Non-Invasive Device for Blood Pressure Wave Acquisition by Means of Mechanical Transducer

Zambrana-Vinaroz, David; Vicente-Samper, José María; Juan, Carlos G.; Sala, V.; Navarro, José María Sabater

doi:10.3390/s19194311

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…The HRV parameter is obtained directly from the ECG signal by calculating the time between the R peaks. However, to calculate the PTT parameter, it is necessary to measure the time between the R peak of the ECG signal and the next peak of the PPG signal as indicated in [ 29 , 31 ]. Figure 4 shows the variation in milliseconds of the HRV and PTT values of a volunteer throughout the experimental procedure (40 min).…”

Section: Methodsmentioning

confidence: 99%

Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals

Zambrana-Vinaroz

Vicente-Samper

Manrique-Córdoba

et al. 2022

Sensors

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Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients’ health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.

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Section: Methodsmentioning

confidence: 99%

Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals

Zambrana-Vinaroz

Vicente-Samper

Manrique-Córdoba

et al. 2022

Sensors

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“…The relationship between PTT and cardiovascular system status has been investigated by many researchers [ 34 ]. PTT changes regularly with changes in vascular wall elasticity, peripheral resistance, and blood viscosity [ 35 ]. Therefore, PTT-based early prevention and diagnosis of CVD has received considerable attention due to its noninvasive and comfortable measurement form [ 36 , 37 ].…”

Section: Methodsmentioning

confidence: 99%

A Flexible Pressure Sensor with a Mesh Structure Formed by Lost Hair for Human Epidermal Pulse Wave Monitoring

Wang

Feng

Wang

et al. 2022

Sensors

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Flexible pressure sensors with the capability of monitoring human vital signs show broad application prospects in personalized healthcare. In this work, a hair-based flexible pressure sensor (HBPS) consisting of lost hair and polymer films was proposed for the continuous monitoring of the human epidermal arterial pulse waveform. A macroscale mesh structure formed by lost hair provides a simplified spacer that endows the triboelectric-based flexible pressure sensor with sufficient contact–separation space. Based on this mesh structure design, the hair-based flexible pressure sensor can respond to the slight pressure change caused by an object with 5 mg weight and hold a stable output voltage under 1–30 Hz external pressure excitation. Additionally, the hair-based flexible pressure sensor showed great sensitivity (0.9 V/kPa) and decent stability after 4500 cycles of operation. Given these compelling features, the HBPS can successfully measure the human epidermal arterial pulses with obvious details at different arteries. The proposed HBPS can also be used to monitor the pulse signals of different subjects. Furthermore, the three different pulse wave transmission time (PTT) values (PTT-foot, PTT-middle, and PTT-peak) can be obtained by simultaneously monitoring human pulse and electrocardiogram signals, which has enormous application potential for assessing cardiovascular system health.

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“…[11] By developing an advanced system for the detection of pathologies in the bronchopulmonary system, it certainly increases the possibility of detecting any pathology on patients early, likewise, increases the chances that patients receive early treatment and avoid any complications [12], in the same way, it will allow a prudent number of asthmatic patients to perform an early detection about the pathologies [13]. Now, the significance of being non-invasive is that it does not cause discomfort to the patient [14], so it would avoid any manipulation on the patient's body.…”

Section: Introductionmentioning

confidence: 99%

Automatic System for Detecting Pathologies in the Respiratory System for the Care of Patients with Bronchial Asthma Visualized by Computerized Radiography

Meneses-Claudio

Perez-Siguas²,

Matta-Solís³

et al. 2023

IJRITCC

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COVID-19 is a disease that directly affects the respiratory tract, being harmful in asthmatic patients because this condition causes a lack of oxygen, even to the extent that it requires external equipment to combat drowning. Likewise, it highlights the importance of maintaining a treatment or therapy correctly to prevent your disease from worsening or being exposed to other diseases, putting your health at risk. The diseases that asthmatic patients can acquire can result from pathologies, which have been growing over the years due to lack of equipment or efficient examinations that generate complete information about respiratory conditions about asthmatic patients, therefore, by developing an advanced system, the chances of detecting pathologies prematurely increase considerably, which is an essential tool today. According to the problem exposed, in this research an automatic system of detection of pathologies in the respiratory system was carried out for the care of the patient with bronchial asthma visualized by computerized radiography, so that any pathology can be detected by means of a premature diagnosis in the respiratory system and the doctor can perform a correct treatment on the asthmatic patient. Through the tests carried out by the system, its performance was accurate and efficient, being suitable to be implemented in various hospitals so that the doctor can treat the disease in time, since the system presented a 98.79% efficiency in the detection of pathologies.

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Non-Invasive Device for Blood Pressure Wave Acquisition by Means of Mechanical Transducer

Cited by 12 publications

References 21 publications

Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals

Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals

A Flexible Pressure Sensor with a Mesh Structure Formed by Lost Hair for Human Epidermal Pulse Wave Monitoring

Automatic System for Detecting Pathologies in the Respiratory System for the Care of Patients with Bronchial Asthma Visualized by Computerized Radiography

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