore, many products to monitor patient’s heart conditions have been introduced on the market. Most of these devices can record a patient’s bio-metric signals both in resting and in exercising situations. However, reading the massive amount of raw electrocardiogram (ECG) signals from the sensors is very time-consuming. Automatic anomaly detection for the ECG signals could act as an assistant for doctors to diagnose a cardiac condition. This paper reviews the current state-of-the-art of this technology discusses the pros and cons of the devices and algorithms found in the literature and the possible research directions to develop the next generation of ambulatory monitoring systems.

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“…However, there are limitations to ABPM since it is usually expensive and uncomfortable. For that reason, researchers have been trying to diagnose hypertension using other sensors such as the photoplethysmography (PPG) and the electrocardiography (ECG), which can be easily integrated into wristbands, smartwatches, chestbands and arm-bands [5,6].…”

Section: Blood Pressure Estimationmentioning

confidence: 99%

“…Previous works show that machine learning algorithms perform well in predicting BP from features derived from PPG and ECG [6,8]. We have employed in our experiment two popular machine learning algorithms: Generalised Linear Models (GLM) with Elastic Net [18] regularisation and Gradient Boosting Machines (GBM) [4] to predict the systolic and diastolic blood pressure (see Section 4.2).…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions

Sasso

Datta

Pfitzner

et al. 2019

Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters

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Hypertension is one of the most prevalent chronic diseases worldwide. Early diagnosis of this condition can prevent the incidence of stroke and also, cardiovascular diseases (CVDs) such as myocardial infarction and heart failure. Lifestyle interventions, such as intermittent fasting (IF), aim to lower blood pressure (BP) levels and increase the health of patients with cardiometabolic conditions. However, for monitoring BP, we still rely on a cuff that slows the flow of blood, which is both uncomfortable and makes continuous monitoring implausible. Recent research has shown that BP can be estimated using comfortable sensors such as the photoplethysmography (PPG) and the electrocardiography (ECG). Features that can be used for the estimation of BP are systolic upstroke time (SUT) and diastolic time (DT) extracted from the PPG signal, and pulse arrival and transit time (PAT/PTT) derived from the combination of ECG and PPG signals. In this paper we present: (1) a study design to collect continuous physiological signals, before and after a 7-days intermittent fasting (IF) intervention from both cardiometabolic and non-hypertensive patients using wearable devices and (2) initial results for predicting continuous blood pressure from the PPG and ECG signals using statistical and machine learning methods. CCS CONCEPTS • Applied computing → Health informatics; • Computing methodologies → Machine learning; • Hardware → Sensor devices and platforms.

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Using Accelerometric and Gyroscopic Data to Improve Blood Pressure Prediction from Pulse Transit Time Using Recurrent Neural Network

Cited by 8 publications

References 15 publications

Few-shot pulse wave contour classification based on multi-scale feature extraction

Few-shot pulse wave contour classification based on multi-scale feature extraction

A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)

Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions

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