Deep learning algorithm evaluation of hypertension classification in less photoplethysmography signals conditions

Yen, Chih-Ta; Chang, Sheng‐Nan; Liao, Chenghong

doi:10.1177/00202940211001904

Cited by 20 publications

(8 citation statements)

References 16 publications

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“…The proposed technique is also compared with some recent work, as presented in Table 6 . In Table 6 , it is clearly indicated that our proposed technique is better in terms of accuracy compared with [ 40 ]. However, the accuracy obtained in [ 41 ] is equal to the proposed one, and hence we added another data set and applied deep learning in the proposed work to gain more attraction.…”

Section: Resultsmentioning

confidence: 96%

Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing

Sadad

Bukhari

Munir

et al. 2022

Computational Intelligence and Neuroscience

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Hypertension is the main cause of blood pressure (BP), which further causes various cardiovascular diseases (CVDs). The recent COVID-19 pandemic raised the burden on the healthcare system and also limits the resources to these patients only. The treatment of chronic patients, especially those who suffer from CVD, has fallen behind, resulting in increased deaths from CVD around the world. Regular monitoring of BP is crucial to prevent CVDs as it can be controlled and diagnosed through constant monitoring. To find an effective and convenient procedure for the early diagnosis of CVDs, photoplethysmography (PPG) is recognized as a low-cost technology. Through PPG technology, various cardiovascular parameters, including blood pressure, heart rate, blood oxygen saturation, etc., are detected. Merging the healthcare domain with information technology (IT) is a demanding area to reduce the rehospitalization of CVD patients. In the proposed model, PPG signals from the Internet of things (IoT)-enabled wearable patient monitoring (WPM) devices are used to monitor the heart rate (HR), etc., of the patients remotely. This article investigates various machine learning techniques such as decision tree (DT), naïve Bayes (NB), and support vector machine (SVM) and the deep learning model one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) to develop a system that assists physicians during continuous monitoring, which achieved an accuracy of 99.5% using PPG-BP data set. The proposed system provides cost-effective, efficient, and fully connected monitoring systems for cardiac patients.

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Section: Resultsmentioning

confidence: 96%

Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing

Sadad

Bukhari

Munir

et al. 2022

Computational Intelligence and Neuroscience

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“…There are three indices of the results: the standard deviation (SD) of the short term of PPT or SD1 in (3), the SD of the long term of PPT or SD2 in (4), and SSR in (5).…”

Section: Poincaré Plot Analysismentioning

confidence: 99%

Poincaré plots to analyze photoplethysmography signal between non-smokers and smokers

Haryadi

Chang

Akrom

et al. 2022

IJECE

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<span>An analysis of blood circulation was used to identify variations of heart rate and to create an early warning system of autonomic dysfunction. The Poincaré plot analyzed blood circulation using photoplethysmography (PPG) signals between non-smokers and smokers in three different indices: SD1, SD2, and SD1 SD2 ratio (SSR). There were twenty subjects separated into non-smoker and smoker groups with sample sizes of 10, respectively. An independent sample t-test to compare the continuous variables. Whereas, the comparison between two groups employed Fisher’s exact test for categorical variables. The result showed that SD1 was found to be considerably lower in the group of smokers (0.03±0.01) than that of the non-smokers (0.06±0.03). Similarly, SSR was recorded at 0.0012±0.0005 and 0.0023±0.0012 for smoking and non-smoking subjects, respectively. As a comparison, SD2 for non-smokers (25.7±0.5) was lower than smokers (27.3±0.4). In conclusion, we revealed that the parameters of Poincaré plots (SD1, SD2, and SSR) exert good performances to significantly differentiate the PPG signals of the group of non-smokers from those of smokers. We also supposed that the method promises to be a suitable method to distinguish the cardiovascular disease group. Therefore, this method can be applied as a part of early detection system of cardiovascular diseases.</span>

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“…The best performing algorithm had a 92.5% accuracy. Yen et al [14] classified hypertension stages, based on PPG signals, using a Deep Residual Network, Convolutional Neural Network, and Bidirectional Long Short-Term Memory model. An accuracy of 76% was achieved in the testing data.…”

Section: Related Workmentioning

confidence: 99%

The Application of Deep Learning Algorithms for PPG Signal Processing and Classification

Esgalhado

Fernandes²,

Vassilenko

et al. 2021

Computers

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Photoplethysmography (PPG) is widely used in wearable devices due to its conveniency and cost-effective nature. From this signal, several biomarkers can be collected, such as heart and respiration rate. For the usual acquisition scenarios, PPG is an artefact-ridden signal, which mandates the need for the designated classification algorithms to be able to reduce the noise component effect on the classification. Within the selected classification algorithm, the hyperparameters’ adjustment is of utmost importance. This study aimed to develop a deep learning model for robust PPG wave detection, which includes finding each beat’s temporal limits, from which the peak can be determined. A study database consisting of 1100 records was created from experimental PPG measurements performed in 47 participants. Different deep learning models were implemented to classify the PPG: Long Short-Term Memory (LSTM), Bidirectional LSTM, and Convolutional Neural Network (CNN). The Bidirectional LSTM and the CNN-LSTM were investigated, using the PPG Synchrosqueezed Fourier Transform (SSFT) as the models’ input. Accuracy, precision, recall, and F1-score were evaluated for all models. The CNN-LSTM algorithm, with an SSFT input, was the best performing model with accuracy, precision, and recall of 0.894, 0.923, and 0.914, respectively. This model has shown to be competent in PPG detection and delineation tasks, under noise-corrupted signals, which justifies the use of this innovative approach.

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Deep learning algorithm evaluation of hypertension classification in less photoplethysmography signals conditions

Cited by 20 publications

References 16 publications

Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing

Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing

Poincaré plots to analyze photoplethysmography signal between non-smokers and smokers

The Application of Deep Learning Algorithms for PPG Signal Processing and Classification

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