New insight of the implication blood pressure detection differences using aneroid sphygmomanometer and digital blood pressure on medical examination

Novitasari, Novitasari; Pratiwi, Nurdina Gita; Hidayat, Syarief; Ardiatna, Wuwus; Hidayat, Asep Rahmat; Supono, Ihsan

doi:10.1088/1742-6596/1528/1/012057

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…HT is often referred to as the "silent killer" because it typically does not show symptoms until it reaches an advanced stage, making it particularly challenging to detect [5]. Currently, the standard method for diagnosing and monitoring HT involves using a sphygmomanometer [6], which classifies patients into four stages of HT: normal, prehypertension (PHT), stage 1 HT, and stage 2 HT [7]. These stages are determined by systolic and diastolic blood pressure.…”

Section: Introductionmentioning

confidence: 99%

Advancing Non-Cuff Hypertension Detection: Leveraging 1D Convolutional Neural Network and Time Domain Physiological Signals

Nuryani,

Utomo,

Prabowo

et al. 2024

Int. J. Onl. Eng.

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Timely identification of hypertension (HT) is crucial for effectively managing and reducing the potential health consequences, including cardiovascular events such as heart attacks and strokes, as well as the development of kidney disease. Traditional cuff-based devices often discourage regular monitoring because they cause discomfort. Furthermore, the lack of symptoms in HT complicates the early detection of this condition. To address these challenges, our study employs a non-cuff methodology that utilizes unprocessed electrocardiogram (ECG) and photoplethysmogram (PPG) signals. We utilize a customized approach to enhance the features of a one-dimensional convolutional neural network (CNN) specifically tailored to optimize timeseries data. In contrast to previous research, our methodology avoids the need for complex signal extraction or transformation techniques. The main goal is to identify the optimal input signals and fine-tune the critical hyperparameters of CNNs. The clinical data underwent analysis, which revealed that the use of an integrated ECG and PPG approach resulted in the highest level of accuracy for detection. Notably, the F1 score achieved an impressive value of 98.88%. When evaluated separately, ECG outperformed PPG. Our study contributes to the advancement of the field by introducing a new approach that combines comfort and high accuracy in the early detection of HT. This method is practical and ensures a patient-friendly experience.

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Section: Introductionmentioning

confidence: 99%

Advancing Non-Cuff Hypertension Detection: Leveraging 1D Convolutional Neural Network and Time Domain Physiological Signals

Nuryani,

Utomo,

Prabowo

et al. 2024

Int. J. Onl. Eng.

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“…In addition, there are differences in measurement results from automatic and non-automatic measurement tools [19]. Therefore, it is important for health workers to be informed about the accuracy of both types of devices [20] [21]. Furthermore, the result of the study from 213 patients who had age range 23-93 years on the use of Non-Invasive Blood Pressure (NIBP), there was a momentary change in the SPO2 value of ±17% [22].…”

Section: Introductionmentioning

confidence: 99%

Quality Analysis of Pressure Measurement Automatic Sphygmomanometer and Non-automatic Sphygmomanometer

Pertiwi¹,

Hadziqoh²,

Mulyadi³

et al. 2022

Eksakta

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Hypertension is the fifth leading cause of death in Indonesia. Hypertension occurs because the blood vessels are continuously experiencing high pressure. Medical device that supports doctors and other health workers in diagnosing hypertension is sphygmomanometer. Sphygmomanometer is an instrument for measuring blood pressure. Accuracy of blood pressure measurement is very important in diagnosing hypertension. An Error in measuring blood pressure will be fatal for patients, health workers, and health facilities. So that this study aims to analyze the quality of automatic and non-automatic sphygmomanometer. This research method is in form of quantitative research based on experiments. The data was taken by using direct measurements. Measurements were carried out by using the 2018 Ministry of Health work method. Quality assessment of the sphygmomanometer was taken based on the results of sphygmomanometer calibration analysis. Automatic sphygmomanometer with the OMRON brand got score 95 and the non-automatic sphygmomanometer with the ABN brand got score 97.3. The results of the analysis showed that both tools had high accuracy of pressure measurement and were within tolerance limits. So it can be concluded that both tools are feasible to use.

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The Design of Korotkoff Sound Detection Using Amplitude Parameter and Oscillation Beat to Estimate Non-invasive Blood Pressure

Pratiwi,

Setiawan,

Naufal

et al. 2023

2023 International Seminar on Intelligent Technology and Its Applications (ISITIA)

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New insight of the implication blood pressure detection differences using aneroid sphygmomanometer and digital blood pressure on medical examination

Cited by 4 publications

References 16 publications

Advancing Non-Cuff Hypertension Detection: Leveraging 1D Convolutional Neural Network and Time Domain Physiological Signals

Advancing Non-Cuff Hypertension Detection: Leveraging 1D Convolutional Neural Network and Time Domain Physiological Signals

Quality Analysis of Pressure Measurement Automatic Sphygmomanometer and Non-automatic Sphygmomanometer

The Design of Korotkoff Sound Detection Using Amplitude Parameter and Oscillation Beat to Estimate Non-invasive Blood Pressure

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