Heart rate estimation in PPG signals using Convolutional-Recurrent Regressor

Ismail, Shahid; Siddiqi, Imran; Akram, Usman

doi:10.1016/j.compbiomed.2022.105470

Cited by 17 publications

(6 citation statements)

References 28 publications

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“…Gated recurrent units (GRU) and long short-term memory (LSTM) are improved versions of RNN, and they provide state-of-the-art performance in many applications, including machine translation, speech recognition, and image captioning Abduh et al (2019) . Heart sound signals are sequential data with strong temporal correlation, so heart sound classification can be efficiently processed by RNN Nogueira et al (2019) ; Ismail et al (2022) ; Sakib et al (2019) . Figure 2 describes the Waveform representation of S1, S2, S3, and S4 sounds in systole and diastole intervals.…”

Section: Introductionmentioning

confidence: 99%

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Zhang

Wang

et al. 2022

Front. Physiol.

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Heart sound classification plays a critical role in the early diagnosis of cardiovascular diseases. Although there have been many advances in heart sound classification in the last few years, most of them are still based on conventional segmented features and shallow structure-based classifiers. Therefore, we propose a new heart sound classification method based on improved mel-frequency cepstrum coefficient features and deep residual learning. Firstly, the heart sound signal is preprocessed, and its improved features are computed. Then, these features are used as input features of the neural network. The pathological information in the heart sound signal is further extracted by the deep residual network. Finally, the heart sound signal is classified into different categories according to the features learned by the neural network. This paper presents comprehensive analyses of different network parameters and network connection strategies. The proposed method achieves an accuracy of 94.43% on the dataset in this paper.

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Section: Introductionmentioning

confidence: 99%

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Zhang

Wang

et al. 2022

Front. Physiol.

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“…In many instances, nonlinear models have demonstrated superior performance compared to linear models, although this outcome is contingent on the specific dataset and approach utilized, i.e., PTT, Pulse Arrival Time (PAT), or PWA using only PPG data. Additionally, more advanced methods, such as Recurrent Neural Networks (RNN) [ 32 ] and Long Short-Term Memory (LSTM) networks [ 33 ], have also been proposed. Although these models may offer a significant advantage over previously mentioned models by incorporating the ability to capture variations in extracted features over time, Deep Learning (DL) models require a large number of data samples to provide reasonably accurate BP values.…”

Section: Introductionmentioning

confidence: 99%

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

Valerio,

Demarchi,

O’Flynn

et al. 2024

Sensors

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Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject’s pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

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“…The blood volume in the blood vessel presents pulsating changes under the action of the heart beat. Thus, the PPG signal contains a considerable amount of cardiovascular physiological information, and it has been adopted to evaluate a wide variety of physiological parameters (e.g., blood oxygen, heart rate, respiratory rate, and blood glucose) [ 9 , 10 , 11 , 12 ]. Furthermore, PPG has been studied in the field of continuous blood pressure measurement [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet

Qin

Liu

et al. 2023

Bioengineering

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Cardiovascular disease (CVD) has become a common health problem of mankind, and the prevalence and mortality of CVD are rising on a year-to-year basis. Blood pressure (BP) is an important physiological parameter of the human body and also an important physiological indicator for the prevention and treatment of CVD. Existing intermittent measurement methods do not fully indicate the real BP status of the human body and cannot get rid of the restraining feeling of a cuff. Accordingly, this study proposed a deep learning network based on the ResNet34 framework for continuous prediction of BP using only the promising PPG signal. The high-quality PPG signals were first passed through a multi-scale feature extraction module after a series of pre-processing to expand the perceptive field and enhance the perception ability on features. Subsequently, useful feature information was then extracted by stacking multiple residual modules with channel attention to increase the accuracy of the model. Lastly, in the training stage, the Huber loss function was adopted to stabilize the iterative process and obtain the optimal solution of the model. On a subset of the MIMIC dataset, the errors of both SBP and DBP predicted by the model met the AAMI standards, while the accuracy of DBP reached Grade A of the BHS standard, and the accuracy of SBP almost reached Grade A of the BHS standard. The proposed method verifies the potential and feasibility of PPG signals combined with deep neural networks in the field of continuous BP monitoring. Furthermore, the method is easy to deploy in portable devices, and it is more consistent with the future trend of wearable blood-pressure-monitoring devices (e.g., smartphones and smartwatches).

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Heart rate estimation in PPG signals using Convolutional-Recurrent Regressor

Cited by 17 publications

References 28 publications

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet

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