Hyp-Net: Automated detection of hypertension using deep convolutional neural network and Gabor transform techniques with ballistocardiogram signals

Gupta, Kapil; Bajaj, Varun; Ansari, Irshad Ahmad; Acharya, U. Rajendra

doi:10.1016/j.bbe.2022.06.001

Cited by 23 publications

(9 citation statements)

References 52 publications

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“… [19] proposed a novel DLM for the screening of bundle branch blocks using vectorcardiogram signals. In [20] , authors proposed a new deep learning framework namely Hyp-Net for the automated detection of hypertension using time-frequency images of 1-D ballistocardiogram signals. Sometimes, for the better classification of medical images, authors compute the features like moments from the images.…”

Section: Previous Workmentioning

confidence: 99%

Deep learning models-based CT-scan image classification for automated screening of COVID-19

Gupta

Bajaj²

2023

Biomedical Signal Processing and Control

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Section: Previous Workmentioning

confidence: 99%

Deep learning models-based CT-scan image classification for automated screening of COVID-19

Gupta

Bajaj²

2023

Biomedical Signal Processing and Control

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“…Ballistocardiography, as the measurement of the body's micromovements due to blood ejected from the heart and moved in the large vessels, is a promising alternative for pulse rate and heart rate variability (HRV) assessment (12)(13)(14)(15)(16)(17)(18)(19)(20). Current technologies such as electrocardiography (ECG) or echocardiography proved in the past to be more reliable in clinical practice because of better reproducibility and fewer interindividual differences.…”

Section: Heart Rate Estimation From Bcgmentioning

confidence: 99%

“…Based on these premises, ballistocardiography (BCG) is a viable, cost-effective approach that allows the measurement of body movements and small vibrations coming from various sources, including respiration and the contractions of the heart (12). Thanks to both recent advancements in terms of vibration sensing technology and the unobtrusiveness of the sensing devices, a remote patient monitoring system (RPMS) making use of BCG signals can be deployed in different kinds of environments and seamlessly collect biomedical parameters, without burdening patients and clinical personnel.…”

Section: Introductionmentioning

confidence: 99%

“…Thanks to both recent advancements in terms of vibration sensing technology and the unobtrusiveness of the sensing devices, a remote patient monitoring system (RPMS) making use of BCG signals can be deployed in different kinds of environments and seamlessly collect biomedical parameters, without burdening patients and clinical personnel. As the interest in the impact of RPMSs on current eHealth dynamics is rapidly increasing ( 13 ), the principle according to which well-gathered data can become well- and easily-processed data, once working in real/realistic remote monitoring contexts, likely demands seeking solutions relying on different paradigms to be proved.…”

Section: Introductionmentioning

confidence: 99%

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Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Tramontano,

Tamburis,

Cioce

et al. 2023

Front. Digit. Health

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Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Paving a path of technological innovation in the clinical field, a very active line of research is currently dealing with the possibility to rely on non-medical-graded low-cost devices, to develop unattended telemedicine (TM) solutions aimed at non-invasively gathering data, signals, and images. In this article, a TM solution is proposed for monitoring the heart rate (HR) of patients during sleep. A remote patient monitoring system (RPMS) featuring a smart belt equipped with pressure sensors for ballistocardiogram (BCG) signals sampling was deployed. A field trial was then conducted over a 2-month period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a photoplethysmography (PPG) signal as the gold standard, to examine the feasibility of the solution via the estimation of HR values from the collected BCG signals. For this purpose, two of the highest-performing approaches for HR estimation from BCG signals, one algorithmic and the other based on a convolutional neural network (CNN), were retrieved from the literature and updated for a TM-related use case. Finally, HR estimation performances were assessed in terms of patient-wise mean absolute error (MAE). Results retrieved from the literature (controlled environment) outperformed those achieved in the experimentation (TM environment) by 29% (MAE = 4.24 vs. 5.46, algorithmic approach) and 52% (MAE = 2.32 vs. 3.54, CNN-based approach), respectively. Nonetheless, a low packet loss ratio, restrained elaboration time of the collected biomedical big data, low-cost deployment, and positive feedback from the users, demonstrate the robustness, reliability, and applicability of the proposed TM solution. In light of this, further steps will be planned to fulfill new targets, such as evaluation of respiratory rate (RR), and pattern assessment of the movement of the participants overnight.

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“…In order to tackle the challenges of feature extraction, researchers have successfully applied deep learning algorithms, showing remarkable performance in feature representation of images and natural language, to analyze various physiological signals, including electrocardiogram (ECG) 34 , ballistocardiogram (BCG) 35 , vectorcardiography (VCG) 36 , electroencephalograph (EEG) 37 , electromyography (EMG) 38 , among others. And these algorithms have yielded promising results in characterizing the nocturnal sleep respiratory audio signal subsequently.…”

Section: Introductionmentioning

confidence: 99%

Automatic snoring detection using a hybrid 1D–2D convolutional neural network

Li,

Yue

et al. 2023

Sci Rep

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Snoring, as a prevalent symptom, seriously interferes with life quality of patients with sleep disordered breathing only (simple snorers), patients with obstructive sleep apnea (OSA) and their bed partners. Researches have shown that snoring could be used for screening and diagnosis of OSA. Therefore, accurate detection of snoring sounds from sleep respiratory audio at night has been one of the most important parts. Considered that the snoring is somewhat dangerously overlooked around the world, an automatic and high-precision snoring detection algorithm is required. In this work, we designed a non-contact data acquire equipment to record nocturnal sleep respiratory audio of subjects in their private bedrooms, and proposed a hybrid convolutional neural network (CNN) model for the automatic snore detection. This model consists of a one-dimensional (1D) CNN processing the original signal and a two-dimensional (2D) CNN representing images mapped by the visibility graph method. In our experiment, our algorithm achieves an average classification accuracy of 89.3%, an average sensitivity of 89.7%, an average specificity of 88.5%, and an average AUC of 0.947, which surpasses some state-of-the-art models trained on our data. In conclusion, our results indicate that the proposed method in this study could be effective and significance for massive screening of OSA patients in daily life. And our work provides an alternative framework for time series analysis.

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Hyp-Net: Automated detection of hypertension using deep convolutional neural network and Gabor transform techniques with ballistocardiogram signals

Cited by 23 publications

References 52 publications

Deep learning models-based CT-scan image classification for automated screening of COVID-19

Deep learning models-based CT-scan image classification for automated screening of COVID-19

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Automatic snoring detection using a hybrid 1D–2D convolutional neural network

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